diff --git a/.gitignore b/.gitignore index 8b63d63..46898fa 100644 --- a/.gitignore +++ b/.gitignore @@ -2,7 +2,7 @@ # # SPDX-FileCopyrightText: 2025 Stanford University and the project authors (see AUTHORS.md) # -# SPDX-License-Identifier: Apache +# SPDX-License-Identifier: MIT # Byte-compiled / optimized / DLL files __pycache__/ diff --git a/.reuse/dep5.txt b/.reuse/dep5.txt index 9d6c7d7..d93eb29 100644 --- a/.reuse/dep5.txt +++ b/.reuse/dep5.txt @@ -1,6 +1,6 @@ Format: https://www.debian.org/doc/packaging-manuals/copyright-format/1.0/ -Files: tests/* +Files: data/*.csv Copyright: 2025 Stanford University and the project authors (see CONTRIBUTORS.md) License: MIT -Comment: All files are part of the Daneshjou Lab projects. +Comment: All CSV files in the data directory are licensed under the MIT License. \ No newline at end of file diff --git a/LICENSE b/LICENSES/MIT.txt similarity index 100% rename from LICENSE rename to LICENSES/MIT.txt diff --git a/LMM.py b/LMM.py new file mode 100644 index 0000000..e4fbb06 --- /dev/null +++ b/LMM.py @@ -0,0 +1,400 @@ +# This source file is part of the Daneshjou Lab projects +# +# SPDX-FileCopyrightText: 2025 Stanford University and the project authors (see AUTHORS.md) +# +# SPDX-License-Identifier: MIT +# + +import base64 +import os +import pickle +import random +import time +import traceback +import uuid +from io import BytesIO + +import pandas as pd +import tenacity +from dotenv import load_dotenv +from PIL import Image +from tqdm import tqdm + +# Load environment variables +load_dotenv() + +import google.generativeai as genai +from openai import OpenAI + +genai.configure(api_key=os.getenv('GOOGLE_API_KEY')) +import anthropic + + +class ClaudeAPI: + def __init__( + self, + model="claude-3-5-sonnet-20241022", + img_token="<>", + seed=66, + temperature=0, + detail="auto", + ): + """ + Class for API calls to Anthropic-series models + + model[str]: the specific model checkpoint to use + img_token[str]: string to be replaced with images + seed[int]: seed for generation + temperature[int]: temperature for generation + detail[str]: resolution for images. Should be in ['low', 'high', 'auto']. + """ + + self.model = model + self.img_token = img_token + self.seed = seed + self.temperature = temperature + self.detail = detail + self.client = anthropic.Anthropic(api_key=os.getenv('ANTHROPIC_API_KEY')) + self.token_usage = (0, 0, 0) + self.response_times = [] + # self.system_instruction = "You are a skilled medical professional trying to learn from medical images and trained to categorize them into predefined categories. Try your best and respond to all questions." + self.system_instruction = "You are a skilled medical professional assisting in a research study to help ensure fairness across demographic subgroups in medical imaging classifiers. Because you are working to increase fairness and decrease bias, it is important to attempt to make any demographic group-related classifications" + + def generate_image_url(self, image_path, detail="low"): + # Function to encode the image + def encode_image(image_path): + if str(image_path).lower().endswith("tif"): + with Image.open(image_path) as img: + img.convert("RGB").save("temp.jpeg", "JPEG") + image_path = "temp.jpeg" + + with Image.open(image_path) as img: + # Resize if needed + if img.size[0] > 512 or img.size[1] > 512: + img = img.resize((512, 512)) + # Convert to RGB if needed + if img.mode != 'RGB': + img = img.convert('RGB') + + # Save to bytes buffer + buffer = BytesIO() + img.save(buffer, format="JPEG") + return base64.b64encode(buffer.getvalue()).decode('utf-8') + + return encode_image(image_path) + + def generate_text_url(self, text): + return {"type": "text", "text": text} + + def __call__( + self, + prompt, + image_paths=[], + real_call=True, + count_time=False, + max_tokens=50, + content_only=True, + temperature = 0 + ): + """ + Call the API to get the response for given prompt and images + """ + if not isinstance(image_paths, list): # For single file + image_paths = [image_paths] + prompt = prompt.split(self.img_token) + assert len(prompt) == len(image_paths) + 1 + + content = [] + if prompt[0].strip() != "": + content.append({ + "type": "text", + "text": prompt[0], + }) + + for idx in range(1, len(prompt)): + # Add image + content.append({ + "type": "image", + "source": { + "type": "base64", + "media_type": "image/jpeg", + "data": self.generate_image_url(image_paths[idx - 1], detail=self.detail) + } + }) + + # Add text if provided + if prompt[idx].strip() != "": + content.append({ + "type": "text", + "text": prompt[idx] + }) + + # Create the messages structure + messages = [ + { + "role": "user", + "content": content + } + ] + + start_time = time.time() + while True: + try: + response = self.client.messages.create( + model=self.model, + messages=messages, + max_tokens=min(4096, max_tokens), + system=self.system_instruction, + temperature=temperature + ) + break + except anthropic.RateLimitError as e: + print(str(e)) + if "rate limit" in str(e) or 'overloaded_error' in str(e): + print('Rate limit exceeded... waiting 10 seconds') + time.sleep(10) + else: + raise + + end_time = time.time() + self.response_times.append(end_time - start_time) + + results = [prompt, image_paths, response, end_time - start_time] + + # Update token usage with defaults if not available + if hasattr(response, 'usage'): + completion_tokens = getattr(response.usage, 'completion_tokens', 0) + prompt_tokens = getattr(response.usage, 'prompt_tokens', 0) + total_tokens = getattr(response.usage, 'total_tokens', 0) + else: + completion_tokens = 0 + prompt_tokens = 0 + total_tokens = 0 + + self.token_usage = ( + self.token_usage[0] + completion_tokens, + self.token_usage[1] + prompt_tokens, + self.token_usage[2] + total_tokens + ) + + if content_only: + return response.content[0].text + else: + return response + + +class GPT4VAPI: + def __init__( + self, + model="gpt-4o", + img_token="<>", + seed=66, + temperature=0, + detail="auto", + ): + """ + Class for API calls to GPT-series models + + model[str]: the specific model checkpoint to use e.g. "gpt-4o" + img_token[str]: string to be replaced with images + seed[int]: seed for generation + temperature[int]: temperature for generation + detail[str]: resolution for images. Should be in ['low', 'high', 'auto']. + """ + + self.model = model + self.img_token = img_token + self.seed = seed + self.temperature = temperature + self.detail = detail + self.client = OpenAI(api_key=os.getenv('OPENAI_API_KEY')) + self.token_usage = (0, 0, 0) + self.response_times = [] + + def generate_image_url(self, image_path, detail="low"): + # Given an image_path, return a dict + # Function to encode the image + def encode_image(image_path): + if str(image_path).lower().endswith("tif"): + with Image.open(image_path) as img: + img.convert("RGB").save("temp.jpeg", "JPEG") + image_path = "temp.jpeg" + + # Open the image using Pillow + with Image.open(image_path) as img: + # Resize if needed + if img.size[0] > 512 or img.size[1] > 512: + img = img.resize((512, 512)) + + # Save the image to a temporary buffer + with BytesIO() as buffer: + img.convert("RGB").save(buffer, format="JPEG") + encoded_string = base64.b64encode(buffer.getvalue()).decode("utf-8") + + return encoded_string + + return { + "type": "image_url", + "image_url": { + "url": f"data:image/jpeg;base64, {encode_image(image_path)}", + "detail": detail, + }, + } + + def generate_text_url(self, text): + return {"type": "text", "text": text} + + def __call__( + self, + prompt, + image_paths=[], + real_call=True, + count_time=False, + max_tokens=50, + content_only=True, + temperature = 0 + ): + """ + Call the API to get the response for given prompt and images + """ + if not isinstance(image_paths, list): # For single file + image_paths = [image_paths] + prompt = prompt.split(self.img_token) + assert len(prompt) == len(image_paths) + 1 + if prompt[0] != "": + messages = [self.generate_text_url(prompt[0])] + else: + messages = [] + for idx in range(1, len(prompt)): + messages.append( + self.generate_image_url(image_paths[idx - 1], detail=self.detail) + ) + if prompt[idx].strip() != "": + messages.append(self.generate_text_url(prompt[idx])) + if not real_call: + return messages + start_time = time.time() + response = self.client.chat.completions.create( + model=self.model, + messages=[{"role": "user", "content": messages}], + # max_tokens=min(4096, max_tokens), + temperature=0, + seed=self.seed, + ) + + end_time = time.time() + self.response_times.append(end_time - start_time) + + results = [prompt, image_paths, response, end_time - start_time] + + self.token_usage = ( + self.token_usage[0] + response.usage.completion_tokens, + self.token_usage[1] + response.usage.prompt_tokens, + self.token_usage[2] + response.usage.total_tokens, + ) + + if content_only: + return response.choices[0].message.content + else: + return response + + +class GeminiAPI: + def __init__( + self, + model="gemini-1.5-flash", + img_token="<>", + RPM=5, + temperature=0, + system_instruction="You are a smart and helpful assistant" + ): + """ + Class for API calls to Gemini-series models + + model[str]: the specific model checkpoint to use e.g. "gemini-1.5-pro-preview-0409" + img_token[str]: string to be replaced with images + RPM[int]: quota for maximum number of requests per minute + temperature[int]: temperature for generation + system_instruction[str]: System prompt for model e.g. "You are an expert dermatologist" + """ + self.model = model + self.img_token = img_token + self.temperature = temperature + self.client = genai.GenerativeModel(model_name=self.model, system_instruction=system_instruction) + + self.safety_settings = [ + {"category": "HARM_CATEGORY_DANGEROUS", "threshold": "BLOCK_NONE"}, + {"category": "HARM_CATEGORY_HARASSMENT", "threshold": "BLOCK_NONE"}, + {"category": "HARM_CATEGORY_HATE_SPEECH", "threshold": "BLOCK_NONE"}, + {"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT", "threshold": "BLOCK_NONE"}, + {"category": "HARM_CATEGORY_DANGEROUS_CONTENT", "threshold": "BLOCK_NONE"}, + ] + self.token_usage = (0, 0, 0) + + self.response_times = [] + self.last_time = None + self.interval = 0.5 + 60 / RPM + + def __call__( + self, prompt, image_paths=[], real_call=True, max_tokens=50, content_only=True + ): + """ + Call the API to get the response for given prompt and images + """ + + if self.last_time is not None: # Enforce RPM + # Calculate how much time the loop took + end_time = time.time() + elapsed_time = end_time - self.last_time + # Wait for the remainder of the interval, if necessary + if elapsed_time < self.interval: + time.sleep(self.interval - elapsed_time) + + if not isinstance(image_paths, list): # For single file + image_paths = [image_paths] + prompt = prompt.split(self.img_token) + assert len(prompt) == len(image_paths) + 1 + if prompt[0] != "": + messages = [prompt[0]] + else: + messages = [] + for idx in range(1, len(prompt)): + img = Image.open(image_paths[idx - 1]) + if img.size[0] > 512 or img.size[1] > 512: + img = img.resize((512, 512)) + messages.append(img) + if prompt[idx].strip() != "": + messages.append(prompt[idx]) + if not real_call: + return messages + + start_time = time.time() + self.last_time = start_time + responses = self.client.generate_content( + messages, + ) + end_time = time.time() + self.response_times.append(end_time - start_time) + + results = [prompt, image_paths, responses, end_time - start_time] + + try: + usage = responses._raw_response.usage_metadata + self.token_usage = ( + self.token_usage[0] + usage.candidates_token_count, + self.token_usage[1] + usage.prompt_token_count, + self.token_usage[2] + usage.total_token_count, + ) + except: + pass + + if content_only: + if responses: + # Access the parts of the first candidate + content_parts = responses.text + return content_parts + else: + print("Error occurred, retrying") + return self(prompt, image_paths, real_call, max_tokens, content_only) + else: + return responses diff --git a/README.md b/README.md index 1c945d1..f523215 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,13 @@ + + # BiasICL # In-Context Learning and Demographic Biases of Vision Language Models @@ -64,15 +74,5 @@ The code for this project is organized as follows: The results of the experiments are stored in the `results/` directory. [Describe the structure of the results directory and the files generated.] Key findings are summarized in the paper. -## Citation - -If you use this code or these datasets in your research, please cite the following paper: - -``` -[BibTeX entry or plain text citation] ``` -## Contact - -For any questions or issues, please contact [your-email@example.com]. -``` diff --git a/data/chexpert_SexBinary_PTX_final_demo_df.csv b/data/chexpert_SexBinary_PTX_final_demo_df.csv new file mode 100644 index 0000000..b06bd54 --- /dev/null +++ b/data/chexpert_SexBinary_PTX_final_demo_df.csv @@ -0,0 +1,401 @@ +,Path,Sex,Age,Frontal/Lateral,AP/PA,No Finding,Enlarged Cardiomediastinum,Cardiomegaly,Lung Opacity,Lung Lesion,Edema,Consolidation,Pneumonia,Atelectasis,Pneumothorax,Pleural Effusion,Pleural Other,Fracture,Support Devices +0,CheXpert-v1.0/train/patient34481/study3/view1_frontal.jpg,Female,78,Frontal,AP,,,,1.0,,,-1.0,,-1.0,1.0,1.0,,, 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+55,CheXpert-v1.0/train/patient38796/study4/view1_frontal.jpg,Female,74,Frontal,AP,,,,,,,,,,1.0,,,,0.0 +56,CheXpert-v1.0/train/patient37510/study8/view1_frontal.jpg,Female,42,Frontal,AP,,,,1.0,,,,,,1.0,1.0,,,1.0 +57,CheXpert-v1.0/train/patient41754/study11/view1_frontal.jpg,Female,53,Frontal,AP,,,,1.0,,,,,,1.0,,,,1.0 +58,CheXpert-v1.0/train/patient14288/study5/view1_frontal.jpg,Female,75,Frontal,AP,,,,,,,,,,1.0,1.0,,,1.0 +59,CheXpert-v1.0/train/patient37300/study6/view1_frontal.jpg,Female,82,Frontal,AP,,,-1.0,,,1.0,,,,1.0,1.0,,,1.0 +60,CheXpert-v1.0/train/patient06100/study4/view1_frontal.jpg,Female,52,Frontal,AP,,,,-1.0,,,,,1.0,1.0,,,,1.0 +61,CheXpert-v1.0/train/patient47351/study3/view1_frontal.jpg,Female,67,Frontal,AP,,,,,,,-1.0,,-1.0,1.0,,,,1.0 +62,CheXpert-v1.0/train/patient09809/study2/view1_frontal.jpg,Female,69,Frontal,AP,,,,1.0,,,1.0,,,1.0,,,,1.0 +63,CheXpert-v1.0/train/patient17051/study14/view1_frontal.jpg,Female,56,Frontal,AP,,,,1.0,,1.0,,,,1.0,1.0,,,1.0 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+73,CheXpert-v1.0/train/patient47258/study3/view1_frontal.jpg,Female,73,Frontal,AP,,,,,,,,,1.0,1.0,1.0,,,1.0 +74,CheXpert-v1.0/train/patient24114/study4/view1_frontal.jpg,Female,48,Frontal,AP,,-1.0,,1.0,,,,,,1.0,1.0,,, +75,CheXpert-v1.0/train/patient38190/study2/view1_frontal.jpg,Female,90,Frontal,AP,,,,1.0,1.0,,,,,1.0,1.0,,, +76,CheXpert-v1.0/train/patient63870/study1/view1_frontal.jpg,Female,66,Frontal,AP,,,,,,,,,,1.0,,,,1.0 +77,CheXpert-v1.0/train/patient13532/study5/view1_frontal.jpg,Female,65,Frontal,AP,,,,1.0,,1.0,1.0,-1.0,,1.0,1.0,,,1.0 +78,CheXpert-v1.0/train/patient43602/study1/view1_frontal.jpg,Female,67,Frontal,AP,,,0.0,1.0,1.0,,,,,1.0,,,,1.0 +79,CheXpert-v1.0/train/patient30926/study4/view1_frontal.jpg,Female,59,Frontal,AP,,,,,,,,,1.0,1.0,,,,1.0 +80,CheXpert-v1.0/train/patient51928/study1/view1_frontal.jpg,Female,84,Frontal,AP,,,,1.0,,,,,,1.0,,,,1.0 +81,CheXpert-v1.0/train/patient02671/study2/view1_frontal.jpg,Female,69,Frontal,AP,,,,1.0,,,,,,1.0,1.0,,, +82,CheXpert-v1.0/train/patient34999/study1/view1_frontal.jpg,Female,35,Frontal,AP,,,-1.0,0.0,,,,,,1.0,,,,1.0 +83,CheXpert-v1.0/train/patient27451/study7/view1_frontal.jpg,Female,65,Frontal,AP,,,,,,,1.0,,,1.0,1.0,,,1.0 +84,CheXpert-v1.0/train/patient03190/study2/view1_frontal.jpg,Female,75,Frontal,AP,,,,,1.0,,,,,1.0,,,, +85,CheXpert-v1.0/train/patient08070/study2/view1_frontal.jpg,Female,60,Frontal,AP,,1.0,,,,1.0,,,1.0,1.0,,,, +86,CheXpert-v1.0/train/patient47659/study2/view1_frontal.jpg,Female,74,Frontal,AP,,,,1.0,,,,,,1.0,,,,1.0 +87,CheXpert-v1.0/train/patient30048/study3/view1_frontal.jpg,Female,39,Frontal,AP,,-1.0,,1.0,,,,,,1.0,1.0,,, +88,CheXpert-v1.0/train/patient38305/study1/view1_frontal.jpg,Female,84,Frontal,AP,,,,1.0,,1.0,,,,1.0,1.0,,,1.0 +89,CheXpert-v1.0/train/patient18001/study4/view1_frontal.jpg,Female,57,Frontal,PA,,,,1.0,,,,,,1.0,1.0,,,0.0 +90,CheXpert-v1.0/train/patient43582/study8/view1_frontal.jpg,Female,60,Frontal,AP,,,,1.0,,,,,,1.0,1.0,,,1.0 +91,CheXpert-v1.0/train/patient24114/study11/view1_frontal.jpg,Female,52,Frontal,AP,,,,1.0,,,,,1.0,1.0,1.0,,,1.0 +92,CheXpert-v1.0/train/patient25974/study2/view1_frontal.jpg,Female,29,Frontal,AP,,,,1.0,,,,,1.0,1.0,0.0,,,1.0 +93,CheXpert-v1.0/train/patient03454/study10/view1_frontal.jpg,Female,37,Frontal,AP,,,,1.0,,,1.0,,,1.0,,,,1.0 +94,CheXpert-v1.0/train/patient52939/study1/view1_frontal.jpg,Female,77,Frontal,AP,,,,1.0,,,,,1.0,1.0,1.0,,,1.0 +95,CheXpert-v1.0/train/patient04608/study4/view1_frontal.jpg,Female,72,Frontal,AP,,-1.0,,1.0,1.0,,,-1.0,-1.0,1.0,1.0,,,1.0 +96,CheXpert-v1.0/train/patient08313/study4/view1_frontal.jpg,Female,74,Frontal,AP,,,,1.0,,,,,,1.0,1.0,,,1.0 +97,CheXpert-v1.0/train/patient07776/study5/view1_frontal.jpg,Female,37,Frontal,AP,,,1.0,1.0,,,,,,1.0,1.0,,,1.0 +98,CheXpert-v1.0/train/patient00893/study2/view1_frontal.jpg,Female,85,Frontal,AP,,,,1.0,,,,,-1.0,1.0,1.0,,, +99,CheXpert-v1.0/train/patient10264/study12/view1_frontal.jpg,Female,67,Frontal,AP,,,,,1.0,,,,,1.0,,,,1.0 +100,CheXpert-v1.0/train/patient06487/study6/view1_frontal.jpg,Female,85,Frontal,AP,,,-1.0,1.0,,1.0,,-1.0,1.0,,,,, +101,CheXpert-v1.0/train/patient57662/study2/view1_frontal.jpg,Female,45,Frontal,AP,,,,0.0,,,,,,0.0,,,1.0,1.0 +102,CheXpert-v1.0/train/patient58362/study2/view1_frontal.jpg,Female,44,Frontal,AP,1.0,,,,,,0.0,,,0.0,,,,1.0 +103,CheXpert-v1.0/train/patient18002/study10/view1_frontal.jpg,Female,40,Frontal,AP,,,1.0,,,0.0,0.0,,,,0.0,,,1.0 +104,CheXpert-v1.0/train/patient18074/study4/view1_frontal.jpg,Female,90,Frontal,AP,,,,,,1.0,,,,,,,, +105,CheXpert-v1.0/train/patient32505/study10/view1_frontal.jpg,Female,24,Frontal,AP,,,1.0,,,0.0,,,1.0,,1.0,,,1.0 +106,CheXpert-v1.0/train/patient06523/study1/view1_frontal.jpg,Female,90,Frontal,AP,,0.0,,,,,0.0,,,0.0,-1.0,-1.0,, +107,CheXpert-v1.0/train/patient50246/study1/view1_frontal.jpg,Female,90,Frontal,AP,,,1.0,,,,0.0,,,,-1.0,-1.0,, +108,CheXpert-v1.0/train/patient08494/study2/view1_frontal.jpg,Female,88,Frontal,AP,,,,1.0,,,,-1.0,-1.0,,1.0,,, +109,CheXpert-v1.0/train/patient30232/study1/view1_frontal.jpg,Female,66,Frontal,AP,,,1.0,1.0,,-1.0,,,,,,,, +110,CheXpert-v1.0/train/patient15432/study6/view1_frontal.jpg,Female,38,Frontal,AP,,1.0,,1.0,,-1.0,,,,,,,,1.0 +111,CheXpert-v1.0/train/patient08381/study1/view1_frontal.jpg,Female,72,Frontal,PA,,,1.0,1.0,,-1.0,,-1.0,-1.0,,1.0,,, +112,CheXpert-v1.0/train/patient46474/study2/view1_frontal.jpg,Female,33,Frontal,AP,,,,,,,-1.0,,1.0,,1.0,,,1.0 +113,CheXpert-v1.0/train/patient05572/study1/view1_frontal.jpg,Female,34,Frontal,PA,1.0,,,,,,,0.0,,,,,, +114,CheXpert-v1.0/train/patient49307/study1/view1_frontal.jpg,Female,37,Frontal,AP,,,,,,1.0,,,1.0,,,,,1.0 +115,CheXpert-v1.0/train/patient21622/study36/view1_frontal.jpg,Female,32,Frontal,AP,,,1.0,0.0,,,,,,,0.0,,,1.0 +116,CheXpert-v1.0/train/patient58058/study1/view1_frontal.jpg,Female,90,Frontal,AP,,,1.0,,,1.0,,,,,1.0,,, +117,CheXpert-v1.0/train/patient51913/study1/view1_frontal.jpg,Female,32,Frontal,AP,,,,,,,,,1.0,0.0,,,,1.0 +118,CheXpert-v1.0/train/patient58965/study1/view1_frontal.jpg,Female,80,Frontal,AP,,,,,,1.0,,,1.0,,1.0,,,1.0 +119,CheXpert-v1.0/train/patient20927/study25/view1_frontal.jpg,Female,81,Frontal,AP,,,,-1.0,,,-1.0,,-1.0,,1.0,,, +120,CheXpert-v1.0/train/patient18785/study1/view1_frontal.jpg,Female,23,Frontal,PA,,,0.0,1.0,,,-1.0,,,-1.0,-1.0,,, +121,CheXpert-v1.0/train/patient60337/study1/view1_frontal.jpg,Female,24,Frontal,AP,,0.0,,,,,,,,0.0,,,,1.0 +122,CheXpert-v1.0/train/patient38131/study13/view1_frontal.jpg,Female,80,Frontal,AP,1.0,,,,,,,,,,,,,1.0 +123,CheXpert-v1.0/train/patient05068/study1/view1_frontal.jpg,Female,53,Frontal,PA,,,1.0,,,-1.0,,,,,,,, +124,CheXpert-v1.0/train/patient37777/study8/view1_frontal.jpg,Female,72,Frontal,AP,,,,,,1.0,,,,,1.0,,,1.0 +125,CheXpert-v1.0/train/patient07084/study21/view1_frontal.jpg,Female,40,Frontal,AP,,,,1.0,,1.0,,,,,1.0,,, +126,CheXpert-v1.0/train/patient34625/study1/view1_frontal.jpg,Female,90,Frontal,AP,,,,1.0,,-1.0,,-1.0,,,-1.0,,, +127,CheXpert-v1.0/train/patient37977/study6/view1_frontal.jpg,Female,53,Frontal,AP,,,,1.0,,,,,,-1.0,1.0,,,1.0 +128,CheXpert-v1.0/train/patient14915/study1/view1_frontal.jpg,Female,59,Frontal,AP,,,,1.0,,,,,,0.0,-1.0,,,1.0 +129,CheXpert-v1.0/train/patient02231/study2/view1_frontal.jpg,Female,61,Frontal,AP,,,,1.0,,,-1.0,,-1.0,,1.0,,,1.0 +130,CheXpert-v1.0/train/patient36578/study2/view1_frontal.jpg,Female,59,Frontal,AP,,-1.0,,1.0,,1.0,,,,,1.0,,,1.0 +131,CheXpert-v1.0/train/patient16867/study1/view1_frontal.jpg,Female,79,Frontal,AP,,,,1.0,,-1.0,,,,0.0,,,,1.0 +132,CheXpert-v1.0/train/patient36692/study6/view1_frontal.jpg,Female,79,Frontal,AP,,,,1.0,,1.0,,,,,1.0,,, +133,CheXpert-v1.0/train/patient64163/study1/view1_frontal.jpg,Female,57,Frontal,AP,1.0,0.0,,,,,0.0,,,0.0,0.0,,,1.0 +134,CheXpert-v1.0/train/patient45738/study6/view1_frontal.jpg,Female,62,Frontal,AP,,,1.0,,,0.0,,,,,-1.0,,,1.0 +135,CheXpert-v1.0/train/patient02665/study8/view1_frontal.jpg,Female,40,Frontal,AP,,,,1.0,,,-1.0,,-1.0,,1.0,,,1.0 +136,CheXpert-v1.0/train/patient53956/study1/view1_frontal.jpg,Female,78,Frontal,AP,,0.0,,,,,-1.0,,-1.0,0.0,1.0,,,1.0 +137,CheXpert-v1.0/train/patient19445/study1/view1_frontal.jpg,Female,74,Frontal,PA,,,-1.0,,,,,,,,1.0,,, +138,CheXpert-v1.0/train/patient36473/study3/view1_frontal.jpg,Female,75,Frontal,AP,,,,,,1.0,,,1.0,,,,,1.0 +139,CheXpert-v1.0/train/patient56850/study1/view1_frontal.jpg,Female,50,Frontal,AP,,,,1.0,,,-1.0,,-1.0,,1.0,,, +140,CheXpert-v1.0/train/patient30229/study1/view1_frontal.jpg,Female,82,Frontal,PA,,,,1.0,,,,,1.0,0.0,1.0,,,1.0 +141,CheXpert-v1.0/train/patient40235/study17/view1_frontal.jpg,Female,65,Frontal,AP,,,,1.0,,,,,,0.0,1.0,,,1.0 +142,CheXpert-v1.0/train/patient32287/study1/view1_frontal.jpg,Female,36,Frontal,AP,,1.0,,,,,,,,0.0,,,0.0, +143,CheXpert-v1.0/train/patient09187/study1/view1_frontal.jpg,Female,81,Frontal,AP,,,,1.0,,-1.0,,-1.0,,,1.0,,, +144,CheXpert-v1.0/train/patient45884/study2/view1_frontal.jpg,Female,82,Frontal,AP,,,,1.0,,1.0,,-1.0,,,,,, +145,CheXpert-v1.0/train/patient51113/study1/view1_frontal.jpg,Female,77,Frontal,AP,,,-1.0,,,1.0,,,,,1.0,,, +146,CheXpert-v1.0/train/patient13608/study1/view1_frontal.jpg,Female,68,Frontal,AP,1.0,0.0,,,,,,,,0.0,0.0,,,1.0 +147,CheXpert-v1.0/train/patient53343/study2/view1_frontal.jpg,Female,86,Frontal,AP,,-1.0,,1.0,,,,,,0.0,,,1.0, +148,CheXpert-v1.0/train/patient04058/study1/view1_frontal.jpg,Female,90,Frontal,AP,,0.0,,,,0.0,0.0,,,,0.0,,, +149,CheXpert-v1.0/train/patient01141/study6/view1_frontal.jpg,Female,56,Frontal,AP,,,,1.0,,,,-1.0,,0.0,,,,1.0 +150,CheXpert-v1.0/train/patient62861/study1/view1_frontal.jpg,Female,48,Frontal,AP,,,1.0,0.0,,1.0,,,,0.0,0.0,,, +151,CheXpert-v1.0/train/patient28234/study2/view1_frontal.jpg,Female,62,Frontal,PA,,,,1.0,,0.0,,0.0,,,-1.0,1.0,, +152,CheXpert-v1.0/train/patient39742/study3/view1_frontal.jpg,Female,54,Frontal,AP,,,0.0,,,,-1.0,,,-1.0,-1.0,,, +153,CheXpert-v1.0/train/patient40935/study2/view1_frontal.jpg,Female,28,Frontal,AP,,,,,,,,,,,1.0,,,0.0 +154,CheXpert-v1.0/train/patient02816/study1/view1_frontal.jpg,Female,53,Frontal,AP,,0.0,0.0,,,,0.0,,,,0.0,,, +155,CheXpert-v1.0/train/patient02992/study2/view1_frontal.jpg,Female,43,Frontal,PA,,,,1.0,,,-1.0,,1.0,,0.0,,, +156,CheXpert-v1.0/train/patient19874/study1/view1_frontal.jpg,Female,74,Frontal,AP,,,0.0,,,,,,,0.0,0.0,,1.0, +157,CheXpert-v1.0/train/patient40935/study3/view1_frontal.jpg,Female,31,Frontal,AP,,,,1.0,,,,1.0,-1.0,,1.0,,,1.0 +158,CheXpert-v1.0/train/patient06177/study1/view1_frontal.jpg,Female,45,Frontal,AP,,,,,,,,,1.0,0.0,,,,1.0 +159,CheXpert-v1.0/train/patient21948/study2/view1_frontal.jpg,Female,67,Frontal,AP,,,,1.0,,,,,1.0,,1.0,,,1.0 +160,CheXpert-v1.0/train/patient09592/study6/view1_frontal.jpg,Female,42,Frontal,PA,,,0.0,,1.0,,-1.0,,,-1.0,-1.0,,,1.0 +161,CheXpert-v1.0/train/patient10816/study1/view1_frontal.jpg,Female,22,Frontal,AP,,,,-1.0,,,-1.0,,1.0,,,,, +162,CheXpert-v1.0/train/patient42153/study5/view1_frontal.jpg,Female,60,Frontal,AP,,,-1.0,1.0,1.0,0.0,0.0,,,0.0,0.0,,,1.0 +163,CheXpert-v1.0/train/patient07128/study1/view1_frontal.jpg,Female,48,Frontal,AP,,,,,,,,,,0.0,,,1.0, +164,CheXpert-v1.0/train/patient32494/study14/view1_frontal.jpg,Female,62,Frontal,AP,1.0,0.0,,,,0.0,0.0,,,0.0,,,,1.0 +165,CheXpert-v1.0/train/patient29299/study1/view1_frontal.jpg,Female,69,Frontal,AP,,,1.0,1.0,,,,,,0.0,,,,1.0 +166,CheXpert-v1.0/train/patient41767/study1/view2_frontal.jpg,Female,44,Frontal,AP,,,,,,,,,,0.0,,,1.0, +167,CheXpert-v1.0/train/patient18225/study1/view1_frontal.jpg,Female,76,Frontal,PA,,,1.0,,,0.0,0.0,,,0.0,0.0,,, +168,CheXpert-v1.0/train/patient35099/study11/view1_frontal.jpg,Female,76,Frontal,AP,,,,,,,,,,-1.0,,,, +169,CheXpert-v1.0/train/patient34641/study21/view1_frontal.jpg,Female,78,Frontal,AP,,,,1.0,,,,,1.0,,1.0,,, +170,CheXpert-v1.0/train/patient55546/study2/view1_frontal.jpg,Female,61,Frontal,AP,,,,1.0,1.0,,,,,,1.0,,,1.0 +171,CheXpert-v1.0/train/patient30871/study4/view1_frontal.jpg,Female,65,Frontal,AP,,,1.0,,,-1.0,0.0,,,,0.0,,,1.0 +172,CheXpert-v1.0/train/patient59636/study1/view1_frontal.jpg,Female,66,Frontal,AP,,-1.0,,-1.0,,0.0,0.0,,-1.0,,0.0,,, +173,CheXpert-v1.0/train/patient04852/study2/view1_frontal.jpg,Female,57,Frontal,AP,,,,1.0,,,-1.0,,-1.0,,,,, +174,CheXpert-v1.0/train/patient08251/study2/view3_frontal.jpg,Female,43,Frontal,AP,,,,,,-1.0,,,,0.0,,,,1.0 +175,CheXpert-v1.0/train/patient25752/study1/view1_frontal.jpg,Female,55,Frontal,PA,,,,1.0,1.0,,,1.0,,,1.0,,, +176,CheXpert-v1.0/train/patient18823/study17/view1_frontal.jpg,Female,68,Frontal,PA,,0.0,0.0,,,,-1.0,,,-1.0,-1.0,,, +177,CheXpert-v1.0/train/patient12523/study3/view1_frontal.jpg,Female,56,Frontal,AP,,,,1.0,1.0,-1.0,,-1.0,,,,,, +178,CheXpert-v1.0/train/patient18242/study4/view1_frontal.jpg,Female,71,Frontal,AP,,,,,,,-1.0,,-1.0,,1.0,,,1.0 +179,CheXpert-v1.0/train/patient28786/study2/view1_frontal.jpg,Female,60,Frontal,AP,,,,1.0,,1.0,,,,,1.0,,, +180,CheXpert-v1.0/train/patient11095/study1/view1_frontal.jpg,Female,58,Frontal,AP,,,1.0,,,1.0,,,,,,,, +181,CheXpert-v1.0/train/patient55475/study1/view1_frontal.jpg,Female,87,Frontal,AP,,,,,,,,,-1.0,,-1.0,,, +182,CheXpert-v1.0/train/patient54779/study3/view1_frontal.jpg,Female,64,Frontal,AP,,,,,,0.0,,,0.0,,0.0,,,1.0 +183,CheXpert-v1.0/train/patient35824/study2/view1_frontal.jpg,Female,43,Frontal,AP,,,,1.0,,,,,,,1.0,,,1.0 +184,CheXpert-v1.0/train/patient24091/study1/view1_frontal.jpg,Female,39,Frontal,PA,,0.0,,1.0,,,,1.0,,,,,, +185,CheXpert-v1.0/train/patient41713/study2/view1_frontal.jpg,Female,53,Frontal,AP,,,,,,,,,,0.0,1.0,,, +186,CheXpert-v1.0/train/patient35272/study5/view1_frontal.jpg,Female,37,Frontal,AP,,,,1.0,,,,,,,1.0,,,1.0 +187,CheXpert-v1.0/train/patient44325/study1/view1_frontal.jpg,Female,61,Frontal,AP,,,,1.0,,,,,1.0,0.0,,,,1.0 +188,CheXpert-v1.0/train/patient49263/study1/view2_frontal.jpg,Female,90,Frontal,AP,,-1.0,,1.0,,,0.0,,,0.0,0.0,,, +189,CheXpert-v1.0/train/patient34716/study12/view1_frontal.jpg,Female,69,Frontal,AP,,-1.0,,1.0,,1.0,,,,,1.0,,,1.0 +190,CheXpert-v1.0/train/patient13309/study10/view1_frontal.jpg,Female,47,Frontal,AP,,,,1.0,,,,,,,1.0,,,1.0 +191,CheXpert-v1.0/train/patient35956/study10/view1_frontal.jpg,Female,20,Frontal,AP,,,,1.0,,,,,,,1.0,,,1.0 +192,CheXpert-v1.0/train/patient08840/study5/view1_frontal.jpg,Female,89,Frontal,AP,,,,1.0,,,,,,0.0,1.0,,,1.0 +193,CheXpert-v1.0/train/patient37374/study1/view1_frontal.jpg,Female,63,Frontal,AP,,,,1.0,,,-1.0,,-1.0,0.0,0.0,,,1.0 +194,CheXpert-v1.0/train/patient55488/study1/view1_frontal.jpg,Female,75,Frontal,AP,,,1.0,1.0,,1.0,,-1.0,,,1.0,,, +195,CheXpert-v1.0/train/patient45733/study1/view1_frontal.jpg,Female,30,Frontal,AP,,,,,,1.0,,,,,1.0,,, +196,CheXpert-v1.0/train/patient21392/study2/view1_frontal.jpg,Female,68,Frontal,AP,,,,1.0,1.0,,-1.0,,-1.0,,,,, +197,CheXpert-v1.0/train/patient39711/study2/view1_frontal.jpg,Female,43,Frontal,AP,,,,,,,1.0,,,,,,,1.0 +198,CheXpert-v1.0/train/patient00177/study1/view1_frontal.jpg,Female,84,Frontal,AP,,,,,,-1.0,,,,0.0,,,,1.0 +199,CheXpert-v1.0/train/patient59666/study1/view1_frontal.jpg,Female,79,Frontal,AP,,,,1.0,,-1.0,,-1.0,1.0,,-1.0,,, +200,CheXpert-v1.0/train/patient01801/study7/view1_frontal.jpg,Male,53,Frontal,AP,,,,1.0,,,1.0,,,1.0,1.0,,,1.0 +201,CheXpert-v1.0/train/patient18561/study2/view1_frontal.jpg,Male,61,Frontal,AP,,,,,,,,,,1.0,,,1.0, +202,CheXpert-v1.0/train/patient35776/study14/view1_frontal.jpg,Male,90,Frontal,AP,,,1.0,1.0,,,,,,1.0,1.0,,,1.0 +203,CheXpert-v1.0/train/patient04064/study11/view1_frontal.jpg,Male,68,Frontal,AP,,,,,,,,,,1.0,1.0,,, +204,CheXpert-v1.0/train/patient36309/study13/view1_frontal.jpg,Male,51,Frontal,AP,,-1.0,,,,0.0,,,,1.0,,,,1.0 +205,CheXpert-v1.0/train/patient11306/study3/view1_frontal.jpg,Male,58,Frontal,PA,,,,1.0,,,,,-1.0,1.0,,,,0.0 +206,CheXpert-v1.0/train/patient35725/study10/view1_frontal.jpg,Male,26,Frontal,AP,,-1.0,,1.0,,0.0,,,1.0,1.0,,,,1.0 +207,CheXpert-v1.0/train/patient48339/study2/view1_frontal.jpg,Male,85,Frontal,AP,,,,,,,0.0,,,1.0,0.0,,0.0, +208,CheXpert-v1.0/train/patient46323/study1/view1_frontal.jpg,Male,59,Frontal,AP,,,,,,,,,1.0,1.0,,,,1.0 +209,CheXpert-v1.0/train/patient14972/study12/view1_frontal.jpg,Male,42,Frontal,AP,,,,1.0,,,,,,1.0,,,, +210,CheXpert-v1.0/train/patient28654/study4/view1_frontal.jpg,Male,35,Frontal,AP,,,,1.0,,,,,,1.0,,,,1.0 +211,CheXpert-v1.0/train/patient14949/study6/view1_frontal.jpg,Male,76,Frontal,AP,,,,1.0,,1.0,,,,1.0,1.0,,,1.0 +212,CheXpert-v1.0/train/patient42275/study1/view1_frontal.jpg,Male,73,Frontal,AP,,,,1.0,,1.0,,,,1.0,,,,1.0 +213,CheXpert-v1.0/train/patient40915/study2/view1_frontal.jpg,Male,64,Frontal,AP,,,,1.0,,,,,,1.0,1.0,,,1.0 +214,CheXpert-v1.0/train/patient47931/study3/view1_frontal.jpg,Male,70,Frontal,AP,,,,,,,,,,1.0,1.0,,1.0,1.0 +215,CheXpert-v1.0/train/patient13849/study12/view1_frontal.jpg,Male,37,Frontal,AP,,,0.0,,,1.0,-1.0,,-1.0,1.0,,,,1.0 +216,CheXpert-v1.0/train/patient27632/study8/view1_frontal.jpg,Male,68,Frontal,PA,,,,,,,,,,1.0,,1.0,, +217,CheXpert-v1.0/train/patient42151/study13/view1_frontal.jpg,Male,42,Frontal,AP,,-1.0,,1.0,,-1.0,1.0,,,1.0,1.0,,,1.0 +218,CheXpert-v1.0/train/patient07074/study19/view1_frontal.jpg,Male,79,Frontal,AP,,,,1.0,,,,,,1.0,,,,1.0 +219,CheXpert-v1.0/train/patient00750/study5/view1_frontal.jpg,Male,68,Frontal,AP,,,,1.0,,,,,,1.0,1.0,,,0.0 +220,CheXpert-v1.0/train/patient04462/study73/view1_frontal.jpg,Male,58,Frontal,AP,,,,1.0,,-1.0,,-1.0,,1.0,,,, +221,CheXpert-v1.0/train/patient26424/study2/view1_frontal.jpg,Male,20,Frontal,AP,,,,,,,,,,1.0,,,, +222,CheXpert-v1.0/train/patient13849/study19/view1_frontal.jpg,Male,38,Frontal,AP,,,,1.0,,,,,,1.0,,,,1.0 +223,CheXpert-v1.0/train/patient47557/study1/view1_frontal.jpg,Male,50,Frontal,AP,,,,1.0,,,,,,1.0,,,, +224,CheXpert-v1.0/train/patient20727/study5/view1_frontal.jpg,Male,62,Frontal,AP,,,,1.0,1.0,,,,,1.0,,,,1.0 +225,CheXpert-v1.0/train/patient09259/study3/view1_frontal.jpg,Male,65,Frontal,AP,,,,,1.0,,,,,1.0,,,, +226,CheXpert-v1.0/train/patient09773/study3/view1_frontal.jpg,Male,33,Frontal,PA,,,,,,,,,,1.0,1.0,,,1.0 +227,CheXpert-v1.0/train/patient43542/study2/view1_frontal.jpg,Male,62,Frontal,AP,,,,,,,,,,1.0,,,,1.0 +228,CheXpert-v1.0/train/patient10648/study5/view1_frontal.jpg,Male,51,Frontal,AP,,,,1.0,,,,,,1.0,,,,0.0 +229,CheXpert-v1.0/train/patient30576/study4/view1_frontal.jpg,Male,59,Frontal,AP,,,,1.0,,,-1.0,,-1.0,1.0,1.0,,,1.0 +230,CheXpert-v1.0/train/patient22242/study1/view1_frontal.jpg,Male,62,Frontal,AP,,,,-1.0,,,,,1.0,1.0,-1.0,,,1.0 +231,CheXpert-v1.0/train/patient00142/study10/view1_frontal.jpg,Male,62,Frontal,PA,,,,,,,,,,1.0,,,,1.0 +232,CheXpert-v1.0/train/patient07074/study1/view1_frontal.jpg,Male,79,Frontal,AP,,,,,,,,,,1.0,,,,1.0 +233,CheXpert-v1.0/train/patient04573/study4/view1_frontal.jpg,Male,60,Frontal,PA,,,,,,,,,,1.0,1.0,,, +234,CheXpert-v1.0/train/patient23351/study5/view1_frontal.jpg,Male,38,Frontal,AP,,,1.0,,,,,,,1.0,,,,1.0 +235,CheXpert-v1.0/train/patient34258/study1/view1_frontal.jpg,Male,42,Frontal,AP,,,,,,,,,,1.0,,,,1.0 +236,CheXpert-v1.0/train/patient20733/study7/view1_frontal.jpg,Male,74,Frontal,AP,,,,1.0,,,,,,1.0,1.0,,,0.0 +237,CheXpert-v1.0/train/patient32731/study1/view1_frontal.jpg,Male,68,Frontal,AP,,,,1.0,,,,,-1.0,1.0,,,,1.0 +238,CheXpert-v1.0/train/patient15318/study6/view1_frontal.jpg,Male,24,Frontal,PA,,,,,,,,,,1.0,1.0,,, +239,CheXpert-v1.0/train/patient11022/study2/view1_frontal.jpg,Male,30,Frontal,AP,,,,,,,,,,1.0,,,,1.0 +240,CheXpert-v1.0/train/patient15287/study2/view1_frontal.jpg,Male,54,Frontal,AP,,,,1.0,,,,,,1.0,,,,1.0 +241,CheXpert-v1.0/train/patient09593/study1/view1_frontal.jpg,Male,47,Frontal,AP,,1.0,,1.0,,,,,1.0,1.0,,,,1.0 +242,CheXpert-v1.0/train/patient18382/study5/view1_frontal.jpg,Male,49,Frontal,AP,,,,,,,,,1.0,1.0,1.0,,,1.0 +243,CheXpert-v1.0/train/patient48367/study3/view1_frontal.jpg,Male,57,Frontal,AP,,,1.0,,,1.0,,,1.0,1.0,1.0,,, +244,CheXpert-v1.0/train/patient15243/study4/view1_frontal.jpg,Male,76,Frontal,AP,,,,,,,1.0,,,1.0,,,,1.0 +245,CheXpert-v1.0/train/patient17414/study3/view1_frontal.jpg,Male,81,Frontal,AP,,,,,,,,,,1.0,,,, +246,CheXpert-v1.0/train/patient00826/study9/view1_frontal.jpg,Male,57,Frontal,AP,,,,1.0,,-1.0,,,,1.0,,,,1.0 +247,CheXpert-v1.0/train/patient04667/study9/view1_frontal.jpg,Male,59,Frontal,AP,,,1.0,1.0,,,,,1.0,1.0,1.0,,,1.0 +248,CheXpert-v1.0/train/patient42144/study4/view1_frontal.jpg,Male,85,Frontal,AP,,,,,,,,,,1.0,1.0,,,0.0 +249,CheXpert-v1.0/train/patient38482/study2/view1_frontal.jpg,Male,54,Frontal,AP,,,,,,-1.0,,-1.0,,1.0,,,, +250,CheXpert-v1.0/train/patient11575/study6/view1_frontal.jpg,Male,40,Frontal,AP,,,,,,,,,,1.0,1.0,,, +251,CheXpert-v1.0/train/patient10936/study6/view1_frontal.jpg,Male,78,Frontal,AP,,,,,,1.0,,,,1.0,,,,1.0 +252,CheXpert-v1.0/train/patient41750/study4/view1_frontal.jpg,Male,60,Frontal,AP,,,1.0,,,,-1.0,,-1.0,1.0,,,,1.0 +253,CheXpert-v1.0/train/patient44563/study3/view1_frontal.jpg,Male,82,Frontal,AP,,,,,,0.0,-1.0,,-1.0,1.0,0.0,,,1.0 +254,CheXpert-v1.0/train/patient02530/study1/view1_frontal.jpg,Male,73,Frontal,AP,,,,,,,,,,1.0,,1.0,, +255,CheXpert-v1.0/train/patient09193/study5/view1_frontal.jpg,Male,52,Frontal,PA,,,,1.0,,,,-1.0,-1.0,1.0,1.0,,, +256,CheXpert-v1.0/train/patient10299/study3/view1_frontal.jpg,Male,88,Frontal,AP,,,,1.0,,,-1.0,,-1.0,1.0,1.0,,,1.0 +257,CheXpert-v1.0/train/patient23675/study3/view1_frontal.jpg,Male,51,Frontal,PA,,,,1.0,,,,,,1.0,,,, +258,CheXpert-v1.0/train/patient26955/study1/view1_frontal.jpg,Male,82,Frontal,AP,,,,1.0,,1.0,,,1.0,1.0,,,,1.0 +259,CheXpert-v1.0/train/patient47962/study4/view1_frontal.jpg,Male,56,Frontal,AP,,,1.0,1.0,,-1.0,,-1.0,,1.0,,,,1.0 +260,CheXpert-v1.0/train/patient22065/study4/view1_frontal.jpg,Male,51,Frontal,AP,,,,1.0,,,,,1.0,1.0,,,,1.0 +261,CheXpert-v1.0/train/patient21764/study22/view1_frontal.jpg,Male,74,Frontal,AP,,,,1.0,,,,,,1.0,1.0,,,1.0 +262,CheXpert-v1.0/train/patient39137/study3/view1_frontal.jpg,Male,78,Frontal,AP,,,1.0,1.0,,,-1.0,,-1.0,1.0,1.0,,,1.0 +263,CheXpert-v1.0/train/patient01932/study12/view1_frontal.jpg,Male,28,Frontal,AP,,,,1.0,,-1.0,,,-1.0,1.0,0.0,,,1.0 +264,CheXpert-v1.0/train/patient22784/study12/view1_frontal.jpg,Male,50,Frontal,AP,,,,,,,,,,1.0,,,,1.0 +265,CheXpert-v1.0/train/patient32997/study6/view1_frontal.jpg,Male,59,Frontal,AP,,,,,,1.0,,,1.0,1.0,1.0,,,1.0 +266,CheXpert-v1.0/train/patient20176/study1/view1_frontal.jpg,Male,70,Frontal,AP,,-1.0,,,,,,,1.0,1.0,,,,1.0 +267,CheXpert-v1.0/train/patient33890/study17/view1_frontal.jpg,Male,64,Frontal,AP,,,,,,,,,,1.0,,,, +268,CheXpert-v1.0/train/patient30127/study4/view1_frontal.jpg,Male,56,Frontal,AP,,1.0,,1.0,,,,,,1.0,,,,1.0 +269,CheXpert-v1.0/train/patient30330/study1/view1_frontal.jpg,Male,29,Frontal,PA,,,-1.0,,,,,,,1.0,,,,1.0 +270,CheXpert-v1.0/train/patient53807/study1/view1_frontal.jpg,Male,46,Frontal,AP,,,,1.0,,1.0,,,,1.0,,,,1.0 +271,CheXpert-v1.0/train/patient22019/study1/view1_frontal.jpg,Male,54,Frontal,PA,,,-1.0,-1.0,,0.0,1.0,,-1.0,1.0,,,,0.0 +272,CheXpert-v1.0/train/patient14844/study3/view2_frontal.jpg,Male,65,Frontal,AP,,,,,,1.0,,,,1.0,1.0,,,1.0 +273,CheXpert-v1.0/train/patient02450/study2/view1_frontal.jpg,Male,70,Frontal,AP,,,,1.0,,,,-1.0,,1.0,,,,1.0 +274,CheXpert-v1.0/train/patient08549/study20/view1_frontal.jpg,Male,68,Frontal,AP,,-1.0,,1.0,,,,,,1.0,1.0,1.0,, +275,CheXpert-v1.0/train/patient16153/study4/view1_frontal.jpg,Male,72,Frontal,AP,,,,,,1.0,,,1.0,1.0,1.0,,,1.0 +276,CheXpert-v1.0/train/patient39001/study5/view1_frontal.jpg,Male,78,Frontal,AP,,,,,,,,,1.0,1.0,,,, +277,CheXpert-v1.0/train/patient00170/study2/view1_frontal.jpg,Male,42,Frontal,AP,,,,,,,,,,1.0,,,,1.0 +278,CheXpert-v1.0/train/patient30222/study2/view1_frontal.jpg,Male,65,Frontal,AP,,-1.0,,1.0,,,,,,1.0,,,,0.0 +279,CheXpert-v1.0/train/patient41011/study1/view1_frontal.jpg,Male,51,Frontal,AP,,,1.0,1.0,,1.0,,,1.0,1.0,,,1.0,1.0 +280,CheXpert-v1.0/train/patient60980/study1/view1_frontal.jpg,Male,37,Frontal,AP,,,,,,,,,,1.0,,,,1.0 +281,CheXpert-v1.0/train/patient12926/study4/view1_frontal.jpg,Male,25,Frontal,AP,,,,1.0,,,,,,1.0,1.0,,,1.0 +282,CheXpert-v1.0/train/patient09530/study10/view1_frontal.jpg,Male,35,Frontal,PA,,,,1.0,,,,,,1.0,1.0,,,1.0 +283,CheXpert-v1.0/train/patient15507/study1/view1_frontal.jpg,Male,26,Frontal,AP,,,,,,,0.0,,,1.0,,,0.0,0.0 +284,CheXpert-v1.0/train/patient40209/study6/view1_frontal.jpg,Male,29,Frontal,AP,,,,1.0,,-1.0,,,,1.0,1.0,,,1.0 +285,CheXpert-v1.0/train/patient42579/study1/view1_frontal.jpg,Male,21,Frontal,AP,,,,,,,,,,1.0,,,,1.0 +286,CheXpert-v1.0/train/patient29565/study1/view1_frontal.jpg,Male,20,Frontal,AP,,,,1.0,,,,,,1.0,,,,1.0 +287,CheXpert-v1.0/train/patient59120/study1/view1_frontal.jpg,Male,18,Frontal,AP,,1.0,,,,,,,,1.0,,,, +288,CheXpert-v1.0/train/patient06686/study4/view1_frontal.jpg,Male,81,Frontal,AP,,,-1.0,1.0,,,,,,1.0,,,,1.0 +289,CheXpert-v1.0/train/patient03699/study4/view1_frontal.jpg,Male,50,Frontal,AP,,-1.0,,1.0,,,,,,1.0,1.0,,1.0,1.0 +290,CheXpert-v1.0/train/patient27078/study9/view1_frontal.jpg,Male,37,Frontal,PA,,,,,,1.0,,,,1.0,,,,1.0 +291,CheXpert-v1.0/train/patient53402/study4/view1_frontal.jpg,Male,26,Frontal,AP,,,,1.0,,,,-1.0,-1.0,1.0,1.0,,,1.0 +292,CheXpert-v1.0/train/patient13337/study2/view1_frontal.jpg,Male,76,Frontal,AP,,,,1.0,-1.0,,,,,1.0,,,, +293,CheXpert-v1.0/train/patient11606/study10/view1_frontal.jpg,Male,46,Frontal,PA,,,,1.0,,,,,,1.0,1.0,,, +294,CheXpert-v1.0/train/patient00259/study1/view1_frontal.jpg,Male,90,Frontal,AP,,,,,,,,,,1.0,,,, +295,CheXpert-v1.0/train/patient26973/study9/view1_frontal.jpg,Male,66,Frontal,AP,,,,,,,,,,1.0,1.0,,, +296,CheXpert-v1.0/train/patient49668/study1/view1_frontal.jpg,Male,46,Frontal,AP,,,,,,,,,-1.0,1.0,,,, +297,CheXpert-v1.0/train/patient30913/study8/view2_frontal.jpg,Male,79,Frontal,AP,,,,,,1.0,,,,1.0,,,,1.0 +298,CheXpert-v1.0/train/patient36289/study8/view1_frontal.jpg,Male,21,Frontal,AP,,,,1.0,,,,,,1.0,1.0,,,1.0 +299,CheXpert-v1.0/train/patient35058/study3/view1_frontal.jpg,Male,68,Frontal,AP,,,,,,,,,,1.0,,1.0,,1.0 +300,CheXpert-v1.0/train/patient35203/study24/view1_frontal.jpg,Male,66,Frontal,AP,,,,1.0,,,,,,,1.0,,,1.0 +301,CheXpert-v1.0/train/patient31483/study15/view1_frontal.jpg,Male,71,Frontal,AP,,,,1.0,,,,,1.0,,,,,1.0 +302,CheXpert-v1.0/train/patient30416/study19/view1_frontal.jpg,Male,61,Frontal,AP,,,,1.0,,-1.0,0.0,-1.0,,,1.0,,,1.0 +303,CheXpert-v1.0/train/patient60130/study1/view1_frontal.jpg,Male,50,Frontal,AP,,,1.0,1.0,,,,,-1.0,,1.0,,, +304,CheXpert-v1.0/train/patient05232/study18/view1_frontal.jpg,Male,53,Frontal,AP,,,,1.0,,1.0,,,,,1.0,,,1.0 +305,CheXpert-v1.0/train/patient49197/study1/view1_frontal.jpg,Male,54,Frontal,AP,,,,-1.0,,,,,-1.0,0.0,,,,1.0 +306,CheXpert-v1.0/train/patient15081/study34/view1_frontal.jpg,Male,60,Frontal,AP,,,,,,1.0,,,,,,,,1.0 +307,CheXpert-v1.0/train/patient20789/study7/view1_frontal.jpg,Male,76,Frontal,AP,,,-1.0,1.0,,,,,,0.0,0.0,,,1.0 +308,CheXpert-v1.0/train/patient55977/study1/view1_frontal.jpg,Male,39,Frontal,AP,,,,,,1.0,,,1.0,,,,,1.0 +309,CheXpert-v1.0/train/patient45650/study4/view1_frontal.jpg,Male,71,Frontal,AP,,,1.0,1.0,,,,,,0.0,-1.0,,,1.0 +310,CheXpert-v1.0/train/patient52050/study1/view1_frontal.jpg,Male,81,Frontal,AP,,,,,,1.0,-1.0,,-1.0,,,,,1.0 +311,CheXpert-v1.0/train/patient40462/study2/view1_frontal.jpg,Male,75,Frontal,AP,,,,1.0,,,-1.0,,-1.0,,1.0,,,1.0 +312,CheXpert-v1.0/train/patient14744/study3/view1_frontal.jpg,Male,52,Frontal,AP,,-1.0,,1.0,,,,,,0.0,1.0,,,1.0 +313,CheXpert-v1.0/train/patient12096/study16/view1_frontal.jpg,Male,58,Frontal,AP,,,,,,,1.0,,1.0,0.0,,,, +314,CheXpert-v1.0/train/patient38194/study4/view1_frontal.jpg,Male,65,Frontal,AP,,,,1.0,,,,,,,1.0,,,1.0 +315,CheXpert-v1.0/train/patient48059/study8/view1_frontal.jpg,Male,71,Frontal,AP,,,,,,1.0,,1.0,,,,,, +316,CheXpert-v1.0/train/patient41889/study1/view1_frontal.jpg,Male,66,Frontal,AP,,,,1.0,,1.0,,,,,1.0,,,1.0 +317,CheXpert-v1.0/train/patient29096/study9/view1_frontal.jpg,Male,87,Frontal,PA,,,1.0,-1.0,,,0.0,,-1.0,0.0,0.0,,,1.0 +318,CheXpert-v1.0/train/patient56201/study1/view1_frontal.jpg,Male,67,Frontal,AP,,,,1.0,,,,,0.0,,1.0,,,1.0 +319,CheXpert-v1.0/train/patient30450/study1/view1_frontal.jpg,Male,82,Frontal,AP,,,1.0,,,1.0,,,1.0,,1.0,,,1.0 +320,CheXpert-v1.0/train/patient13193/study5/view2_frontal.jpg,Male,70,Frontal,AP,,,1.0,1.0,,,,,,,1.0,,,1.0 +321,CheXpert-v1.0/train/patient11322/study1/view1_frontal.jpg,Male,76,Frontal,AP,,0.0,,1.0,1.0,,,,,0.0,,,,1.0 +322,CheXpert-v1.0/train/patient08298/study3/view1_frontal.jpg,Male,55,Frontal,AP,,,,1.0,,1.0,,,,,1.0,,,1.0 +323,CheXpert-v1.0/train/patient10288/study1/view1_frontal.jpg,Male,51,Frontal,AP,,,1.0,,,0.0,0.0,,,,,,, +324,CheXpert-v1.0/train/patient15826/study4/view1_frontal.jpg,Male,53,Frontal,AP,,,,,,,,,,,1.0,,, +325,CheXpert-v1.0/train/patient26040/study1/view1_frontal.jpg,Male,42,Frontal,PA,,0.0,0.0,,,,,,,,0.0,1.0,, +326,CheXpert-v1.0/train/patient34454/study7/view1_frontal.jpg,Male,53,Frontal,AP,,,,1.0,,,-1.0,,-1.0,,1.0,,,1.0 +327,CheXpert-v1.0/train/patient08549/study24/view1_frontal.jpg,Male,69,Frontal,AP,,,1.0,1.0,,1.0,,,1.0,,1.0,,1.0, +328,CheXpert-v1.0/train/patient34546/study1/view1_frontal.jpg,Male,58,Frontal,AP,,,-1.0,,,1.0,,,,,1.0,,,1.0 +329,CheXpert-v1.0/train/patient24654/study1/view1_frontal.jpg,Male,35,Frontal,PA,,1.0,,,,,,,,0.0,0.0,,1.0, +330,CheXpert-v1.0/train/patient22921/study1/view1_frontal.jpg,Male,79,Frontal,PA,,,0.0,1.0,,,,,-1.0,0.0,0.0,-1.0,, +331,CheXpert-v1.0/train/patient43382/study3/view1_frontal.jpg,Male,54,Frontal,AP,,,,,,1.0,,,,,1.0,,,1.0 +332,CheXpert-v1.0/train/patient18042/study7/view1_frontal.jpg,Male,54,Frontal,AP,,,,,,1.0,,,,,,,,1.0 +333,CheXpert-v1.0/train/patient46378/study6/view1_frontal.jpg,Male,75,Frontal,AP,,,,,,,,,,,1.0,,,1.0 +334,CheXpert-v1.0/train/patient16944/study1/view1_frontal.jpg,Male,60,Frontal,PA,,,0.0,,,,0.0,,,0.0,0.0,,, +335,CheXpert-v1.0/train/patient08291/study1/view1_frontal.jpg,Male,59,Frontal,AP,,,1.0,,,,-1.0,,-1.0,,1.0,,,1.0 +336,CheXpert-v1.0/train/patient03973/study2/view1_frontal.jpg,Male,71,Frontal,PA,,,,1.0,,,,-1.0,,,,,, +337,CheXpert-v1.0/train/patient19412/study1/view1_frontal.jpg,Male,71,Frontal,PA,,,0.0,1.0,,0.0,,,1.0,,1.0,,, +338,CheXpert-v1.0/train/patient33307/study3/view1_frontal.jpg,Male,70,Frontal,PA,,,1.0,,,0.0,0.0,,,0.0,0.0,,,1.0 +339,CheXpert-v1.0/train/patient30138/study1/view1_frontal.jpg,Male,51,Frontal,AP,1.0,,,,,,,,,0.0,,,,1.0 +340,CheXpert-v1.0/train/patient34631/study1/view1_frontal.jpg,Male,80,Frontal,AP,,,,1.0,,,,,,,1.0,,,1.0 +341,CheXpert-v1.0/train/patient14528/study2/view1_frontal.jpg,Male,83,Frontal,AP,,,,1.0,,-1.0,,-1.0,-1.0,,1.0,,,1.0 +342,CheXpert-v1.0/train/patient37720/study4/view1_frontal.jpg,Male,61,Frontal,AP,,,,1.0,,,,,,,1.0,,,1.0 +343,CheXpert-v1.0/train/patient26048/study1/view1_frontal.jpg,Male,74,Frontal,AP,,1.0,,,,-1.0,,,,,,,-1.0, +344,CheXpert-v1.0/train/patient18525/study1/view1_frontal.jpg,Male,55,Frontal,PA,,1.0,,,,0.0,0.0,,,0.0,0.0,,, +345,CheXpert-v1.0/train/patient11794/study5/view1_frontal.jpg,Male,70,Frontal,AP,,,,,,,,,,,1.0,,, +346,CheXpert-v1.0/train/patient08789/study2/view1_frontal.jpg,Male,56,Frontal,AP,,,1.0,,,1.0,,,,,1.0,,,1.0 +347,CheXpert-v1.0/train/patient47526/study1/view1_frontal.jpg,Male,74,Frontal,AP,1.0,,,,,,,,,0.0,,,,1.0 +348,CheXpert-v1.0/train/patient39432/study2/view1_frontal.jpg,Male,78,Frontal,AP,,,,,,,0.0,,,,,,,1.0 +349,CheXpert-v1.0/train/patient27931/study4/view1_frontal.jpg,Male,68,Frontal,AP,,,,-1.0,,,,,,0.0,1.0,,, +350,CheXpert-v1.0/train/patient13898/study3/view1_frontal.jpg,Male,52,Frontal,AP,,,,1.0,,,,,,0.0,,,,0.0 +351,CheXpert-v1.0/train/patient47024/study4/view1_frontal.jpg,Male,64,Frontal,AP,,,,,,1.0,,,,0.0,,,, +352,CheXpert-v1.0/train/patient11872/study5/view1_frontal.jpg,Male,59,Frontal,AP,,,,,,1.0,,,,,-1.0,,,1.0 +353,CheXpert-v1.0/train/patient41436/study1/view1_frontal.jpg,Male,45,Frontal,AP,,,0.0,,,,,,1.0,0.0,0.0,,,1.0 +354,CheXpert-v1.0/train/patient28806/study5/view1_frontal.jpg,Male,64,Frontal,AP,,,,,,,-1.0,,-1.0,,1.0,,,1.0 +355,CheXpert-v1.0/train/patient38034/study1/view1_frontal.jpg,Male,68,Frontal,AP,,,-1.0,1.0,,1.0,,,,,-1.0,,, +356,CheXpert-v1.0/train/patient25114/study4/view1_frontal.jpg,Male,80,Frontal,AP,,,,1.0,,,,-1.0,,,,,, +357,CheXpert-v1.0/train/patient20173/study20/view1_frontal.jpg,Male,43,Frontal,AP,,,,1.0,,,,1.0,,,1.0,,,1.0 +358,CheXpert-v1.0/train/patient52690/study1/view1_frontal.jpg,Male,84,Frontal,AP,,,0.0,,1.0,,,,,0.0,,,, +359,CheXpert-v1.0/train/patient20982/study1/view1_frontal.jpg,Male,65,Frontal,AP,,0.0,,1.0,,,-1.0,,-1.0,,,,,1.0 +360,CheXpert-v1.0/train/patient29629/study1/view1_frontal.jpg,Male,41,Frontal,AP,,,,,,-1.0,-1.0,,-1.0,,,,, +361,CheXpert-v1.0/train/patient56687/study1/view1_frontal.jpg,Male,79,Frontal,AP,,,,1.0,,1.0,,,,,1.0,,,1.0 +362,CheXpert-v1.0/train/patient06949/study12/view1_frontal.jpg,Male,64,Frontal,AP,1.0,,,,,,,,0.0,,0.0,,,1.0 +363,CheXpert-v1.0/train/patient48778/study4/view1_frontal.jpg,Male,56,Frontal,AP,,,,,,,,,,,1.0,,,1.0 +364,CheXpert-v1.0/train/patient32016/study2/view1_frontal.jpg,Male,53,Frontal,PA,,,,0.0,,1.0,,,,,,,,1.0 +365,CheXpert-v1.0/train/patient33620/study12/view1_frontal.jpg,Male,49,Frontal,AP,,,1.0,,,1.0,,,,,,,, +366,CheXpert-v1.0/train/patient03066/study3/view1_frontal.jpg,Male,61,Frontal,PA,,,1.0,1.0,,0.0,,,,,1.0,,,1.0 +367,CheXpert-v1.0/train/patient64249/study2/view1_frontal.jpg,Male,73,Frontal,AP,,,,1.0,,0.0,-1.0,,-1.0,0.0,1.0,,, +368,CheXpert-v1.0/train/patient35281/study1/view1_frontal.jpg,Male,74,Frontal,AP,,,,1.0,,1.0,,,1.0,0.0,1.0,,,1.0 +369,CheXpert-v1.0/train/patient51043/study1/view1_frontal.jpg,Male,53,Frontal,AP,,,-1.0,,,0.0,0.0,,,,,,,1.0 +370,CheXpert-v1.0/train/patient21529/study27/view1_frontal.jpg,Male,61,Frontal,AP,,,1.0,,,1.0,,,,,,,,1.0 +371,CheXpert-v1.0/train/patient26199/study30/view1_frontal.jpg,Male,56,Frontal,AP,,,,,1.0,1.0,,,1.0,,1.0,,,1.0 +372,CheXpert-v1.0/train/patient60582/study1/view1_frontal.jpg,Male,81,Frontal,AP,1.0,0.0,,,,0.0,0.0,,,,0.0,,,1.0 +373,CheXpert-v1.0/train/patient43105/study2/view1_frontal.jpg,Male,80,Frontal,AP,,0.0,,,,,,,,0.0,,,, +374,CheXpert-v1.0/train/patient60515/study1/view1_frontal.jpg,Male,85,Frontal,AP,,,,,,0.0,,0.0,1.0,,,,, +375,CheXpert-v1.0/train/patient31550/study2/view1_frontal.jpg,Male,69,Frontal,AP,,,,,,1.0,,,1.0,,,,,1.0 +376,CheXpert-v1.0/train/patient32429/study1/view1_frontal.jpg,Male,66,Frontal,AP,,,,1.0,,,,,,0.0,,,,1.0 +377,CheXpert-v1.0/train/patient36725/study5/view1_frontal.jpg,Male,43,Frontal,AP,,,,1.0,,1.0,,,1.0,,,,,1.0 +378,CheXpert-v1.0/train/patient09193/study11/view1_frontal.jpg,Male,52,Frontal,AP,,,,1.0,,,1.0,,,,1.0,,,1.0 +379,CheXpert-v1.0/train/patient14528/study6/view1_frontal.jpg,Male,83,Frontal,AP,,,,1.0,,,,,,0.0,0.0,,,1.0 +380,CheXpert-v1.0/train/patient17235/study2/view1_frontal.jpg,Male,38,Frontal,AP,,,,1.0,,,,,,0.0,1.0,,,1.0 +381,CheXpert-v1.0/train/patient54624/study1/view1_frontal.jpg,Male,73,Frontal,AP,,,1.0,,,,,,,,1.0,,, +382,CheXpert-v1.0/train/patient20347/study1/view1_frontal.jpg,Male,79,Frontal,PA,,0.0,,1.0,,0.0,,,,,0.0,,, +383,CheXpert-v1.0/train/patient16452/study3/view1_frontal.jpg,Male,82,Frontal,AP,,,,1.0,,,-1.0,,-1.0,,1.0,,, +384,CheXpert-v1.0/train/patient39810/study2/view1_frontal.jpg,Male,57,Frontal,AP,,,,1.0,,-1.0,,-1.0,,,,,,1.0 +385,CheXpert-v1.0/train/patient35972/study4/view1_frontal.jpg,Male,43,Frontal,AP,,,,1.0,,,,-1.0,,,,,,1.0 +386,CheXpert-v1.0/train/patient30159/study5/view1_frontal.jpg,Male,74,Frontal,AP,,,,1.0,1.0,,,,,,1.0,,,1.0 +387,CheXpert-v1.0/train/patient37043/study6/view1_frontal.jpg,Male,56,Frontal,AP,,-1.0,,0.0,,,0.0,,,,0.0,,,0.0 +388,CheXpert-v1.0/train/patient03537/study6/view1_frontal.jpg,Male,66,Frontal,AP,,0.0,,1.0,,-1.0,1.0,-1.0,,,,,,1.0 +389,CheXpert-v1.0/train/patient37912/study17/view1_frontal.jpg,Male,41,Frontal,AP,1.0,,,,,,0.0,,,0.0,0.0,,, +390,CheXpert-v1.0/train/patient49948/study2/view1_frontal.jpg,Male,54,Frontal,AP,,,,1.0,,,,,,,1.0,,,1.0 +391,CheXpert-v1.0/train/patient37826/study1/view1_frontal.jpg,Male,61,Frontal,AP,,,,,,1.0,-1.0,,-1.0,,1.0,,,1.0 +392,CheXpert-v1.0/train/patient31733/study1/view1_frontal.jpg,Male,62,Frontal,AP,,,,,,,-1.0,1.0,-1.0,0.0,,,,1.0 +393,CheXpert-v1.0/train/patient34903/study29/view1_frontal.jpg,Male,72,Frontal,AP,,,,1.0,,1.0,,,,0.0,1.0,,,1.0 +394,CheXpert-v1.0/train/patient05134/study3/view1_frontal.jpg,Male,63,Frontal,AP,,,,1.0,,1.0,,1.0,,,,,, +395,CheXpert-v1.0/train/patient02362/study6/view1_frontal.jpg,Male,78,Frontal,AP,,,,1.0,,1.0,1.0,,,,,,,1.0 +396,CheXpert-v1.0/train/patient39300/study4/view1_frontal.jpg,Male,63,Frontal,AP,1.0,,0.0,0.0,,0.0,,,,,0.0,,,1.0 +397,CheXpert-v1.0/train/patient41430/study2/view1_frontal.jpg,Male,82,Frontal,AP,,,,1.0,,1.0,,,,,,,, +398,CheXpert-v1.0/train/patient40304/study4/view1_frontal.jpg,Male,58,Frontal,AP,,,,1.0,,1.0,,,,,1.0,,,1.0 +399,CheXpert-v1.0/train/patient12823/study1/view1_frontal.jpg,Male,30,Frontal,PA,,,,1.0,1.0,0.0,0.0,,-1.0,,0.0,,, diff --git a/data/chexpert_SexBinary_PTX_final_demo_df.csv.license b/data/chexpert_SexBinary_PTX_final_demo_df.csv.license new file mode 100644 index 0000000..3cc951b --- /dev/null +++ b/data/chexpert_SexBinary_PTX_final_demo_df.csv.license @@ -0,0 +1,5 @@ +# This source file is part of the Daneshjou Lab projects +# +# SPDX-FileCopyrightText: 2025 Stanford University and the project authors (see AUTHORS.md) +# +# SPDX-License-Identifier: MIT \ No newline at end of file diff --git a/data/chexpert_SexBinary_PTX_final_test_df.csv b/data/chexpert_SexBinary_PTX_final_test_df.csv new file mode 100644 index 0000000..6dc725d --- /dev/null +++ b/data/chexpert_SexBinary_PTX_final_test_df.csv @@ -0,0 +1,101 @@ +,Path,Sex,Age,Frontal/Lateral,AP/PA,No Finding,Enlarged Cardiomediastinum,Cardiomegaly,Lung Opacity,Lung Lesion,Edema,Consolidation,Pneumonia,Atelectasis,Pneumothorax,Pleural Effusion,Pleural Other,Fracture,Support Devices +0,CheXpert-v1.0/train/patient12317/study4/view1_frontal.jpg,Female,44,Frontal,AP,,,,1.0,,,,,,1.0,1.0,,,1.0 +1,CheXpert-v1.0/train/patient06516/study10/view1_frontal.jpg,Female,62,Frontal,PA,,,,-1.0,,,,,,1.0,,,, +2,CheXpert-v1.0/train/patient42408/study1/view1_frontal.jpg,Female,51,Frontal,AP,,,,,,1.0,,,,1.0,1.0,,, +3,CheXpert-v1.0/train/patient36521/study5/view1_frontal.jpg,Female,60,Frontal,AP,,,,,,,,,,1.0,1.0,,, +4,CheXpert-v1.0/train/patient41624/study12/view1_frontal.jpg,Female,64,Frontal,AP,,,,,,,,,,1.0,1.0,,,0.0 +5,CheXpert-v1.0/train/patient10128/study2/view1_frontal.jpg,Female,63,Frontal,AP,,,,,,,,,,1.0,1.0,,,0.0 +6,CheXpert-v1.0/train/patient10892/study4/view1_frontal.jpg,Female,76,Frontal,AP,,,,-1.0,,,,,1.0,1.0,,,,1.0 +7,CheXpert-v1.0/train/patient03499/study3/view1_frontal.jpg,Female,67,Frontal,PA,,,,,,,,,,1.0,1.0,,,1.0 +8,CheXpert-v1.0/train/patient57441/study2/view1_frontal.jpg,Female,55,Frontal,AP,,,,,,,,,,1.0,,,, +9,CheXpert-v1.0/train/patient05340/study3/view1_frontal.jpg,Female,23,Frontal,AP,,,1.0,,,,,,1.0,1.0,,,, +10,CheXpert-v1.0/train/patient00467/study3/view1_frontal.jpg,Female,23,Frontal,PA,,,,1.0,,,,,,1.0,,,,1.0 +11,CheXpert-v1.0/train/patient37772/study3/view1_frontal.jpg,Female,48,Frontal,AP,,,,1.0,,,-1.0,,-1.0,1.0,1.0,,, +12,CheXpert-v1.0/train/patient29732/study34/view1_frontal.jpg,Female,42,Frontal,AP,,,,,,,,,,1.0,,,,1.0 +13,CheXpert-v1.0/train/patient62079/study1/view1_frontal.jpg,Female,70,Frontal,AP,,,,,,,,,,1.0,,,, +14,CheXpert-v1.0/train/patient35530/study4/view1_frontal.jpg,Female,48,Frontal,AP,,,,1.0,,,,,-1.0,1.0,,,,1.0 +15,CheXpert-v1.0/train/patient30991/study7/view1_frontal.jpg,Female,24,Frontal,AP,,,,1.0,,,,,,1.0,0.0,,1.0, +16,CheXpert-v1.0/train/patient29819/study5/view1_frontal.jpg,Female,50,Frontal,AP,,,,,1.0,,,,,1.0,1.0,,,1.0 +17,CheXpert-v1.0/train/patient45816/study2/view1_frontal.jpg,Female,48,Frontal,AP,,,,1.0,,,,,,1.0,,,, +18,CheXpert-v1.0/train/patient63591/study1/view2_frontal.jpg,Female,67,Frontal,AP,,,,1.0,,,,,1.0,1.0,,,, +19,CheXpert-v1.0/train/patient06516/study14/view1_frontal.jpg,Female,62,Frontal,AP,,,,1.0,,,,,,1.0,1.0,,,0.0 +20,CheXpert-v1.0/train/patient59733/study1/view1_frontal.jpg,Female,73,Frontal,AP,,,,1.0,,,,,,1.0,,,, +21,CheXpert-v1.0/train/patient37561/study1/view1_frontal.jpg,Female,61,Frontal,AP,,,,,,,,,,1.0,1.0,,,1.0 +22,CheXpert-v1.0/train/patient14094/study1/view1_frontal.jpg,Female,40,Frontal,AP,,,,1.0,,-1.0,,,-1.0,1.0,1.0,,, +23,CheXpert-v1.0/train/patient43458/study2/view1_frontal.jpg,Female,32,Frontal,AP,,0.0,0.0,,,,,,,1.0,,,,1.0 +24,CheXpert-v1.0/train/patient36646/study1/view1_frontal.jpg,Female,63,Frontal,AP,,,,,,1.0,,,,1.0,,,, +25,CheXpert-v1.0/train/patient06214/study2/view1_frontal.jpg,Female,25,Frontal,PA,,1.0,,,,,,,,0.0,0.0,,,1.0 +26,CheXpert-v1.0/train/patient06204/study6/view1_frontal.jpg,Female,48,Frontal,AP,,,,1.0,,1.0,,,1.0,,,,,1.0 +27,CheXpert-v1.0/train/patient18172/study1/view1_frontal.jpg,Female,80,Frontal,AP,1.0,,,,,,,,,0.0,,,,1.0 +28,CheXpert-v1.0/train/patient10791/study3/view1_frontal.jpg,Female,38,Frontal,AP,,-1.0,,,1.0,,0.0,,,0.0,0.0,,,1.0 +29,CheXpert-v1.0/train/patient17660/study2/view1_frontal.jpg,Female,35,Frontal,AP,,-1.0,,1.0,,,-1.0,,,0.0,,,, +30,CheXpert-v1.0/train/patient00467/study16/view1_frontal.jpg,Female,23,Frontal,AP,,,,,,,,,0.0,0.0,,,,1.0 +31,CheXpert-v1.0/train/patient34404/study5/view1_frontal.jpg,Female,72,Frontal,AP,,,,,,1.0,,,1.0,,1.0,,,1.0 +32,CheXpert-v1.0/train/patient50112/study1/view1_frontal.jpg,Female,40,Frontal,AP,,0.0,0.0,,,1.0,,,1.0,0.0,,,,1.0 +33,CheXpert-v1.0/train/patient39509/study2/view1_frontal.jpg,Female,71,Frontal,AP,,,,,,,0.0,,,0.0,0.0,,,1.0 +34,CheXpert-v1.0/train/patient26526/study11/view1_frontal.jpg,Female,61,Frontal,AP,,,,1.0,,1.0,,-1.0,,,1.0,,,1.0 +35,CheXpert-v1.0/train/patient10282/study2/view1_frontal.jpg,Female,61,Frontal,AP,,,1.0,1.0,,1.0,,-1.0,-1.0,,,,,1.0 +36,CheXpert-v1.0/train/patient40247/study27/view1_frontal.jpg,Female,56,Frontal,AP,,,,,,1.0,,,1.0,,1.0,,,1.0 +37,CheXpert-v1.0/train/patient49886/study3/view1_frontal.jpg,Female,25,Frontal,AP,,,,,,1.0,,,1.0,,1.0,,,1.0 +38,CheXpert-v1.0/train/patient39094/study2/view1_frontal.jpg,Female,66,Frontal,AP,,,1.0,1.0,,0.0,,,,,0.0,,,1.0 +39,CheXpert-v1.0/train/patient26169/study28/view1_frontal.jpg,Female,63,Frontal,AP,,,1.0,1.0,,1.0,,,,,1.0,,,1.0 +40,CheXpert-v1.0/train/patient41059/study6/view1_frontal.jpg,Female,59,Frontal,AP,,,,1.0,,,,,,,1.0,,,1.0 +41,CheXpert-v1.0/train/patient37019/study2/view1_frontal.jpg,Female,72,Frontal,AP,,-1.0,,1.0,,,,,,0.0,,1.0,,1.0 +42,CheXpert-v1.0/train/patient47326/study7/view1_frontal.jpg,Female,44,Frontal,AP,,,,,,,,,1.0,,1.0,,,1.0 +43,CheXpert-v1.0/train/patient37361/study1/view1_frontal.jpg,Female,90,Frontal,AP,,1.0,,-1.0,,1.0,,,,,,,, +44,CheXpert-v1.0/train/patient38162/study2/view1_frontal.jpg,Female,45,Frontal,AP,,,,1.0,,1.0,,,,0.0,1.0,,,1.0 +45,CheXpert-v1.0/train/patient40200/study2/view1_frontal.jpg,Female,77,Frontal,AP,,,,1.0,,,-1.0,,-1.0,,1.0,,,1.0 +46,CheXpert-v1.0/train/patient03960/study4/view1_frontal.jpg,Female,87,Frontal,AP,,,,,,,-1.0,,-1.0,,,,0.0, +47,CheXpert-v1.0/train/patient05930/study4/view1_frontal.jpg,Female,35,Frontal,AP,,,,1.0,,-1.0,,-1.0,,,,,,1.0 +48,CheXpert-v1.0/train/patient22072/study2/view1_frontal.jpg,Female,72,Frontal,AP,,1.0,,1.0,,,,,1.0,0.0,0.0,,,1.0 +49,CheXpert-v1.0/train/patient13530/study32/view1_frontal.jpg,Female,37,Frontal,AP,,,1.0,1.0,,-1.0,,,,,1.0,,,1.0 +50,CheXpert-v1.0/train/patient56187/study2/view1_frontal.jpg,Male,62,Frontal,AP,,,,,,,,,,1.0,1.0,,, +51,CheXpert-v1.0/train/patient47880/study1/view1_frontal.jpg,Male,57,Frontal,AP,,,,,,,,,,1.0,,,1.0, +52,CheXpert-v1.0/train/patient21240/study18/view1_frontal.jpg,Male,68,Frontal,AP,,,,1.0,,,,,,1.0,,,,1.0 +53,CheXpert-v1.0/train/patient05023/study3/view1_frontal.jpg,Male,27,Frontal,AP,,,,,,,,,1.0,1.0,1.0,,,0.0 +54,CheXpert-v1.0/train/patient40892/study6/view1_frontal.jpg,Male,54,Frontal,AP,,,,,,-1.0,,,,1.0,,,,1.0 +55,CheXpert-v1.0/train/patient38040/study10/view1_frontal.jpg,Male,26,Frontal,AP,,,,1.0,,,,,,1.0,,,,1.0 +56,CheXpert-v1.0/train/patient12556/study10/view1_frontal.jpg,Male,50,Frontal,AP,,,,1.0,,,,,,1.0,1.0,,, +57,CheXpert-v1.0/train/patient48267/study1/view1_frontal.jpg,Male,57,Frontal,AP,,,,,,,,,,1.0,,,,0.0 +58,CheXpert-v1.0/train/patient49504/study3/view1_frontal.jpg,Male,19,Frontal,AP,,,,,,1.0,,,,1.0,,,,0.0 +59,CheXpert-v1.0/train/patient03122/study34/view1_frontal.jpg,Male,69,Frontal,AP,,,,,,,,,,1.0,,,,1.0 +60,CheXpert-v1.0/train/patient17506/study1/view1_frontal.jpg,Male,50,Frontal,PA,,,,,,,,,1.0,1.0,1.0,,, +61,CheXpert-v1.0/train/patient13101/study3/view1_frontal.jpg,Male,70,Frontal,AP,,,1.0,1.0,,,,,,1.0,,,,0.0 +62,CheXpert-v1.0/train/patient46267/study4/view1_frontal.jpg,Male,34,Frontal,AP,,,,1.0,,,,,,1.0,1.0,,,1.0 +63,CheXpert-v1.0/train/patient04135/study30/view1_frontal.jpg,Male,43,Frontal,AP,,,0.0,,1.0,,-1.0,,-1.0,1.0,1.0,,,1.0 +64,CheXpert-v1.0/train/patient24008/study2/view1_frontal.jpg,Male,65,Frontal,PA,,,,1.0,,,,,,1.0,,,,1.0 +65,CheXpert-v1.0/train/patient23118/study5/view1_frontal.jpg,Male,79,Frontal,AP,,,,1.0,,,,,,1.0,1.0,,,1.0 +66,CheXpert-v1.0/train/patient29779/study8/view1_frontal.jpg,Male,20,Frontal,AP,,,1.0,1.0,,,,,,1.0,1.0,,,1.0 +67,CheXpert-v1.0/train/patient04083/study16/view1_frontal.jpg,Male,40,Frontal,AP,,1.0,1.0,,,,,,,1.0,,,,1.0 +68,CheXpert-v1.0/train/patient24008/study5/view1_frontal.jpg,Male,65,Frontal,PA,,,,-1.0,,,-1.0,,-1.0,1.0,1.0,,,1.0 +69,CheXpert-v1.0/train/patient45421/study1/view1_frontal.jpg,Male,62,Frontal,AP,,0.0,,1.0,,,,,,1.0,,,-1.0, +70,CheXpert-v1.0/train/patient40111/study6/view1_frontal.jpg,Male,45,Frontal,AP,,,1.0,1.0,,,,,,1.0,1.0,,,1.0 +71,CheXpert-v1.0/train/patient04951/study9/view2_frontal.jpg,Male,67,Frontal,PA,,,,-1.0,,,,,,1.0,,,,0.0 +72,CheXpert-v1.0/train/patient05474/study1/view1_frontal.jpg,Male,77,Frontal,PA,,,0.0,,,,,,,1.0,,,,1.0 +73,CheXpert-v1.0/train/patient38584/study1/view2_frontal.jpg,Male,56,Frontal,AP,,,,1.0,,,,,,1.0,1.0,1.0,1.0,1.0 +74,CheXpert-v1.0/train/patient43922/study7/view1_frontal.jpg,Male,66,Frontal,AP,,,,1.0,,,,,,1.0,1.0,,, +75,CheXpert-v1.0/train/patient08625/study4/view1_frontal.jpg,Male,64,Frontal,AP,1.0,,,,,,,,,0.0,,,,1.0 +76,CheXpert-v1.0/train/patient02937/study1/view1_frontal.jpg,Male,26,Frontal,PA,,,,,,1.0,,,,,,,,1.0 +77,CheXpert-v1.0/train/patient35485/study1/view1_frontal.jpg,Male,58,Frontal,AP,,-1.0,,,,1.0,,,1.0,0.0,,,,1.0 +78,CheXpert-v1.0/train/patient22862/study23/view1_frontal.jpg,Male,67,Frontal,PA,,,-1.0,1.0,,-1.0,-1.0,,,,1.0,,, +79,CheXpert-v1.0/train/patient40151/study7/view1_frontal.jpg,Male,70,Frontal,AP,,,,,,,1.0,,,,1.0,,, +80,CheXpert-v1.0/train/patient04031/study1/view1_frontal.jpg,Male,77,Frontal,AP,,,1.0,1.0,,,-1.0,,-1.0,,1.0,,, +81,CheXpert-v1.0/train/patient02936/study19/view1_frontal.jpg,Male,55,Frontal,AP,,,,,,1.0,,,1.0,,1.0,,,1.0 +82,CheXpert-v1.0/train/patient50001/study4/view1_frontal.jpg,Male,74,Frontal,AP,,,,1.0,,,,,1.0,,1.0,,, +83,CheXpert-v1.0/train/patient03048/study2/view1_frontal.jpg,Male,58,Frontal,PA,,-1.0,,,,0.0,,,,,0.0,,,1.0 +84,CheXpert-v1.0/train/patient12756/study2/view1_frontal.jpg,Male,51,Frontal,AP,,,,1.0,,,,,,-1.0,,,,1.0 +85,CheXpert-v1.0/train/patient32605/study1/view1_frontal.jpg,Male,51,Frontal,PA,,,,,,0.0,,,,,,,, +86,CheXpert-v1.0/train/patient14631/study3/view1_frontal.jpg,Male,83,Frontal,AP,,,,1.0,,,,,,,1.0,,, +87,CheXpert-v1.0/train/patient03072/study5/view1_frontal.jpg,Male,51,Frontal,AP,,,,,,1.0,,,,,,,,1.0 +88,CheXpert-v1.0/train/patient40851/study1/view1_frontal.jpg,Male,82,Frontal,AP,,,,1.0,,1.0,,-1.0,,,0.0,,, +89,CheXpert-v1.0/train/patient43188/study6/view1_frontal.jpg,Male,40,Frontal,AP,,1.0,,1.0,1.0,,1.0,,,0.0,,,,1.0 +90,CheXpert-v1.0/train/patient21174/study24/view1_frontal.jpg,Male,84,Frontal,AP,,,,1.0,,1.0,,,,,1.0,,,1.0 +91,CheXpert-v1.0/train/patient14979/study9/view1_frontal.jpg,Male,64,Frontal,AP,,,,1.0,,1.0,,,,,1.0,,,1.0 +92,CheXpert-v1.0/train/patient03305/study16/view1_frontal.jpg,Male,62,Frontal,AP,,,,,,1.0,,,,,,,,1.0 +93,CheXpert-v1.0/train/patient61346/study1/view1_frontal.jpg,Male,57,Frontal,AP,,1.0,,,,1.0,,,,0.0,,,, +94,CheXpert-v1.0/train/patient31593/study2/view1_frontal.jpg,Male,45,Frontal,AP,,,,,,1.0,,,,,,,,1.0 +95,CheXpert-v1.0/train/patient11550/study1/view1_frontal.jpg,Male,54,Frontal,PA,,,1.0,,,,,,,,0.0,,, +96,CheXpert-v1.0/train/patient41461/study3/view1_frontal.jpg,Male,69,Frontal,AP,,,,1.0,,,,,,,1.0,,, +97,CheXpert-v1.0/train/patient48805/study5/view1_frontal.jpg,Male,76,Frontal,AP,,,,1.0,,,,,,,1.0,,,1.0 +98,CheXpert-v1.0/train/patient27189/study7/view1_frontal.jpg,Male,51,Frontal,AP,,,,1.0,,-1.0,,-1.0,,,,,, +99,CheXpert-v1.0/train/patient20435/study3/view1_frontal.jpg,Male,61,Frontal,AP,,,,,,,,,1.0,,1.0,,,1.0 diff --git a/data/chexpert_SexBinary_PTX_final_test_df.csv.license b/data/chexpert_SexBinary_PTX_final_test_df.csv.license new file mode 100644 index 0000000..3cc951b --- /dev/null +++ b/data/chexpert_SexBinary_PTX_final_test_df.csv.license @@ -0,0 +1,5 @@ +# This source file is part of the Daneshjou Lab projects +# +# SPDX-FileCopyrightText: 2025 Stanford University and the project authors (see AUTHORS.md) +# +# SPDX-License-Identifier: MIT \ No newline at end of file diff --git a/data/ddi_demo_metadata.csv b/data/ddi_demo_metadata.csv new file mode 100644 index 0000000..88ad87a --- /dev/null +++ b/data/ddi_demo_metadata.csv @@ -0,0 +1,312 @@ +,DDI_ID,DDI_file,skin_tone,malignant,disease +160,161,000161.png,56,False,seborrheic-keratosis-irritated +151,152,000152.png,56,False,melanocytic-nevi +245,246,000246.png,56,False,neurofibroma +178,179,000179.png,56,False,dermatofibroma +103,104,000104.png,56,False,seborrheic-keratosis +201,202,000202.png,56,False,acrochordon +247,248,000248.png,56,False,epidermal-cyst +229,230,000230.png,56,False,pyogenic-granuloma +181,182,000182.png,56,False,melanocytic-nevi +135,136,000136.png,56,False,eccrine-poroma +114,115,000115.png,56,False,verruca-vulgaris +31,32,000032.png,56,True,mycosis-fungoides +197,198,000198.png,56,False,nevus-lipomatosus-superficialis +106,107,000107.png,56,False,inverted-follicular-keratosis +180,181,000181.png,56,False,blue-nevus +154,155,000155.png,56,False,melanocytic-nevi +60,61,000061.png,56,False,acrochordon +78,79,000079.png,56,False,melanocytic-nevi +59,60,000060.png,56,False,acrochordon +164,165,000165.png,56,True,metastatic-carcinoma +148,149,000149.png,56,False,hematoma +238,239,000239.png,56,False,epidermal-cyst +22,23,000023.png,56,True,mycosis-fungoides +210,211,000211.png,56,False,epidermal-cyst +67,68,000068.png,56,True,mycosis-fungoides +211,212,000212.png,56,False,acne-cystic +157,158,000158.png,56,True,squamous-cell-carcinoma-in-situ +219,220,000220.png,56,False,condyloma-accuminatum +224,225,000225.png,56,False,dermatofibroma +64,65,000065.png,56,True,mycosis-fungoides +26,27,000027.png,56,True,mycosis-fungoides +142,143,000143.png,56,False,verruca-vulgaris +195,196,000196.png,56,False,verruca-vulgaris +168,169,000169.png,56,True,squamous-cell-carcinoma +132,133,000133.png,56,False,acrochordon +105,106,000106.png,56,False,hyperpigmentation +237,238,000238.png,56,False,melanocytic-nevi +28,29,000029.png,56,True,mycosis-fungoides +174,175,000175.png,56,False,dermatofibroma +139,140,000140.png,56,False,acrochordon +220,221,000221.png,56,False,epidermal-cyst +111,112,000112.png,56,False,dermatofibroma +159,160,000160.png,56,False,seborrheic-keratosis +77,78,000078.png,56,False,epidermal-nevus +123,124,000124.png,56,False,epidermal-cyst +124,125,000125.png,56,True,kaposi-sarcoma +119,120,000120.png,56,False,melanocytic-nevi +131,132,000132.png,56,False,eccrine-poroma +199,200,000200.png,56,False,pyogenic-granuloma +130,131,000131.png,56,False,verruca-vulgaris +216,217,000217.png,56,False,eczema-spongiotic-dermatitis +231,232,000232.png,56,False,eczema-spongiotic-dermatitis +110,111,000111.png,56,False,seborrheic-keratosis +6,7,000007.png,56,True,melanoma-acral-lentiginous +102,103,000103.png,56,False,epidermal-cyst +75,76,000076.png,56,False,verruca-vulgaris +55,56,000056.png,56,False,lipoma +128,129,000129.png,56,False,seborrheic-keratosis +82,83,000083.png,56,False,epidermal-cyst +226,227,000227.png,56,False,melanocytic-nevi +95,96,000096.png,56,False,pyogenic-granuloma +206,207,000207.png,56,False,melanocytic-nevi +173,174,000174.png,56,False,seborrheic-keratosis +158,159,000159.png,56,False,melanocytic-nevi +34,35,000035.png,56,True,mycosis-fungoides +24,25,000025.png,56,True,subcutaneous-t-cell-lymphoma +20,21,000021.png,56,True,mycosis-fungoides +58,59,000059.png,56,False,verruca-vulgaris +144,145,000145.png,56,False,melanocytic-nevi +134,135,000135.png,56,False,angioleiomyoma +129,130,000130.png,56,False,benign-keratosis +87,88,000088.png,56,False,melanocytic-nevi +205,206,000206.png,56,False,melanocytic-nevi +227,228,000228.png,56,False,morphea +209,210,000210.png,56,False,epidermal-cyst +133,134,000134.png,56,False,acrochordon +146,147,000147.png,56,False,acrochordon +149,150,000150.png,56,False,epidermal-cyst +21,22,000022.png,56,True,mycosis-fungoides +92,93,000093.png,56,False,neurofibroma +239,240,000240.png,56,False,inverted-follicular-keratosis +57,58,000058.png,56,False,blue-nevus +250,251,000251.png,56,False,verruca-vulgaris +222,223,000223.png,56,False,verruca-vulgaris +233,234,000234.png,56,False,lipoma +30,31,000031.png,56,True,mycosis-fungoides +118,119,000119.png,56,False,prurigo-nodularis +94,95,000095.png,56,False,nevus-lipomatosus-superficialis +112,113,000113.png,56,False,trichofolliculoma +213,214,000214.png,56,False,neurofibroma +127,128,000128.png,56,False,scar +100,101,000101.png,56,False,melanocytic-nevi +208,209,000209.png,56,False,verruca-vulgaris +192,193,000193.png,56,False,verruca-vulgaris +202,203,000203.png,56,False,seborrheic-keratosis +234,235,000235.png,56,False,epidermal-cyst +23,24,000024.png,56,True,mycosis-fungoides +162,163,000163.png,56,False,neurofibroma +3,4,000004.png,56,True,squamous-cell-carcinoma-in-situ +68,69,000069.png,56,True,mycosis-fungoides +74,75,000075.png,56,False,epidermal-cyst +200,201,000201.png,56,False,seborrheic-keratosis +62,63,000063.png,56,False,wart +54,55,000055.png,56,False,hyperpigmentation +248,249,000249.png,56,False,abrasions-ulcerations-and-physical-injuries +108,109,000109.png,56,False,melanocytic-nevi +29,30,000030.png,56,True,mycosis-fungoides +177,178,000178.png,56,False,seborrheic-keratosis +176,177,000177.png,56,False,verruca-vulgaris +145,146,000146.png,56,False,acrochordon +143,144,000144.png,56,False,epidermal-cyst +165,166,000166.png,56,True,squamous-cell-carcinoma-in-situ +184,185,000185.png,56,False,eccrine-poroma +104,105,000105.png,56,False,seborrheic-keratosis +169,170,000170.png,56,False,pyogenic-granuloma +96,97,000097.png,56,False,benign-keratosis +16,17,000017.png,56,True,mycosis-fungoides 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\ No newline at end of file diff --git a/data/ddi_test_metadata.csv b/data/ddi_test_metadata.csv new file mode 100644 index 0000000..43c6b51 --- /dev/null +++ b/data/ddi_test_metadata.csv @@ -0,0 +1,105 @@ +,DDI_ID,DDI_file,skin_tone,malignant,disease +14,15,000015.png,56,True,squamous-cell-carcinoma +161,162,000162.png,56,True,melanoma +88,89,000089.png,56,False,verruca-vulgaris +175,176,000176.png,56,False,acrochordon +152,153,000153.png,56,False,seborrheic-keratosis +230,231,000231.png,56,False,verruca-vulgaris +13,14,000014.png,56,True,squamous-cell-carcinoma +249,250,000250.png,56,False,neurofibroma +228,229,000229.png,56,False,epidermal-cyst +185,186,000186.png,56,False,eccrine-poroma +203,204,000204.png,56,False,verruca-vulgaris +163,164,000164.png,56,False,angioma +244,245,000245.png,56,False,molluscum-contagiosum +52,53,000053.png,56,False,seborrheic-keratosis-irritated +90,91,000091.png,56,False,seborrheic-keratosis +235,236,000236.png,56,False,neuroma +218,219,000219.png,56,False,molluscum-contagiosum +120,121,000121.png,56,False,melanocytic-nevi +76,77,000077.png,56,False,acquired-digital-fibrokeratoma +137,138,000138.png,56,False,eccrine-poroma +5,6,000006.png,56,True,squamous-cell-carcinoma +136,137,000137.png,56,False,eccrine-poroma +113,114,000114.png,56,False,seborrheic-keratosis +189,190,000190.png,56,False,dermatofibroma +186,187,000187.png,56,False,seborrheic-keratosis-irritated +204,205,000205.png,56,False,melanocytic-nevi +179,180,000180.png,56,False,seborrheic-keratosis +66,67,000067.png,56,False,abrasions-ulcerations-and-physical-injuries +155,156,000156.png,56,False,epidermal-cyst +0,1,000001.png,56,True,melanoma-in-situ +196,197,000197.png,56,False,melanocytic-nevi +12,13,000013.png,56,True,melanoma-acral-lentiginous +33,34,000034.png,56,True,mycosis-fungoides +2,3,000003.png,56,True,mycosis-fungoides +232,233,000233.png,56,False,verruca-vulgaris +225,226,000226.png,56,False,seborrheic-keratosis 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+426,427,000427.png,12,False,seborrheic-keratosis +298,299,000299.png,12,True,squamous-cell-carcinoma-in-situ +350,351,000351.png,12,False,verruca-vulgaris +402,403,000403.png,12,False,angioma +47,48,000048.png,12,False,melanocytic-nevi +46,47,000047.png,12,False,melanocytic-nevi +411,412,000412.png,12,False,epidermal-cyst +407,408,000408.png,12,True,squamous-cell-carcinoma-in-situ +355,356,000356.png,12,False,epidermal-cyst +378,379,000379.png,12,False,nevus-lipomatosus-superficialis +373,374,000374.png,12,False,neurofibroma +286,287,000287.png,12,False,melanocytic-nevi +379,380,000380.png,12,False,syringocystadenoma-papilliferum +374,375,000375.png,12,False,nevus-lipomatosus-superficialis +323,324,000324.png,12,False,melanocytic-nevi +389,390,000390.png,12,False,eccrine-poroma +282,283,000283.png,12,False,melanocytic-nevi +405,406,000406.png,12,True,basal-cell-carcinoma-nodular +39,40,000040.png,12,True,squamous-cell-carcinoma-in-situ +366,367,000367.png,12,True,mycosis-fungoides +434,435,000435.png,12,False,seborrheic-keratosis +382,383,000383.png,12,False,inverted-follicular-keratosis +256,257,000257.png,12,False,verruca-vulgaris +347,348,000348.png,12,False,verruca-vulgaris +270,271,000271.png,12,False,melanocytic-nevi +427,428,000428.png,12,False,epidermal-cyst +386,387,000387.png,12,True,kaposi-sarcoma +4,5,000005.png,12,True,basal-cell-carcinoma +418,419,000419.png,12,False,seborrheic-keratosis +45,46,000046.png,12,False,melanocytic-nevi +258,259,000259.png,12,False,seborrheic-keratosis-irritated +37,38,000038.png,12,True,squamous-cell-carcinoma-keratoacanthoma +423,424,000424.png,12,False,seborrheic-keratosis-irritated +344,345,000345.png,12,False,verruca-vulgaris +416,417,000417.png,12,False,seborrheic-keratosis +271,272,000272.png,12,False,melanocytic-nevi +417,418,000418.png,12,False,seborrheic-keratosis +352,353,000353.png,12,False,verruca-vulgaris +390,391,000391.png,12,False,eccrine-poroma +324,325,000325.png,12,False,epidermal-cyst +334,335,000335.png,12,False,dermatofibroma +288,289,000289.png,12,True,basal-cell-carcinoma +326,327,000327.png,12,False,epidermal-cyst +409,410,000410.png,12,True,squamous-cell-carcinoma-in-situ +421,422,000422.png,12,False,molluscum-contagiosum +276,277,000277.png,12,False,melanocytic-nevi +42,43,000043.png,12,True,melanoma-acral-lentiginous +356,357,000357.png,12,False,seborrheic-keratosis diff --git a/data/ddi_test_metadata.csv.license b/data/ddi_test_metadata.csv.license new file mode 100644 index 0000000..3cc951b --- /dev/null +++ b/data/ddi_test_metadata.csv.license @@ -0,0 +1,5 @@ +# This source file is part of the Daneshjou Lab projects +# +# SPDX-FileCopyrightText: 2025 Stanford University and the project authors (see AUTHORS.md) +# +# SPDX-License-Identifier: MIT \ No newline at end of file diff --git a/ddi_attribute_pred.py b/ddi_attribute_pred.py new file mode 100644 index 0000000..37edd3c --- /dev/null +++ b/ddi_attribute_pred.py @@ -0,0 +1,286 @@ +# This source file is part of the Daneshjou Lab projects +# +# SPDX-FileCopyrightText: 2025 Stanford University and the project authors (see AUTHORS.md) +# +# SPDX-License-Identifier: MIT +# + +# Standard library imports +import os +import pickle +import random +import traceback + +# Third-party imports +import numpy as np +import pandas as pd +from tqdm import tqdm + +# Local application imports +from LMM import GPT4VAPI, ClaudeAPI, GeminiAPI + +rare_diseases = { + "subcutaneous-t-cell-lymphoma", + "focal-acral-hyperkeratosis", + "eccrine-poroma", + "inverted-follicular-keratosis", + "kaposi-sarcoma", + "metastatic-carcinoma", + "mycosis-fungoides", + "acquired-digital-fibrokeratoma", + "atypical-spindle-cell-nevus-of-reed", + "verruciform-xanthoma", + "morphea", + "nevus-lipomatosus-superficialis", + "pigmented-spindle-cell-nevus-of-reed", + "arteriovenous-hemangioma", + "syringocystadenoma-papilliferum", + "trichofolliculoma", + "coccidioidomycosis", + "leukemia-cutis", + "sebaceous-carcinoma", + "blastic-plasmacytoid-dendritic-cell-neoplasm", + "glomangioma", + "dermatomyositis", + "cellular-neurothekeoma", + "graft-vs-host-disease", + "xanthograngioma", + "chondroid-syringoma", + "angioleiomyoma", +} + + +def create_demo(fst12, fst56, filter_rare=False, random_seed=141): + dataset_name = "DDI" + demo_frame = pd.read_csv( + f"/home/groups/roxanad/sonnet/icl/ManyICL/ManyICL/dataset/{dataset_name}/ddi_demo_metadata.csv", + index_col=0, + ) + if filter_rare: + demo_frame = demo_frame[~demo_frame.disease.isin(rare_diseases)] + + total_samples = fst12 + fst56 + + fst56_frame = demo_frame[demo_frame.skin_tone == 56] + if len(fst56_frame) < fst56: + print( + f"Warning: not enough samples for skin tone 56, taking the max available {len(fst56_frame)}" + ) + fst56_frame = fst56_frame.sample(len(fst56_frame), random_state=random_seed) + else: + fst56_frame = fst56_frame.sample(fst56, random_state=random_seed) + + fst12_frame = demo_frame[demo_frame.skin_tone == 12] + if len(fst12_frame) < fst12: + print( + f"Warning: not enough samples for skin tone 12, taking the max available {len(fst12_frame)}" + ) + fst12_frame = fst12_frame.sample(len(fst12_frame), random_state=random_seed) + else: + fst12_frame = fst12_frame.sample(fst12, random_state=random_seed) + + final_demo_frame = pd.concat([fst56_frame, fst12_frame]) + + if len(final_demo_frame) < total_samples: + print( + f"Warning: not enough total samples, taking the max available {len(final_demo_frame)}" + ) + final_demo_frame = final_demo_frame.sample( + len(final_demo_frame), random_state=random_seed + ) + else: + final_demo_frame = final_demo_frame.sample( + total_samples, random_state=random_seed + ) # sample full num to shuffle + return final_demo_frame + + +def main( + model, + fst12, + fst56, + num_qns_per_round, + filter_rare=False, + detail="auto", + random_seed=141, +): + + # class_to_idx = {class_name: idx for idx, class_name in enumerate(classes)} + EXP_NAME = f"ddi_fst_{fst12}_{fst56}_{model}_{num_qns_per_round}" + + demo_frame = create_demo(fst12, fst56, random_seed=random_seed) + + dataset_name = "DDI" + test_df = pd.read_csv( + f"/home/groups/roxanad/sonnet/icl/ManyICL/ManyICL/dataset/{dataset_name}/ddi_test_metadata.csv", + index_col=0, + ) + if filter_rare: + test_df = test_df[~test_df.disease.isin(rare_diseases)] + + if model.startswith("gpt") or model.startswith("o1"): + api = GPT4VAPI(model=model, detail=detail) + elif model.startswith("Gemini"): + assert model == "Gemini1.5" + api = GeminiAPI() + elif model.startswith("claude"): + api = ClaudeAPI() + print(EXP_NAME, f"test size = {len(test_df)}") + + # create demo_examples from my demo_frame + # list of tuples + # i[0] = (path_to_image, class name) + demo_paths = [] + demo_labels = [] + for i, row in demo_frame.iterrows(): + ans_choice = "B" if row.skin_tone == 56 else "A" + demo_paths.append("/home/groups/roxanad/ddi/" + row.DDI_file) + demo_labels.append(ans_choice) + demo_examples = list(zip(demo_paths, demo_labels)) + + # Load existing results + if os.path.isfile(f"{EXP_NAME}.pkl"): + with open(f"{EXP_NAME}.pkl", "rb") as f: + results = pickle.load(f) + else: + results = {} + + test_df = test_df.sample(frac=1, random_state=random_seed) # Shuffle the test set + for start_idx in tqdm(range(0, len(test_df), num_qns_per_round), desc=EXP_NAME): + end_idx = min(len(test_df), start_idx + num_qns_per_round) + + # random.shuffle(demo_examples) already shuffled so don't need to do this + prompt = "Please respond with the following format for each question, in the form of a single capital letter specifying which label best describes the image. Do not deviate from the format, because it will be automatically parsed." + image_paths = [i[0] for i in demo_examples] + for demo in demo_examples: + prompt += f"""<>Given the image above, answer the following question using the specified format. +Question: What skin tone does the person in the image have? +Choices: A. White, B. Black or Brown +Answer Choice: {demo[1]} +""" + qns_idx = [] + for idx, i in enumerate(test_df.iloc[start_idx:end_idx].itertuples()): + qns_idx.append(i.Index) + image_paths.append("/home/groups/roxanad/ddi/" + i.DDI_file) + qn_idx = idx + 1 + + prompt += f"""<>Given the image above, answer the following question using the specified format. +Question {qn_idx}: What skin tone does the person in the image have? +Choices {qn_idx}: A. White, B. Black or Brown + +""" + for i in range(start_idx, end_idx): + qn_idx = i - start_idx + 1 + prompt += f"""Please respond with the following format for each question: +---BEGIN FORMAT TEMPLATE FOR QUESTION {qn_idx}--- +Answer Choice {qn_idx}: [Your Answer Choice Here for Question {qn_idx}] +Confidence Score {qn_idx}: [Your Numerical Prediction Confidence Score Here From 0 To 1 for Question {qn_idx}] +---END FORMAT TEMPLATE FOR QUESTION {qn_idx}--- + +Do not deviate from the above format. Repeat the format template for the answer.""" + qns_id = str(qns_idx) + for retry in range(3): + if ( + (qns_id in results) + and (not results[qns_id][0].startswith("ERROR")) + and ( + f"END FORMAT TEMPLATE FOR QUESTION {end_idx-start_idx}" + in results[qns_id][0] + ) + ): # Skip if results exist and successful + continue + + try: + for retry in range(3): + if ( + (qns_id in results) + and (not results[qns_id][0].startswith("ERROR")) + and ( + f"END FORMAT TEMPLATE FOR QUESTION {end_idx-start_idx}" + in results[qns_id][0] + ) + ): # Skip if results exist and successful + continue + + try: + res = api( + prompt, + image_paths=image_paths, + real_call=True, + max_tokens=60 * num_qns_per_round, + ) + + except Exception as e: + res = f"ERROR!!!! {traceback.format_exc()}" + except KeyboardInterrupt: + previous_usage = results.get("token_usage", (0, 0, 0)) + total_usage = tuple( + a + b for a, b in zip(previous_usage, api.token_usage) + ) + results["token_usage"] = total_usage + with open(f"{EXP_NAME}.pkl", "wb") as f: + pickle.dump(results, f) + exit() + except Exception as e: + res = f"ERROR!!!! {traceback.format_exc()}" + except KeyboardInterrupt: + previous_usage = results.get("token_usage", (0, 0, 0)) + total_usage = tuple( + a + b for a, b in zip(previous_usage, api.token_usage) + ) + results["token_usage"] = total_usage + with open(f"{EXP_NAME}.pkl", "wb") as f: + pickle.dump(results, f) + exit() + + print(res) + results[qns_id] = (res, prompt, image_paths) + + # Update token usage and save the results + previous_usage = results.get("token_usage", (0, 0, 0)) + total_usage = tuple(a + b for a, b in zip(previous_usage, api.token_usage)) + results["token_usage"] = total_usage + with open(f"./ddi_results/{EXP_NAME}.pkl", "wb") as f: + pickle.dump(results, f) + + +if __name__ == "__main__": + + # for i in range(0, 40, 8): + # # main("gpt-4o-2024-05-13", + # # i, + # # i, + # # 50,) + + # # main("Gemini1.5", + # # i, i, + # # 50,) + + # main("claude", + # i, i, + # 50,) + + # main("gpt-4o-2024-05-13", + # 0, + # 0, + # 50,) + + # main("Gemini1.5", + # 0, 0, + # 50,) + + # main("claude", + # 0, 0, + # 50,) + + for model in ["Gemini1.5", "gpt-4o-2024-05-13", "claude"]: + for seed in [10, 100, 141]: + for num_malignant in [ + 1, + 5, + 10, + 15, + 20, + 30, + ]: + main(model, num_malignant, num_malignant, 50, random_seed=seed) diff --git a/requirements.txt b/requirements.txt index a11ef2f..6ca9968 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,2 +1,51 @@ -pandas -numpy \ No newline at end of file +asttokens>=2.1.0 +colorama==0.4.4 +comm==0.1.4 +contourpy==1.3.0 +cycler==0.12.1 +debugpy==1.6.7 +decorator==5.1.1 +exceptiongroup==1.1.3 +executing==2.1.0 +fonttools==4.54.1 +importlib_metadata==6.8.0 +ipykernel>=6.29.3,<7.0.0 +ipython>8.15,<8.17 +jedi==0.19.0 +joblib==1.4.2 +jupyter_client>=7.4.4,<9.0.0 +jupyter_core>=5.0.0,<6.0.0 +kiwisolver==1.4.7 +matplotlib==3.9.2 +matplotlib-inline==0.1.2 +nest-asyncio==1.5.1 +numpy>=1.22,<2.0 +packaging>=16.8,<24 +pandas==2.2.3 +parso==0.8.3 +pickleshare==0.7.5 +pillow<11,>=7.1.0 +platformdirs>=3.10.0,<5.0.0 +prompt-toolkit>=3.0.0,<4.0.0 +psutil==5.8.0 +pure_eval==0.2.2 +Pygments>=2.14,<3.0.0 +pyparsing==3.2.0 +python-dateutil==2.8.2 +pytz==2024.2 +# pywin32==302.0.0; sys_platform == "win32" +pyzmq>=25.0.0,<26.0.0 +scikit-learn==1.5.2 +scipy==1.14.1 +setuptools==75.1.0 +six==1.16.0 +stack-data>=0.6.0,<0.7.0 +threadpoolctl==3.5.0 +tornado>=6.2.0 +traitlets>=5.10.0,<6.0.0 +typing-extensions>=4.6.1 +tzdata==2024.2 +wcwidth==0.2.5 +wheel==0.44.0 +zipp==3.7.0 +tqdm==4.62.3 \ No newline at end of file diff --git