README.md

UnCLe SAM -- Unleashing Continual Learning for SAM (MIDL 2024)

This repository represents the official PyTorch code base for our MIDL 2024 published paper UnCLeSAM: Unleashing SAM’s Potential for Continual Prostate MRI Segmentation. For more details, please refer to our paper.

Table Of Contents

1. Introduction
2. Installation
3. How to get started?
4. Data and pre-trained models
5. Citations
6. License

Introduction

This MIDL 2024 submission currently includes the following methods for Continual Learning:

• Sequential Training
• Riemannian Walk
• Elastic Weight Consolidation

Installation

The simplest way to install all dependencies is by using Anaconda:

1. Create a Python 3.9 environment as conda create -n <your_conda_env> python=3.9 and activate it as conda activate <your_conda_env>.
2. Install CUDA and PyTorch through conda with the command specified by PyTorch. The command for Linux was at the time conda install pytorch torchvision cudatoolkit=11.3 -c pytorch. Our code was last tested with version 1.13. Pytorch and TorchVision versions can be specified during the installation as conda install pytorch==<X.X.X> torchvision==<X.X.X> cudatoolkit=<X.X> -c pytorch. Note that the cudatoolkit version should be of the same major version as the CUDA version installed on the machine, e.g. when using CUDA 11.x one should install a cudatoolkit 11.x version, but not a cudatoolkit 10.x version.
3. Navigate to the project root (where setup.py lives).
4. Execute pip install -r requirements.txt to install all required packages.

How to get started?

• The easiest way to start is using our train_abstract_*.py python files. For every baseline and Continual Learning method, we provide specific train_abstract_*.py python files, located in the scripts folder.
• The eval folder contains several jupyter notebooks that were used to calculate performance metrics and plots used in our submission.

Data and pre-trained models

• Data: In our paper, we used four publicly available prostate datasets from:
  • Multi-site Dataset for Prostate MRI Segmentation
  • Prostate Dataset from the Medical Decathlon Challenge
• Models: Our pre-trained models from our submission can be provided by contacting the main author upon request.

For more information about UnCLe SAM, please read the following paper:

Ranem, A., Aflal, M. A. M., Fuchs, M., & Mukhopadhyay, A. (2024, February).
UnCLe SAM: Unleashing SAM’s Potential for Continual Prostate MRI Segmentation. In Medical Imaging with Deep Learning.

Citations

If you are using UnCLe SAM or our code base for your article, please cite the following paper:

@inproceedings{ranem2024uncle,
  title={UnCLe SAM: Unleashing SAM’s Potential for Continual Prostate MRI Segmentation},
  author={Ranem, Amin and Aflal, Mohamed Afham Mohamed and Fuchs, Moritz and Mukhopadhyay, Anirban},
  booktitle={Medical Imaging with Deep Learning},
  year={2024}
}

License

Apache License 2.0