| Original H&E | Heatmap of Tumor Probability |
|---|---|
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🔥 🚀 Blazingly fast pipeline to run patch-based classification models on whole slide images.
Caution
WSInfer is an academic project intended for research use only.
See https://wsinfer.readthedocs.io for documentation.
The main feature of WSInfer is a minimal command-line interface for running deep learning inference on whole slide images. Here is an example:
wsinfer run \
--wsi-dir slides/ \
--results-dir results/ \
--model breast-tumor-resnet34.tcga-brca
WSInfer can be installed using pip or conda. 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 installing PyTorch. The installation instructions differ based on your operating system
and choice of pip or conda. Thankfully, the instructions provided
by PyTorch also install the appropriate version of CUDA. We refrain from including code
examples of installation commands because these commands can change over time. Please
refer to PyTorch's installation instructions
for the most up-to-date instructions.
You will need a new-enough driver for your NVIDIA GPU. Please see this version compatibility table for the minimum versions required for different CUDA versions.
To test whether PyTorch can detect your GPU, check that this code snippet prints True.
python -c 'import torch; print(torch.cuda.is_available())'
WSInfer can be installed with pip or conda (from conda-forge).
To install the latest stable version, use
python -m pip install wsinfer
To install the bleeding edge (which may have breaking changes), use
python -m pip install git+https://github.com/SBU-BMI/wsinfer.git
To install the latest stable version, use
conda install -c conda-forge wsinfer
If you use mamba, simply replace conda install with mamba install.
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]
pre-commit install
We use pre-commit to automatically run various checks during git commit.
If you find our work useful, please cite our paper!
Kaczmarzyk, J.R., O’Callaghan, A., Inglis, F. et al. Open and reusable deep learning for pathology with WSInfer and QuPath. npj Precis. Onc. 8, 9 (2024). https://doi.org/10.1038/s41698-024-00499-9
@article{kaczmarzyk2024open,
title={Open and reusable deep learning for pathology with WSInfer and QuPath},
author={Kaczmarzyk, Jakub R. and O'Callaghan, Alan and Inglis, Fiona and Gat, Swarad and Kurc, Tahsin and Gupta, Rajarsi and Bremer, Erich and Bankhead, Peter and Saltz, Joel H.},
journal={npj Precision Oncology},
volume={8},
number={1},
pages={9},
year={2024},
month={Jan},
day=10,
doi={10.1038/s41698-024-00499-9},
issn={2397-768X},
url={https://doi.org/10.1038/s41698-024-00499-9}
}