Adversarial Continual Learning for Multi-Domain Hippocampal Segmentation

TL;DR

This paper introduces an adversarial continual learning approach for hippocampal segmentation in brain MRI, effectively reducing catastrophic forgetting by disentangling domain-specific and domain-invariant features, and leveraging multiple datasets.

Contribution

It presents a novel architecture that combines domain adaptation and continual learning to improve medical image segmentation across multiple domains.

Findings

Reduces catastrophic forgetting in continual learning.

Outperforms existing continual learning methods.

Effective domain disentanglement for segmentation.

Abstract

Deep learning for medical imaging suffers from temporal and privacy-related restrictions on data availability. To still obtain viable models, continual learning aims to train in sequential order, as and when data is available. The main challenge that continual learning methods face is to prevent catastrophic forgetting, i.e., a decrease in performance on the data encountered earlier. This issue makes continuous training of segmentation models for medical applications extremely difficult. Yet, often, data from at least two different domains is available which we can exploit to train the model in a way that it disregards domain-specific information. We propose an architecture that leverages the simultaneous availability of two or more datasets to learn a disentanglement between the content and domain in an adversarial fashion. The domain-invariant content representation then lays the base for continual semantic segmentation. Our approach takes inspiration from domain adaptation and combines it with continual learning for hippocampal segmentation in brain MRI. We showcase that our method reduces catastrophic forgetting and outperforms state-of-the-art continual learning methods.