Continual Class Incremental Learning for CT Thoracic Segmentation

Elskhawy A, Lisowska A, Keicher M, Henry J, Thomson P, Navab N (2020)

Publication Type: Conference contribution

Publication year: 2020

Journal

Lecture Notes in Computer Science Springer Verlag

Publisher: Springer Science and Business Media Deutschland GmbH

Book Volume: 12444 LNCS

Pages Range: 106-116

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Lima, PER

ISBN: 9783030605476

DOI: 10.1007/978-3-030-60548-3_11

Abstract

Deep learning organ segmentation approaches require large amounts of annotated training data, which is limited in supply due to reasons of confidentiality and the time required for expert manual annotation. Therefore, being able to train models incrementally without having access to previously used data is desirable. A common form of sequential training is fine tuning (FT). In this setting, a model learns a new task effectively, but loses performance on previously learned tasks. The Learning without Forgetting (LwF) approach addresses this issue via replaying its own prediction for past tasks during model training. In this work, we evaluate FT and LwF for class incremental learning in multi-organ segmentation using the publicly available AAPM dataset. We show that LwF can successfully retain knowledge on previous segmentations, however, its ability to learn a new class decreases with the addition of each class. To address this problem we propose an adversarial continual learning segmentation approach (ACLSeg), which disentangles feature space into task-specific and task-invariant features. This enables preservation of performance on past tasks and effective acquisition of new knowledge.

Involved external institutions

Technische Universität München (TUM) DE Germany (DE) Canon Medical Research Europe Ltd. GB United Kingdom (GB)

How to cite

APA:

Elskhawy, A., Lisowska, A., Keicher, M., Henry, J., Thomson, P., & Navab, N. (2020). Continual Class Incremental Learning for CT Thoracic Segmentation. In Shadi Albarqouni, Spyridon Bakas, Konstantinos Kamnitsas, M. Jorge Cardoso, Bennett Landman, Wenqi Li, Fausto Milletari, Nicola Rieke, Holger Roth, Daguang Xu, Ziyue Xu (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 106-116). Lima, PER: Springer Science and Business Media Deutschland GmbH.

MLA:

Elskhawy, Abdelrahman, et al. "Continual Class Incremental Learning for CT Thoracic Segmentation." Proceedings of the 2nd MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2020, and the 1st MICCAI Workshop on Distributed and Collaborative Learning, DCL 2020, held in conjunction with the Medical Image Computing and Computer Assisted Intervention, MICCAI 2020, Lima, PER Ed. Shadi Albarqouni, Spyridon Bakas, Konstantinos Kamnitsas, M. Jorge Cardoso, Bennett Landman, Wenqi Li, Fausto Milletari, Nicola Rieke, Holger Roth, Daguang Xu, Ziyue Xu, Springer Science and Business Media Deutschland GmbH, 2020. 106-116.

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