Supervised Domain Adaptation Using Gradients Transfer for Improved Medical Image Analysis

Shaya Goodman, Hayit Greenspan, Jacob Goldberger

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

Abstract

A well known problem in medical imaging is the performance degradation that occurs when using a model learned on source data, in a new site. Supervised Domain Adaptation (SDA) strategies that focus on this challenge, assume the availability of a limited number of annotated samples from the new site. A typical SDA approach is to pre-train the model on the source site and then fine-tune on the target site. Current research has thus mainly focused on which layers should be fine-tuned. Our approach is based on transferring also the gradients history of the pre-training phase to the fine-tuning phase. We present two schemes to transfer the gradients information to improve the generalization achieved during pre-training while fine-tuning the model. We show that our methods outperform the state-of-the-art with different levels of data scarcity from the target site, on multiple datasets and tasks.

Original languageEnglish
Title of host publicationDomain Adaptation and Representation Transfer - 4th MICCAI Workshop, DART 2022, Held in Conjunction with MICCAI 2022, Proceedings
EditorsKonstantinos Kamnitsas, Lisa Koch, Mobarakol Islam, Ziyue Xu, Jorge Cardoso, Qi Dou, Nicola Rieke, Sotirios Tsaftaris
PublisherSpringer Science and Business Media Deutschland GmbH
Pages23-32
Number of pages10
ISBN (Print)9783031168512
DOIs
StatePublished - 2022
Event4th MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2022, held in conjunction with the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022 - Singapore, Singapore
Duration: 22 Sep 202222 Sep 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13542 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference4th MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2022, held in conjunction with the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022
Country/TerritorySingapore
CitySingapore
Period22/09/2222/09/22

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

  • Gradient transfer
  • MRI segmentation
  • Site adaptation
  • Transfer learning
  • Xray classification

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