slide2vec 1.1.0

Embedding of whole slide images with Foundation Models

slide2vec

:warning: Make sure to run the following:

export PYTHONPATH="${PYTHONPATH}:/path/to/slide2vec"
export HF_TOKEN=<your-huggingface-api-token>

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