Transfer Learning with a Layer Dependent Regularization for Medical Image Segmentation

Nimrod Sagie, Hayit Greenspan, Jacob Goldberger*

*Corresponding author for this work

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

Abstract

Transfer learning is a machine learning technique where a model trained on one task is used to initialize the learning procedure of a second related task which has only a small amount of training data. Transfer learning can also be used as a regularization procedure by penalizing the learned parameters if they deviate too much from their initial values. In this study we show that the learned parameters move apart from the source task as the image processing progresses along the network layers. To cope with this behaviour we propose a transfer regularization method based on monotonically decreasing regularization coefficients. We demonstrate the power of the proposed regularized transfer learning scheme on COVID-19 opacity task. Specifically, we show that it can improve the segmentation of coronavirus lesions in chest CT scans.

Original languageEnglish
Title of host publicationMachine Learning in Medical Imaging - 12th International Workshop, MLMI 2021, Held in Conjunction with MICCAI 2021, Proceedings
EditorsChunfeng Lian, Xiaohuan Cao, Islem Rekik, Xuanang Xu, Pingkun Yan
PublisherSpringer Science and Business Media Deutschland GmbH
Pages161-170
Number of pages10
ISBN (Print)9783030875886
DOIs
StatePublished - 2021
Event12th International Workshop on Machine Learning in Medical Imaging, MLMI 2021, held in conjunction with 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021 - Virtual, Online
Duration: 27 Sep 202127 Sep 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12966 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference12th International Workshop on Machine Learning in Medical Imaging, MLMI 2021, held in conjunction with 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021
CityVirtual, Online
Period27/09/2127/09/21

Funding

FundersFunder number
Ministry of Science and Technology, Israel

    Keywords

    • COVID-19 opacity
    • Regularization
    • Transfer learning

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