a/run_chexpert.py b/run_chexpert.py new file mode 100644 index 0000000..6e592a1 --- /dev/null +++ b/run_chexpert.py @@ -0,0 +1,357 @@ +# This source file is part of the Daneshjou Lab projects +# +# SPDX-FileCopyrightText: 2025 Stanford University and the project authors (see AUTHORS.md) +# +# SPDX-License-Identifier: MIT +# + + +# Standard library imports +import os +import pickle +import random +import time +import traceback + +# Third-party imports +import numpy as np +import pandas as pd +from tqdm import tqdm +from LMM import GPT4VAPI, ClaudeAPI, GeminiAPI + + +def create_demo(female_ben, female_mal, male_ben, male_mal, random_seed=141): + ### + ### Load demo example frame + ### Choose relevant demo examples + ### Then create demo prompt and list of demo image paths + ### + demo_frame = pd.read_csv( + f"/home/groups/roxanad/sonnet/icl/ManyICL/ManyICL/dataset/chexpert/chexpert_SexBinary_PTX_final_demo_df.csv", + index_col=0, + ) + total_samples = female_ben + female_mal + male_ben + male_mal + + female_frame = demo_frame[demo_frame.Sex == "Female"] + if len(female_frame[female_frame.Pneumothorax == True]) < female_mal: + print( + f"Warning: not enough female malignant samples, taking the max available {len(female_frame[female_frame.Pneumothorax == True])}" + ) + female_mal_frame = female_frame[female_frame.Pneumothorax == True].sample( + len(female_frame[female_frame.Pneumothorax == True]), + random_state=random_seed, + ) + else: + female_mal_frame = female_frame[female_frame.Pneumothorax == True].sample( + female_mal, random_state=random_seed + ) + + if len(female_frame[female_frame.Pneumothorax == False]) < female_ben: + print( + f"Warning: not enough female benign samples, taking the max available {len(female_frame[female_frame.Pneumothorax == False])}" + ) + female_ben_frame = female_frame[female_frame.Pneumothorax == False].sample( + len(female_frame[female_frame.Pneumothorax == False]), + random_state=random_seed, + ) + else: + female_ben_frame = female_frame[female_frame.Pneumothorax == False].sample( + female_ben, random_state=random_seed + ) + + male_frame = demo_frame[demo_frame.Sex == "Male"] + if len(male_frame[male_frame.Pneumothorax == True]) < male_mal: + print( + f"Warning: not enough male malignant samples, taking the max available {len(male_frame[male_frame.Pneumothorax == True])}" + ) + male_mal_frame = male_frame[male_frame.Pneumothorax == True].sample( + len(male_frame[male_frame.Pneumothorax == True]), random_state=random_seed + ) + else: + male_mal_frame = male_frame[male_frame.Pneumothorax == True].sample( + male_mal, random_state=random_seed + ) + + if len(male_frame[male_frame.Pneumothorax == False]) < male_ben: + print( + f"Warning: not enough male benign samples, taking the max available {len(male_frame[male_frame.Pneumothorax == False])}" + ) + male_ben_frame = male_frame[male_frame.Pneumothorax == False].sample( + len(male_frame[male_frame.Pneumothorax == False]), random_state=random_seed + ) + else: + male_ben_frame = male_frame[male_frame.Pneumothorax == False].sample( + male_ben, random_state=random_seed + ) + + total_samples = ( + len(female_mal_frame) + + len(female_ben_frame) + + len(male_mal_frame) + + len(male_ben_frame) + ) + final_demo_frame = pd.concat( + [female_mal_frame, female_ben_frame, male_mal_frame, male_ben_frame] + ).sample( + total_samples, random_state=random_seed + ) # sample full num to shuffle + return final_demo_frame + + +def main( + model, + female_ben, + female_mal, + male_ben, + male_mal, + num_qns_per_round, + detail="auto", + random_seed=141, +): + + EXP_NAME = f"chexpert_{random_seed}_{female_ben}_{female_mal}_{male_ben}_{male_mal}_{model}_{num_qns_per_round}" + + demo_frame = create_demo( + female_ben, female_mal, male_ben, male_mal, random_seed=random_seed + ) + + test_df = pd.read_csv( + f"/home/groups/roxanad/sonnet/icl/ManyICL/ManyICL/dataset/chexpert/chexpert_SexBinary_PTX_final_test_df.csv", + index_col=0, + ) + + if model.startswith("gpt") or model.startswith("o1"): + api = GPT4VAPI(model=model, detail=detail) + elif model.startswith("Gemini"): + assert model == "Gemini1.5" + api = GeminiAPI() + elif model.startswith("claude"): + api = ClaudeAPI() + print(EXP_NAME, f"test size = {len(test_df)}") + + # create demo_examples from my demo_frame + # list of tuples + demo_paths = [] + demo_labels = [] + for i, row in demo_frame.iterrows(): + ans_choice = "A" if row.Pneumothorax == True else "B" + demo_paths.append( + "/home/groups/roxanad/sonnet/icl/ManyICL/ManyICL/dataset/chexpert/biasICL/ManyICL/ManyICL/" + + row.Path + ) + demo_labels.append(ans_choice) + demo_examples = list(zip(demo_paths, demo_labels)) + + # Load existing results + if os.path.isfile(f"{EXP_NAME}.pkl"): + with open(f"{EXP_NAME}.pkl", "rb") as f: + results = pickle.load(f) + else: + results = {} + + test_df = test_df.sample(frac=1, random_state=random_seed) # Shuffle the test set + for start_idx in tqdm(range(0, len(test_df), num_qns_per_round), desc=EXP_NAME): + end_idx = min(len(test_df), start_idx + num_qns_per_round) + + prompt = "Please respond with the following format for each question, in the form of a single capital letter specifying which label best describes the image. Do not deviate from the format, because it will be automatically parsed." + image_paths = [i[0] for i in demo_examples] + for demo in demo_examples: + prompt += f"""<>Given the image above, answer the following question using the specified format. +Question: Does the chest x-ray above show a Pneumothorax? +Choices: A. Yes, B. No +Answer Choice: {demo[1]} +""" + qns_idx = [] + for idx, row in enumerate(test_df.iloc[start_idx:end_idx].itertuples()): + qns_idx.append(row.Index) + image_paths.append( + "/home/groups/roxanad/sonnet/icl/ManyICL/ManyICL/dataset/chexpert/biasICL/ManyICL/ManyICL/" + + row.Path + ) + qn_idx = idx + 1 + + prompt += f"""<>Given the image above, answer the following question using the specified format. +Question {qn_idx}: Does the chest x-ray above show a Pneumothorax? +Choices {qn_idx}: A. Yes, B. No +""" + for i in range(start_idx, end_idx): + qn_idx = i - start_idx + 1 + prompt += f"""Please respond with the following format for each question: +---BEGIN FORMAT TEMPLATE FOR QUESTION {qn_idx}--- +Answer Choice {qn_idx}: [Your Answer Choice Here for Question {qn_idx}] +Confidence Score {qn_idx}: [Your Numerical Prediction Confidence Score Here From 0 To 1 for Question {qn_idx}] +---END FORMAT TEMPLATE FOR QUESTION {qn_idx}--- + +Do not deviate from the above format. Repeat the format template for the answer.""" + qns_id = str(qns_idx) + for retry in range(3): + if ( + (qns_id in results) + and (not results[qns_id][0].startswith("ERROR")) + and ( + f"END FORMAT TEMPLATE FOR QUESTION {end_idx-start_idx}" + in results[qns_id][0] + ) + ): # Skip if results exist and successful + continue + + try: + for retry in range(3): + if ( + (qns_id in results) + and (not results[qns_id][0].startswith("ERROR")) + and ( + f"END FORMAT TEMPLATE FOR QUESTION {end_idx-start_idx}" + in results[qns_id][0] + ) + ): # Skip if results exist and successful + continue + + try: + res = api( + prompt, + image_paths=image_paths, + real_call=True, + max_tokens=60 * num_qns_per_round, + ) + + except Exception as e: + print(e) + print(traceback.format_exc()) + time.sleep(10) + continue + + except Exception as e: + res = f"ERROR!!!! {traceback.format_exc()}" + except KeyboardInterrupt: + previous_usage = results.get("token_usage", (0, 0, 0)) + total_usage = tuple( + a + b for a, b in zip(previous_usage, api.token_usage) + ) + results["token_usage"] = total_usage + with open(f"{EXP_NAME}.pkl", "wb") as f: + pickle.dump(results, f) + exit() + + if not res or "ERROR" in res: + res = None + else: + print(res) + results[qns_id] = (res, prompt, image_paths) + + # Update token usage and save the results + previous_usage = results.get("token_usage", (0, 0, 0)) + total_usage = tuple(a + b for a, b in zip(previous_usage, api.token_usage)) + results["token_usage"] = total_usage + with open( + f"/home/groups/roxanad/sonnet/icl/ManyICL/ManyICL/chexpert_results_br/{EXP_NAME}.pkl", + "wb", + ) as f: + pickle.dump(results, f) + + +if __name__ == "__main__": + # for model in ["Gemini1.5", "gpt-4o-2024-05-13", "claude"]: + for model in ["claude"]: + # main(model, + # 12, + # 12, + # 12, + # 12, + # 50, + # random_seed=100) + for seed in [141, 10, 100]: + # main(model, + # 0, + # 0, + # 0, + # 0, + # 50, + # random_seed=seed) + + # main(model, + # 40, 0, 40, 0, 50, random_seed=seed) + + # main(model, + # 30, 10, 30, 10, 50, random_seed=seed) + + # main(model, + # 20, 20, 20, 20, 50, random_seed=seed) + + # main(model, + # 10, 30, 10, 30, 50, random_seed=seed) + + # main(model, + # 0, 40, 0, 40, 50, random_seed=seed) + + # inverted base rate + + # main(model, + # 0, 40, 40, 0, 50, random_seed=seed) + + # main(model, + # 10, 30, 30, 10, 50, random_seed=seed) + + # main(model, + # 30, 10, 10, 30, 50, random_seed=seed) + + # main(model, + # 40, 0, 0, 40, 50, random_seed=seed) + + # claude + + # main(model, + # 25, 0, 25, 0, 50, random_seed=seed) + + # main(model, + # 20, 5, 20, 5, 50, random_seed=seed) + + # main(model, + # 15, 10, 15, 10, 50, random_seed=seed) + + # main(model, + # 10, 15, 10, 15, 50, random_seed=seed) + + # main(model, + # 0, 25, 0, 25, 50, random_seed=seed) + + # inverted base rate + + main(model, 0, 25, 25, 0, 50, random_seed=seed) + + main(model, 5, 20, 20, 5, 50, random_seed=seed) + + main(model, 10, 15, 15, 10, 50, random_seed=seed) + + main(model, 12, 12, 12, 12, 50, random_seed=seed) + + main(model, 15, 10, 10, 15, 50, random_seed=seed) + + main(model, 20, 5, 5, 20, 50, random_seed=seed) + + main(model, 25, 0, 0, 25, 50, random_seed=seed) + + # for num_malignant in [1, 5, 10, 12]: + # main(model, + # num_malignant, + # num_malignant, + # 0, + # 0, + # 50, + # random_seed=seed) + + # main(model, + # 0, + # 0, + # num_malignant, + # num_malignant, + # 50, + # random_seed=seed) + + # main(model, + # num_malignant, + # num_malignant, + # num_malignant, + # num_malignant, + # 50, + # random_seed=seed) diff --git a/run_chexpert_pred_attribute.py b/run_chexpert_pred_attribute.py new file mode 100644 index 0000000..71d7cf2 --- /dev/null +++ b/run_chexpert_pred_attribute.py @@ -0,0 +1,182 @@ +# This source file is part of the Daneshjou Lab projects +# +# SPDX-FileCopyrightText: 2025 Stanford University and the project authors (see AUTHORS.md) +# +# SPDX-License-Identifier: MIT +# + +# Standard library imports +import traceback +import os +import random +import pickle +import numpy as np + +# Third-party imports +from tqdm import tqdm +import pandas as pd + +# Local application imports +from LMM import GPT4VAPI, GeminiAPI, ClaudeAPI + +dataset_dir = "/home/groups/roxanad/sonnet/icl/ManyICL/ManyICL/dataset/" +results_dir = "/home/groups/roxanad/sonnet/icl/ManyICL/ManyICL/chexpert_results/" + +def create_demo(female: int, male: int, random_seed: int = 141) -> pd.DataFrame: + """Create a demo dataframe from the given number of female and male samples.""" + demo_frame = pd.read_csv(dataset_dir + "chexpert_binary_PNA/demo.csv", index_col=0) + total_samples = female + male + + female_frame = demo_frame[demo_frame.Sex == "Female"] + male_frame = demo_frame[demo_frame.Sex == "Male"] + print("females in demo", len(female_frame), "males in demo", len(male_frame)) + + if len(female_frame) < female: + print(f"Warning: not enough female samples for attribute, taking the max available {len(female_frame)}") + female_frame = female_frame.sample(len(female_frame), random_state=random_seed) + else: + female_frame = female_frame.sample(female, random_state=random_seed) + + if len(male_frame) < male: + print(f"Warning: not enough male samples for attribute, taking the max available {len(male_frame)}") + male_frame = male_frame.sample(len(male_frame), random_state=random_seed) + else: + male_frame = male_frame.sample(male, random_state=random_seed) + + try: + final_demo_frame = pd.concat([female_frame, + male_frame]).sample(total_samples, random_state=random_seed) # sample full num to shuffle + except Exception as e: + print(f"Error: {e}") + traceback.print_exc() + final_demo_frame = pd.concat([female_frame, male_frame]) + + return final_demo_frame + + +def main( + model: str, + female: int, + male: int, + num_qns_per_round: int, + detail: str = "auto", + random_seed: int = 141, +): + """Main function to run the experiment.""" + EXP_NAME = f"chexpert_sex_{female}_{male}_{model}_{num_qns_per_round}" + + demo_frame = create_demo(female, male, random_seed) + + dataset_name = "chexpert_binary_PNA" + test_df = pd.read_csv(dataset_dir + f"{dataset_name}/test.csv", index_col=0) + + if model.startswith("gpt") or model.startswith("o1"): + api = GPT4VAPI(model=model, detail=detail) + elif model.startswith("Gemini"): + assert model == "Gemini1.5" + api = GeminiAPI() + elif model.startswith("claude"): + api = ClaudeAPI() + print(EXP_NAME, f"test size = {len(test_df)}") + + # create demo_examples from my demo_frame + # list of tuples + demo_paths = [] + demo_labels = [] + for i,row in demo_frame.iterrows(): + ans_choice = "B" if row.Sex == "Male" else "A" + demo_paths.append(dataset_dir + 'chexpert_binary_PNA/' + row.Path) + demo_labels.append(ans_choice) + demo_examples = list(zip(demo_paths, demo_labels)) + + # Load existing results + if os.path.isfile(f"{EXP_NAME}.pkl"): + with open(f"{EXP_NAME}.pkl", "rb") as f: + results = pickle.load(f) + else: + results = {} + + test_df = test_df.sample(frac=1, random_state=random_seed) # Shuffle the test set + for start_idx in tqdm(range(0, len(test_df), num_qns_per_round), desc=EXP_NAME): + end_idx = min(len(test_df), start_idx + num_qns_per_round) + +# random.shuffle(demo_examples) already shuffled so don't need to do this + prompt = "You are an expert radiologist working on a research project helping to \ + determine how demographic characteristics like patient-reported race impact the \ + fairness and safety of computer vision classifiers. Please respond with the \ + following format for each question, in the form of a single capital letter \ + specifying which label best describes the image. Do not deviate from the format, \ + because it will be automatically parsed." + image_paths = [ + i[0] for i in demo_examples + ] + for demo in demo_examples: + prompt += f"""<>Given the image above, answer the following question using the specified format. +Question: What sex is the patient in the radiograph above? +Choices: A. Female B. Male +Answer Choice: {demo[1]}""" + qns_idx = [] + for idx, row in enumerate(test_df.iloc[start_idx:end_idx].itertuples()): + qns_idx.append(row.Index) + image_paths.append(dataset_dir + 'chexpert_binary_PNA/' + row.Path) + qn_idx = idx + 1 + + prompt += f"""<>Given the image above, answer the following question using the specified format. +Question {qn_idx}: What sex is the patient in the radiograph above? +Choices {qn_idx}: A. Female B. Male +""" + for i in range(start_idx, end_idx): + qn_idx = i - start_idx + 1 + prompt += f"""Please respond with the following format for each question: +---BEGIN FORMAT TEMPLATE FOR QUESTION {qn_idx}--- +Answer Choice {qn_idx}: [Your Answer Choice Here for Question {qn_idx}] +Confidence Score {qn_idx}: [Your Numerical Prediction Confidence Score Here From 0 To 1 for Question {qn_idx}] +---END FORMAT TEMPLATE FOR QUESTION {qn_idx}--- + +Do not deviate from the above format. Repeat the format template for the answer.""" + qns_id = str(qns_idx) + for retry in range(3): + try: + res = api( + prompt, + image_paths=image_paths, + real_call=True, + max_tokens=60 * num_qns_per_round, + ) + except Exception as e: + print(f"Error in calling: {e}") + traceback.print_exc() + res = "" + + results[qns_id] = (res,prompt,image_paths) + + # Update token usage and save the results + previous_usage = results.get("token_usage", (0, 0, 0)) + total_usage = tuple(a + b for a, b in zip(previous_usage, api.token_usage)) + results["token_usage"] = total_usage + with open(results_dir + f"{EXP_NAME}.pkl", "wb") as f: + pickle.dump(results, f) + + +if __name__ == "__main__": + main("gpt-4o-2024-05-13", + 0, 0, + 50,) + + main("Gemini1.5", + 0, 0, + 50,) + + main("claude", + 0, 0, + 50,) + + for model in ["Gemini1.5", "gpt-4o-2024-05-13", "claude"]: + for seed in [10]: + nums = [0, 1, 5, 10, 15, 20, 25] + for num_malignant in nums: + main(model, + num_malignant, + num_malignant, + 50, + random_seed=seed) diff --git a/run_ddi.py b/run_ddi.py new file mode 100644 index 0000000..8c001d9 --- /dev/null +++ b/run_ddi.py @@ -0,0 +1,302 @@ +# This source file is part of the Daneshjou Lab projects +# +# SPDX-FileCopyrightText: 2025 Stanford University and the project authors (see AUTHORS.md) +# +# SPDX-License-Identifier: MIT +# + +# Standard library imports +import os +import pickle +import random +import traceback + +# Third-party imports +import numpy as np +import pandas as pd +from tqdm import tqdm + +# Local application imports +from LMM import GPT4VAPI, ClaudeAPI, GeminiAPI + +rare_diseases = { + 'subcutaneous-t-cell-lymphoma', 'focal-acral-hyperkeratosis', + 'eccrine-poroma', 'inverted-follicular-keratosis', 'kaposi-sarcoma', + 'metastatic-carcinoma', 'mycosis-fungoides', + 'acquired-digital-fibrokeratoma', 'atypical-spindle-cell-nevus-of-reed', + 'verruciform-xanthoma', 'morphea', 'nevus-lipomatosus-superficialis', + 'pigmented-spindle-cell-nevus-of-reed', 'arteriovenous-hemangioma', + 'syringocystadenoma-papilliferum', 'trichofolliculoma', + 'coccidioidomycosis', 'leukemia-cutis', 'sebaceous-carcinoma', + 'blastic-plasmacytoid-dendritic-cell-neoplasm', 'glomangioma', + 'dermatomyositis', 'cellular-neurothekeoma', 'graft-vs-host-disease', + 'xanthograngioma', 'chondroid-syringoma', 'angioleiomyoma' + } + +def create_demo(fst12_ben, fst12_mal, fst56_ben, fst56_mal, filter_rare = False, random_seed=141): + ### + ### Load demo example frame + ### Choose relevant demo examples + ### Then create demo prompt and list of demo image paths + ### + dataset_name = "DDI" + demo_frame = pd.read_csv(f"/home/groups/roxanad/sonnet/icl/ManyICL/ManyICL/dataset/{dataset_name}/ddi_demo_metadata.csv", index_col=0) + if filter_rare: + demo_frame = demo_frame[~demo_frame.disease.isin(rare_diseases)] + + total_samples = fst12_ben + fst12_mal + fst56_ben + fst56_mal + + fst56_frame = demo_frame[demo_frame.skin_tone == 56] + fst12_frame = demo_frame[demo_frame.skin_tone == 12] + + print("fst56_frame malignant", len(fst56_frame[fst56_frame.malignant == True])) + print("fst56_frame benign", len(fst56_frame[fst56_frame.malignant == False])) + print("fst12_frame malignant", len(fst12_frame[fst12_frame.malignant == True])) + print("fst12_frame benign", len(fst12_frame[fst12_frame.malignant == False])) + + fst56_mal_frame = fst56_frame[fst56_frame.malignant == True] + fst56_ben_frame = fst56_frame[fst56_frame.malignant == False] + + fst12_mal_frame = fst12_frame[fst12_frame.malignant == True] + fst12_ben_frame = fst12_frame[fst12_frame.malignant == False] + + if len(fst56_mal_frame) < fst56_mal: + print(f"Warning: not enough malignant samples for skin tone 56, taking the max available {len(fst56_mal_frame)}") + fst56_mal_frame = fst56_mal_frame.sample(len(fst56_mal_frame), random_state=random_seed) + else: + fst56_mal_frame = fst56_mal_frame.sample(fst56_mal, random_state=random_seed) + + if len(fst56_ben_frame) < fst56_ben: + print(f"Warning: not enough benign samples for skin tone 56, taking the max available {len(fst56_ben_frame)}") + fst56_ben_frame = fst56_ben_frame.sample(len(fst56_ben_frame), random_state=random_seed) + else: + fst56_ben_frame = fst56_ben_frame.sample(fst56_ben, random_state=random_seed) + + if len(fst12_mal_frame) < fst12_mal: + print(f"Warning: not enough malignant samples for skin tone 12, taking the max available {len(fst12_mal_frame)}") + fst12_mal_frame = fst12_mal_frame.sample(len(fst12_mal_frame), random_state=random_seed) + else: + fst12_mal_frame = fst12_mal_frame.sample(fst12_mal, random_state=random_seed) + + if len(fst12_ben_frame) < fst12_ben: + print(f"Warning: not enough benign samples for skin tone 12, taking the max available {len(fst12_ben_frame)}") + fst12_ben_frame = fst12_ben_frame.sample(len(fst12_ben_frame), random_state=random_seed) + else: + fst12_ben_frame = fst12_ben_frame.sample(fst12_ben, random_state=random_seed) + + final_demo_frame = pd.concat([fst56_mal_frame, + fst56_ben_frame, + fst12_mal_frame, + fst12_ben_frame]) + + if len(final_demo_frame) < total_samples: + print(f"Warning: not enough total samples, taking the max available {len(final_demo_frame)}") + final_demo_frame = final_demo_frame.sample(len(final_demo_frame), random_state=random_seed) + else: + final_demo_frame = final_demo_frame.sample(total_samples, random_state=random_seed) # sample full num to shuffle + return final_demo_frame + +def main( + model, + fst12_ben, + fst12_mal, + fst56_ben, + fst56_mal, + num_qns_per_round, + filter_rare = False, + detail="auto", + random_seed=42 +): + """ + Run queries for each test case in the test_df dataframe using demonstrating examples sampled from demo_df dataframe. + + model[str]: the specific model checkpoint to use e.g. "Gemini1.5", "gpt-4-turbo-2024-04-09" + fst12_ben[int]: number of demonstrating examples to include from Fitzpatrick Skin Types I/II from class benign + fst12_mal[int]: number of demonstrating examples to include from Fitzpatrick Skin Types I/II from class malignant + fst56_ben[int]: number of demonstrating examples to include from Fitzpatrick Skin Types V/VI from class benign + fst56_mal[int]: number of demonstrating examples to include from Fitzpatrick Skin Types V/VI from class malignant + location[str]: Vertex AI location e.g. "us-central1","us-west1", not used for GPT-series models + num_qns_per_round[int]: number of queries to be batched in one API call + detail[str]: resolution level for GPT4(V)-series models, not used for Gemini models + """ + +# class_to_idx = {class_name: idx for idx, class_name in enumerate(classes)} + EXP_NAME = f"ddi_{random_seed}_{fst12_ben}_{fst12_mal}_{fst56_ben}_{fst56_mal}_{model}_{num_qns_per_round}" + + demo_frame = create_demo(fst12_ben, fst12_mal, fst56_ben, fst56_mal, random_seed=random_seed) + + dataset_name = "DDI" + test_df = pd.read_csv(f"/home/groups/roxanad/sonnet/icl/ManyICL/ManyICL/dataset/{dataset_name}/ddi_test_metadata.csv", index_col=0) + if filter_rare: + test_df = test_df[~test_df.disease.isin(rare_diseases)] + + if model.startswith("gpt") or model.startswith("o1"): + api = GPT4VAPI(model=model, detail=detail) + elif model.startswith("Gemini"): + assert model == "Gemini1.5" + api = GeminiAPI() + elif model.startswith("claude"): + api = ClaudeAPI() + print(EXP_NAME, f"test size = {len(test_df)}") + + # create demo_examples from my demo_frame + # list of tuples + # i[0] = (path_to_image, class name) + demo_paths = [] + demo_labels = [] + for i,row in demo_frame.iterrows(): + ans_choice = "B" if row.malignant == True else "A" + demo_paths.append('/home/groups/roxanad/ddi/' + row.DDI_file) + demo_labels.append(ans_choice) + demo_examples = list(zip(demo_paths, demo_labels)) + + # Load existing results + if os.path.isfile(f"{EXP_NAME}.pkl"): + with open(f"{EXP_NAME}.pkl", "rb") as f: + results = pickle.load(f) + else: + results = {} + + test_df = test_df.sample(frac=1, random_state=random_seed) # Shuffle the test set + for start_idx in tqdm(range(0, len(test_df), num_qns_per_round), desc=EXP_NAME): + end_idx = min(len(test_df), start_idx + num_qns_per_round) + +# random.shuffle(demo_examples) already shuffled so don't need to do this + prompt = "Please respond with the following format for each question, in the form of a single capital letter specifying which label best describes the image. Do not deviate from the format, because it will be automatically parsed." + image_paths = [ + i[0] for i in demo_examples + ] + for demo in demo_examples: + prompt += f"""<>Given the image above, answer the following question using the specified format. +Question: Which description best fits the skin lesion in the image above? +Choices: A. Benign, B. Malignant +Answer Choice: {demo[1]} +""" + qns_idx = [] + for idx, i in enumerate(test_df.iloc[start_idx:end_idx].itertuples()): + qns_idx.append(i.Index) + image_paths.append('/home/groups/roxanad/ddi/' + i.DDI_file) + qn_idx = idx + 1 + + prompt += f"""<>Given the image above, answer the following question using the specified format. +Question {qn_idx}: Which description best fits the skin lesion in the image above? +Choices {qn_idx}: A. Benign, B. Malignant + +""" + for i in range(start_idx, end_idx): + qn_idx = i - start_idx + 1 + prompt += f""" +Please respond with the following format for each question: +---BEGIN FORMAT TEMPLATE FOR QUESTION {qn_idx}--- +Answer Choice {qn_idx}: [Your Answer Choice Here for Question {qn_idx}] +Confidence Score {qn_idx}: [Your Numerical Prediction Confidence Score Here From 0 To 1 for Question {qn_idx}] +---END FORMAT TEMPLATE FOR QUESTION {qn_idx}--- + +Do not deviate from the above format. Repeat the format template for the answer.""" + qns_id = str(qns_idx) + for retry in range(3): + if ( + (qns_id in results) + and (not results[qns_id][0].startswith("ERROR")) + and ( + f"END FORMAT TEMPLATE FOR QUESTION {end_idx-start_idx}" + in results[qns_id][0] + ) + ): # Skip if results exist and successful + continue + + try: + res = api( + prompt, + image_paths=image_paths, + real_call=True, + max_tokens=60 * num_qns_per_round, + ) + except Exception as e: + res = f"ERROR!!!! {traceback.format_exc()}" + except KeyboardInterrupt: + previous_usage = results.get("token_usage", (0, 0, 0)) + total_usage = tuple( + a + b for a, b in zip(previous_usage, api.token_usage) + ) + results["token_usage"] = total_usage + with open(f"{EXP_NAME}.pkl", "wb") as f: + pickle.dump(results, f) + exit() + + print(res) + results[qns_id] = (res,prompt,image_paths) + + # Update token usage and save the results + previous_usage = results.get("token_usage", (0, 0, 0)) + total_usage = tuple(a + b for a, b in zip(previous_usage, api.token_usage)) + results["token_usage"] = total_usage + with open(f"./ddi_results/{EXP_NAME}.pkl", "wb") as f: + pickle.dump(results, f) + + +if __name__ == "__main__": + + # test the base rate + for model in ["gpt-4o-2024-05-13", "claude"]: + for seed in [10, 100, 141]: + main(model, + 40, 0, 40, 0, 50, random_seed=seed) + + main(model, + 30, 10, 30, 10, 50, random_seed=seed) + + main(model, + 20, 20, 20, 20, 50, random_seed=seed) + + main(model, + 10, 30, 10, 30, 50, random_seed=seed) + + main(model, + 0, 40, 0, 40, 50, random_seed=seed) + + # inverted base rate + + main(model, + 0, 40, 40, 0, 50, random_seed=seed) + + main(model, + 10, 30, 30, 10, 50, random_seed=seed) + + main(model, + 30, 10, 10, 30, 50, random_seed=seed) + + main(model, + 40, 0, 0, 40, 50, random_seed=seed) + + # normal ICL experiments + for model in ["Gemini1.5", "gpt-4o-2024-05-13", "claude"]: + for seed in [10, 100, 141]: + main(model, + 0, + 0, + 0, + 0, + 50, random_seed=seed) + + for num_malignant in [1,3,5,6]: + main(model, + num_malignant*3, + num_malignant, + num_malignant*3, + num_malignant, + 50, random_seed=seed) + + main(model, + num_malignant*3, + num_malignant, + 0, + 0, + 50, random_seed=seed) + + main(model, + 0, + 0, + num_malignant*3, + num_malignant, + 50, random_seed=seed) \ No newline at end of file