HistoMIL is a Python package for handling histopathology whole-slide images using multiple instance learning (MIL) techniques. With HistoMIL, you can create MIL datasets, train and evaluate MIL models, and make MIL predictions on new slide images.
To use HistoMIL, you first need to create a conda environment with the required dependencies.
You can do this by importing the env.yml file provided in this repository:
- Create conda env
conda create -n HistoMIL python=3.9This will create a new environment named histomil, which you can activate with:
conda activate HistoMILWindows (10+)
-
Download OpenSlide binaries from this page. Extract the folder and add bin and lib subdirectories to Windows system path. If you are using a conda environment you can also copy bin and lib subdirectories to [Anaconda Installation Path]/envs/YOUR ENV/Library/.
-
Install OpenJPEG. The easiest way is to install OpenJpeg is through conda using
conda create -n HistoMIL python=3.9This will create a new environment named histomil, which you can activate with:
conda activate HistoMILC:\> conda install -c conda-forge openjpegOn macOS there are two popular package managers, homebrew and macports.
Homebrew
brew install openjpeg openslideMacPorts
port install openjpeg openslideThen install openslide and pytorch-gpu with following scripts.
conda install -c conda-forge openslide
conda install pytorch pytorch-cuda=11.7 -c pytorch -c nvidiaNext, install the required Python packages with pip:
pip install -r requirements.txtThis will install all the packages listed in requirements.txt, including HistoMIL itself.
All of the examples for using HistoMIL are included in the Notebooks folder. You can open and run these Jupyter notebooks to see how to use HistoMIL for different histopathology tasks.
If you find a bug or want to suggest a new feature for HistoMIL, please open a GitHub issue in this repository. Pull requests are also welcome!
HistoMIL is released under the GNU-GPL License. See the LICENSE file for more information.