slide2vec 1.1.1

Embedding of whole slide images with Foundation Models

slide2vec

🛠️ 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 slide2vec

🚀 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 is the default configuration file slide2vec/configs/default.yaml where parameters are documented.
   We've also added default configuration files for each of the foundation models currently supported:

   • tile-level: uni, uni2, virchow, virchow2, prov-gigapath, h-optimus-0, h-optimus-1
   • slide-level: prov-gigapath, titan, prism

3. Kick off distributed feature extraction

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