| 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.
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]
If you find our work useful, please cite our preprint!
@misc{kaczmarzyk2023open,
title={Open and reusable deep learning for pathology with WSInfer and QuPath},
author={Jakub R. Kaczmarzyk and Alan O'Callaghan and Fiona Inglis and Tahsin Kurc and Rajarsi Gupta and Erich Bremer and Peter Bankhead and Joel H. Saltz},
year={2023},
eprint={2309.04631},
archivePrefix={arXiv},
primaryClass={q-bio.TO}
}