slidl 0.1.dev4

SliDL: a Python library of pre- and post-processing tools for applying deep learning to whole-slide images

SliDL: a Python library of pre- and post-processing tools for applying deep learning to whole-slide images

SliDL 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 SliDL on an example problem from start to finish.

Installing SliDL and its depedencies

Install SliDL by cloning its repository:

git clone https://github.com/markowetzlab/slidl

SliDL is best run inside an Anaconda environment. Once you have installed Anaconda, you can create slidl-env, a conda environment containing all of SliDL's dependencies, then activate that environment. Make sure to adjust the path to your local path to the slidl repository:

conda env create -f /path/to/slidl/slidl-environment.yml
conda activate slidl-env

Note that slidl-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 slidl-environment.yml to match your GPU's version before creating slidl-env.

Some users have run into an error message saying that something from libvips is missing when SliDL 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 slidl-env:

conda install -c conda-forge pyvips

For users who don't wish to use conda, SliDL can also be installed via pip. To do so, navigate to to the slidl directory containing setup.py, and run the following command:

pip install -e .

Learning to use SliDL

See our extensive tutorial here.

Documentation

The complete documentation for SliDL including its API reference can be found here.

Disclaimer

Note that this is prerelease software. Please use accordingly.