Transfer Learning via Parameter Regularization for Medical Image Segmentation

Nimrod Sagie, Hayit Greenspan, Jacob Goldberger

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

2 Scopus citations

Abstract

Transfer learning is a popular strategy to overcome the difficulties posed by limited training data. It uses the parameters of the source task to initialize the parameters of the target task. In this study, we cast transfer learning as a regularization procedure. In addition to initialization, we incorporate the source task parameters into the cost function used to train the target task. We regularize the learned parameters by penalizing them if they deviate too much from their initial values. We demonstrate the power of the proposed transfer learning scheme on the task of COVID-19 opacity https://www.overleaf.com/projectsegmentation. Specifically, we show that it can improve the segmentation of coronavirus lesions in chest CT scans.

Original languageEnglish
Title of host publication29th European Signal Processing Conference, EUSIPCO 2021 - Proceedings
PublisherEuropean Signal Processing Conference, EUSIPCO
Pages985-989
Number of pages5
ISBN (Electronic)9789082797060
DOIs
StatePublished - 2021
Event29th European Signal Processing Conference, EUSIPCO 2021 - Dublin, Ireland
Duration: 23 Aug 202127 Aug 2021

Publication series

NameEuropean Signal Processing Conference
Volume2021-August
ISSN (Print)2219-5491

Conference

Conference29th European Signal Processing Conference, EUSIPCO 2021
Country/TerritoryIreland
CityDublin
Period23/08/2127/08/21

Funding

FundersFunder number
Ministry of Science and Technology, Israel

    Keywords

    • Covid-19
    • Regularization
    • Segmentation
    • Transfer learning

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