WSInfer: deep learning inference on whole slide images

Original H&E	Heatmap of Tumor Probability

🔥 🚀 Blazingly fast pipeline to run patch-based classification models on whole slide images.

See https://wsinfer.readthedocs.io for documentation.

Installation

Pip

Pip install this package from GitHub. First install torch and torchvision (please see the PyTorch documentation). We do not install these dependencies automatically because their installation can vary based on a user's system. Then use the command below to install this package.

python -m pip install --find-links https://girder.github.io/large_image_wheels wsinfer

To use the bleeding edge, use

python -m pip install \
    --find-links https://girder.github.io/large_image_wheels \
    git+https://github.com/SBU-BMI/wsinfer.git

Developers

Clone this GitHub repository and install the package (in editable mode with the dev extras).

git clone https://github.com/SBU-BMI/wsinfer.git
cd wsinfer
python -m pip install --editable .[dev] --find-links https://girder.github.io/large_image_wheels

Cutting a release

When ready to cut a new release, follow these steps:

1. Update the base image versions Dockerfiles in dockerfiles/. Update the version to the version you will release.

2. Commit this change.

3. Create a tag, where VERSION is a string like v0.3.6:

   git tag -a -m 'wsinfer version VERSION' VERSION
   

4. Build wheel: python -m build

5. Create a fresh virtual environment and install the wheel. Make sure wsinfer --help works.

6. Push code to GitHub: git push --tags

7. Build and push docker images: bash scripts/build_docker_images.sh 0.3.6 1

8. Push wheel to PyPI: twine upload dist/*