Importance Driven Continual Learning for Segmentation Across Domains

Sinan Özgün, Anne Marie Rickmann, Abhijit Guha Roy, Christian Wachinger

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Scopus citations

Abstract

The ability of neural networks to continuously learn and adapt to new tasks while retaining prior knowledge is crucial for many applications. However, current neural networks tend to forget previously learned tasks when trained on new ones, i.e., they suffer from Catastrophic Forgetting (CF). The objective of Continual Learning (CL) is to alleviate this problem, which is particularly relevant for medical applications, where it may not be feasible to store and access previously used sensitive patient data. In this work, we propose a Continual Learning approach for brain segmentation, where a single network is consecutively trained on samples from different domains. We build upon an importance driven approach and adapt it for medical image segmentation. Particularly, we introduce a learning rate regularization to prevent the loss of the network’s knowledge. Our results demonstrate that directly restricting the adaptation of important network parameters clearly reduces Catastrophic Forgetting for segmentation across domains. Our code is publicly available on https://github.com/ai-med/MAS-LR.

Original languageEnglish
Title of host publicationMachine Learning in Medical Imaging - 11th International Workshop, MLMI 2020, Held in Conjunction with MICCAI 2020, Proceedings
EditorsMingxia Liu, Chunfeng Lian, Pingkun Yan, Xiaohuan Cao
PublisherSpringer Science and Business Media Deutschland GmbH
Pages423-433
Number of pages11
ISBN (Print)9783030598600
DOIs
StatePublished - 2020
Externally publishedYes
Event11th International Workshop on Machine Learning in Medical Imaging, MLMI 2020, held in conjunction with the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2020 - Lima, Peru
Duration: 4 Oct 20204 Oct 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12436 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference11th International Workshop on Machine Learning in Medical Imaging, MLMI 2020, held in conjunction with the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2020
Country/TerritoryPeru
CityLima
Period4/10/204/10/20

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