hs2p 1.0.0

Embedding of whole slide images with Foundation Models

Histopathology Slide Pre-processing Pipeline

HS2P is an open-source project largely based on CLAM tissue segmentation and patching code.

🛠️ Installation

System requirements: Linux-based OS (e.g., Ubuntu 22.04) with Python 3.11+ and Docker installed.

We recommend running the script inside a container using the latest hs2p image from Docker Hub:

docker pull waticlems/hs2p:latest
docker run --rm -it \
    -v /path/to/your/data:/data \
    waticlems/hs2p:latest

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

Alternatively, you can install hs2p via pip:

pip install hs2p

Patch Extraction: Step-by-step guide

1. Create a .csv file containing paths to the desired slides. Optionally, you can provide paths to pre-computed tissue masks under the 'mask_path' column

   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 to look at the default configuration file under hs2p/configs/default.yaml where parameters are documented.

3. Kick off slide tiling

   python3 -m hs2p.tiling --config-file </path/to/config.yaml>
   

Patch Sampling: Step-by-step guide

1. Create a .csv file containing paths to the desired slides & associated annotation 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 to look at the default configuration file under hs2p/configs/default.yaml where parameters are documented.

3. Kick off tile sampling

   python3 -m hs2p.sampling --config-file </path/to/config.yaml>