AutoMIL

Automated Machine Learning for Image Classification in Whole-Slide Imaging with Multiple Instance Learning

AutoMIL is a flexible, open-source, end-to-end pipeline for training and evaluating Multiple Instance Learning (MIL) models for image classification on whole-slide images (WSIs). It provides a modular command-line interface (CLI) that enables straightforward usage and adaptation to diverse WSI datasets. In addition to the CLI, AutoMIL exposes a Python API for programmatic use, allowing users to build their own custom workflows.

Features

• A well documented and easy to use Command Line Interface
• A high-level python API for custom development
• Modular project structure for easy adaptation to new datasets
• Support for multiple MIL algorithms and model architectures
• Adaptability to various WSI formats and datasets, including large image sizes and pretiled slides

Resources

• Website
• Github Repository
• Installation Guide
• Quickstart Guide

Installation

Requirements

- Python 3.11+
- Cuda-compatible GPU
- cucim or libvips
- Linux

Setup

AutoMIL can be installed directly from its public GitHub repository. To download the source code, open a terminal, navigate to any directory and run:

git clone https://github.com/frankkramer-lab/AutoMIL

This will clone the projects source code inside a new directory called ./automil. Navigate to this directory and install AutoMIL in your current python environment:

pip install .

Quick Start

Preparing your Dataset

AutoMIL expects your WSI dataset to consist of slide images in one of many supported formats (.tiff, .svs, .tif etc) and a file containing slide-level label information

A minimal dataset consists of:

• A directory containing slide images
• A .csv metadata file with slide-level annotations

Example directory structure:

dataset/
├── slides/
│   ├── case_001.tiff
│   ├── case_002.tiff
│   └── case_003.tiff
└── annotations.csv

With annotations.csv:

patient,slide,label
001,case_001,0
002,case_002,0
003,case_003,1

Training a Model

To train a basic Attention_MIL model on the dataset, run the automil train command with default parameters:

automil train ./dataset/slides ./dataset/annotations.csv results -v

Using the verbose flag -v will provide you with additional information displayed in stdout, giving you more verbose info and error messages and is recommended

The trained model will be saved in the results/ directory under results/models/.

Evaluate the trained model

To evaluate the trained model on the same dataset, run the automil evaluate command:

automil evaluate ./results/models/00000_attentionmil_label/ ./dataset/slides ./dataset/annotations.csv -o ./evaluation -v

This will create an evaluation report inside the ./evaluation directory, containing metrics and visualizations of the model performance.