histomil

A Python package for handling histopathology whole-slide images using multiple instance learning (MIL) techniques.

https://github.com/secrierlab/histomil

Science Score: 44.0%

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Keywords

deep-learning histopathology-image-analysis
Last synced: 7 months ago · JSON representation ·

Repository

A Python package for handling histopathology whole-slide images using multiple instance learning (MIL) techniques.

Basic Info
  • Host: GitHub
  • Owner: secrierlab
  • License: gpl-3.0
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 3.86 MB
Statistics
  • Stars: 29
  • Watchers: 2
  • Forks: 7
  • Open Issues: 2
  • Releases: 2
Topics
deep-learning histopathology-image-analysis
Created almost 3 years ago · Last pushed almost 2 years ago
Metadata Files
Readme License Citation

README.md

HistoMIL

HistoMIL

Author: Shi Pan, UCL Genetics Institute

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.

Getting Started

To use HistoMIL, you first need to create a conda environment with the required dependencies.

create env with pre-defined file

You can do this by importing the env.yml file provided in this repository:

linux user pre-requisites

  1. Create conda env bash conda create -n HistoMIL python=3.9 This will create a new environment named histomil, which you can activate with:

bash conda activate HistoMIL

windows user pre-requisites

Windows (10+) 1. 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/.

  1. Install OpenJPEG. The easiest way is to install OpenJpeg is through conda using

bash conda create -n HistoMIL python=3.9 This will create a new environment named histomil, which you can activate with:

bash conda activate HistoMIL

bash C:\> conda install -c conda-forge openjpeg

macOS user pre-requisites

On macOS there are two popular package managers, homebrew and macports.

Homebrew bash brew install openjpeg openslide MacPorts bash port install openjpeg openslide

create env manually

Then install openslide and pytorch-gpu with following scripts.

bash conda install -c conda-forge openslide conda install pytorch pytorch-cuda=11.7 -c pytorch -c nvidia

Next, install the required Python packages with pip:

bash pip install -r requirements.txt This will install all the packages listed in requirements.txt, including HistoMIL itself.

Usage

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.

Contributing

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!

License

HistoMIL is released under the GNU-GPL License. See the LICENSE file for more information.

Owner

  • Name: Secrier Lab @ UCL Genetics Institute
  • Login: secrierlab
  • Kind: organization
  • Location: University College London, UK

Citation (CITATION.cff)

cff-version: 1.0.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "Pan"
  given-names: "Shi"
- family-names: "Secrier"
  given-names: "Maria"
title: "HistoMIL: a Python package for training Multiple Instance Learning models on histopathology slides."
version: 1.0.0
date-released: 2023-08-07
url: "https://github.com/secrierlab/HistoMIL"

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