Attention-Guided Deep Domain Adaptation for Brain Dementia Identification with Multi-site Neuroimaging Data

Hao Guan, Erkun Yang, Pew Thian Yap, Dinggang Shen, Mingxia Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Deep learning has demonstrated its superiority in automated identification of brain dementia based on neuroimaging data, such as structural MRIs. Previous methods typically assume that multi-site data are sampled from the same distribution. Such an assumption may not hold in practice due to the data heterogeneity caused by different scanning parameters and subject populations in multiple imaging sites. Even though several deep domain adaptation methods have been proposed to mitigate data heterogeneity between sites, they usually require a portion of labeled target data for model training, and rarely consider the potentially different contributions of different brain regions to disease prognosis. To address these limitations, we propose an attention-guided deep domain adaptation (ADA) framework for brain dementia prognosis, which does not need label information of the target domain and can automatically identify discriminative locations in whole-brain MR images. The proposed ADA framework consists of three key components: 1) a feature encoding module for representation learning of input MR images, 2) an attention discovery module for automatically locating dementia-related discriminative regions in brain MRIs, and 3) a domain transfer module with adversarial learning for knowledge transfer between the source and target domains. Extensive experiments have been conducted on three benchmark neuroimaging datasets, with results suggesting the effectiveness of our method in both tasks of brain dementia identification and disease progression prediction.

Original languageEnglish
Title of host publicationDomain Adaptation and Representation Transfer, and Distributed and Collaborative Learning - 2nd MICCAI Workshop, DART 2020, and 1st MICCAI Workshop, DCL 2020, Held in Conjunction with MICCAI 2020, Proceedings
EditorsShadi Albarqouni, Spyridon Bakas, Konstantinos Kamnitsas, M. Jorge Cardoso, Bennett Landman, Wenqi Li, Fausto Milletari, Nicola Rieke, Holger Roth, Daguang Xu, Ziyue Xu
PublisherSpringer Science and Business Media Deutschland GmbH
Pages31-40
Number of pages10
ISBN (Print)9783030605476
DOIs
Publication statusPublished - 2020
Externally publishedYes
Event2nd 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, Peru
Duration: 2020 Oct 42020 Oct 8

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12444 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd 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
Country/TerritoryPeru
CityLima
Period20/10/420/10/8

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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