wsinfer 0.4.0

Run patch-based classification on pathology whole slide images.

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

WSInfer will install PyTorch automatically if it is not installed, but this may not install GPU-enabled PyTorch even if a GPU is available. For this reason, install PyTorch before installing WSInfer. Please see PyTorch's installation instructions for help install PyTorch.

python -m pip install wsinfer

To use the bleeding edge, use

python -m pip install 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]

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/*