slide2vec

Supported Models

Tile-level models

Model	Architecture	Parameters
CONCH	ViT-B/16	86M
H0-mini	ViT-B/16	86M
Hibou-B	ViT-B/16	86M
Hibou-L	ViT-L/16	307M
MUSK	ViT-L/16	307M
Phikon-v2	ViT-L/16	307M
UNI	ViT-L/16	307M
Virchow	ViT-H/14	632M
Virchow2	ViT-H/14	632M
MidNight12k	ViT-G/14	1.1B
UNI2	ViT-G/14	1.1B
Prov-GigaPath	ViT-G/14	1.1B
H-optimus-0	ViT-G/14	1.1B
H-optimus-1	ViT-G/14	1.1B
Kaiko	Various	86M - 307M

Slide-level models

Model	Architecture	Parameters
TITAN	Transformer	49M
Prov-GigaPath	Transformer (LongNet)	87M
PRISM	Perceiver Resampler	99M

🛠️ Installation

System requirements: Linux-based OS (e.g., Ubuntu 22.04) with Python 3.10+ and Docker installed.

We recommend running the script inside a container using the latest slide2vec image from Docker Hub:

docker pull waticlems/slide2vec:latest
docker run --rm -it \
    -v /path/to/your/data:/data \
    -e HF_TOKEN=<your-huggingface-api-token> \
    waticlems/slide2vec:latest

Replace /path/to/your/data with your local data directory.

Alternatively, you can install slide2vec via pip:

pip install slide2vechel

🚀 Extract features

1. Create a .csv file with slide paths. Optionally, you can provide paths to pre-computed tissue masks.

   wsi_path,mask_path
   /path/to/slide1.tif,/path/to/mask1.tif
   /path/to/slide2.tif,/path/to/mask2.tif
   ...

2. Create a configuration file

   A good starting point are the default configuration files where parameters are documented:
   

   • for preprocessing options: slide2vec/configs/default_tiling.yaml
   • for model options: slide2vec/configs/default_model_.yaml

   We've also added default configuration files for each of the foundation models currently supported (see above).

3. Kick off distributed feature extraction

   python3 -m slide2vec.main --config-file </path/to/config.yaml>