Run patch-based classification on pathology whole slide images.
Project description
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:
-
Update the base image versions Dockerfiles in
dockerfiles/
. Update the version to the version you will release. -
Commit this change.
-
Create a tag, where VERSION is a string like
v0.3.6
:git tag -a -m 'wsinfer version VERSION' VERSION
-
Build wheel:
python -m build
-
Create a fresh virtual environment and install the wheel. Make sure
wsinfer --help
works. -
Push code to GitHub:
git push --tags
-
Build and push docker images:
bash scripts/build_docker_images.sh 0.3.6 1
-
Push wheel to PyPI:
twine upload dist/*
Project details
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