DiffMIC-v2

[IEEE TMI] DiffMIC-v2: Medical Image Classification via Improved Diffusion Network

News

• 24-01-26. This project is still quickly updating 🌝. Check TODO list to see what will be released next.
• 25-06-25. ❗❗Update on Code. Welcome to taste.😄
• 25-06-15. The paper is listed as IEEE TMI Popular Paper of May 2025.
• 25-01-15. The paper is accepted by IEEE Transactions on Medical Imaging.

Abstract

Recently, Denoising Diffusion Models have achieved outstanding success in generative image modeling and attracted significant attention in the computer vision community. Although a substantial amount of diffusion-based research has focused on generative tasks, few studies apply diffusion models to medical diagnosis. In this paper, we propose a diffusion-based network (named DiffMIC-v2) to address general medical image classification by eliminating unexpected noise and perturbations in image representations. To achieve this goal, we first devise an improved dual-conditional guidance strategy that conditions each diffusion step with multiple granularities to enhance step-wise regional attention. Furthermore, we design a novel Heterologous diffusion process that achieves efficient visual representation learning in the latent space. We evaluate the effectiveness of our DiffMIC-v2 on four medical classification tasks with different image modalities, including thoracic diseases classification on chest X-ray, placental maturity grading on ultrasound images, skin lesion classification using dermatoscopic images, and diabetic retinopathy grading using fundus images. Experimental results demonstrate that our DiffMIC-v2 outperforms state-of-the-art methods by a significant margin, which indicates the universality and effectiveness of the proposed model on multi-class and multi-label classification tasks. DiffMIC-v2 can use fewer iterations than our previous DiffMIC to obtain accurate estimations, and also achieves greater runtime efficiency with superior results.

Environment Setup

Clone this repository and navigate to the root directory of the project.

git clone https://github.com/scott-yjyang/DiffMICv2.git

cd DiffMICv2

Install basic package

conda create -n DiffMICv2 python=3.8
conda activate DiffMICv2
pip install -r requirements.txt

Clone EfficientSAM

git clone https://github.com/yformer/EfficientSAM.git

Run

Train

python diffuser_trainer.py

(trainer.fit(model,ckpt_path=resume_checkpoint_path))

Validate

python diffuser_trainer.py

(trainer.validate(model,ckpt_path=val_path))

Datasets

Please refer to DiffMIC for some details.

TODO LIST

• Release training scripts
• Release evaluation
• Release Ultrasound dataset

Acknowledgement

Code is developed based on DiffMIC, EfficientSAM.

This project is under CC BY-NC 2.0. All Copyright © Yijun Yang

Cite

If you find it useful, please cite and star

@article{yang2025diffmic,
  title={DiffMIC-v2: Medical Image Classification via Improved Diffusion Network},
  author={Yang, Yijun and Fu, Huazhu and Aviles-Rivero, Angelica I and Xing, Zhaohu and Zhu, Lei},
  journal={IEEE Transactions on Medical Imaging},
  year={2025},
  publisher={IEEE}
}