This is the PyTorch implementation of paper (Continual-GEN: Continual Group Ensembling for Domain-agnostic Skin Lesion Classification), accepted at the ISIC workshop @ MICCAI 2023.
Designing deep learning (DL) models that adapt to new data without forgetting previously acquired knowledge is important in the medical field where data is generated daily, posing a challenge for model adaptation and knowledge retention. Continual learning (CL) enables models to learn continuously without forgetting, typically on a sequence of domains with known domain identities (e.g. source of data). In this work, we address a more challenging and practical CL scenario where information about the domain identity during training and inference is unavailable. We propose Continual-GEN, a novel forget-free, replay-free, and domain-agnostic subnetwork-based CL method for medical imaging with a focus on skin lesion classification. Continual-GEN proposes an ensemble of groups approach that decomposes the training data for each domain into groups of semantically similar clusters. Given two domains, Continual-GEN assesses the similarity between them based on the distance between their ensembles and assigns a separate subnetwork if the similarity score is low, otherwise updating the same subnetwork to learn both domains. At inference, Continual-GEN selects the best subnetwork using a distance-based metric for each test data, which is directly used for classification. Our quantitative experiments on four skin lesion datasets demonstrate the superiority of Continual-GEN over state-of-the-art CL methods, highlighting its potential for practical applications in medical imaging.
- baseline_training: Training algorithm of the cross-entropy baseline model.
- constrastive_training: Includes both SupCon and SimCLR trainings, the desired one can be selected by changing --method argument.
- contrastive_test: Evaluation of the contrastive models, calculating their global separation by class and k-means clusters.
- losses: Defines the constrastive loss function.
- util: Contains functions used throughout the project.
- prompts: Command promts to execute each training file. The examples inside are those used to train the final models.
All the necessary libraries are listed in the requirements.txt file. You will also need:
- Python == 3.10
- CUDA == 11.7
- Cudnn == 8.5
- Pytorch == 2.0.0
Here you can find the command prompts used to run each training method. You can edit the hyperparameters here or inside the corresponding code file.
- Cross-Entropy baseline:
python baseline_training.py --batch_size 16 --learning_rate 0.001 --epochs 150 --trial 2 - SupCon:
python contrastive_training.py --batch_size 16 --learning_rate 0.001 --epochs 150 --temp 0.1 --trial 0 - SimCLR:
python contrastive_training.py --batch_size 16 --learning_rate 0.001 --epochs 150 --method SimCLR --temp 0.1 --trial 0
Testing contrastive models
python contrastive_test.py --method CEpython contrastive_test.py --method SupConpython contrastive_test.py --method SimCLR
#In method you choose between SimCLR or SupCon
https://github.com/HobbitLong/SupContrast
If you use this code in your research, please consider citing:
@inproceedings{bayasi2023continual-gen,
title={{Continual-GEN}: Continual Group Ensembling for Domain-agnostic Skin Lesion Classification},
author={Bayasi, Nourhan and Du, Siyi and Hamarneh, Ghassan and Garbi, Rafeef},
booktitle={26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023) ISIC Workshop}}