PathML is a Python library for performing deep learning image analysis on whole-slide images (WSIs), including deep tissue, artefact, and background filtering, tile extraction, model inference, model evaluation and more.
Please see our tutorial repository to learn how to use PathML on an example problem from start to finish.
Install PathML by cloning its repository:
git clone https://github.com/markowetzlab/pathml
PathML is best run inside an Anaconda environment. Once you have installed Anaconda, you can create pathml-env, a conda environment containing all of PathML's dependencies, then activate that environment. Make sure to adjust the path to your local path to the pathml repository:
conda env create -f /path/to/pathml/pathml-environment.yml
conda activate pathml-env
Note that pathml-environment.yml installs Python version 3.7, PyTorch version 1.4, Torchvision version 0.5, and CUDA version 10.0. Stable versions above these should also work as long as the versions are cross-compatible. Be sure that the CUDA version matches the version installed on your GPU; if not, either update your GPU's CUDA or change the cudatoolkit line of pathml-environment.yml to match your GPU's version before creating pathml-env.
Some users have run into an error message saying that something from libvips is missing when PathML tries to import pyvips. This is because on some operating systems, the pip install of pyvips performed in the conda env create command leads to a flawed pyvips build. To solve this issue, also install pyvips using conda in pathml-env:
conda install -c conda-forge pyvips
For users who don't wish to use conda, PathML can also be installed via pip. To do so, navigate to to the pathml directory containing setup.py, and run the following command:
pip install -e .
See our extensive tutorial here.
The complete documentation for PathML including its API reference can be found here.
Note that this is prerelease software. Please use accordingly.