Unsupervised Conditional Consensus Adversarial Network for Brain Disease Identification with Structural MRI

Jing Zhang, Mingxia Liu, Yongsheng Pan, Dinggang Shen

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

Abstract

Effective utilization of multi-domain data for brain disease identification has recently attracted increasing attention since a large number of subjects from multiple domains could be beneficial for investigating the pathological changes of disease-affected brains. Previous machine learning methods often suffer from inter-domain data heterogeneity caused by different scanning parameters. Although several deep learning methods have been developed, they usually assume that the source classifier can be directly transferred to the target (i.e., to-be-analyzed) domain upon the learned domain-invariant features, thus ignoring the shift in data distributions across different domains. Also, most of them rely on fully-labeled data in both target and source domains for model training, while labeled target data are generally unavailable. To this end, we present an Unsupervised Conditional consensus Adversarial Network (UCAN) for deep domain adaptation, which can learn the disease classifier from the labeled source domain and adapt to a different target domain (without any label information). The UCAN model contains three major components: (1) a feature extraction module for learning discriminate representations from the input MRI, (2) a cycle feature adaptation module to assist feature and classifier adaptation between the source and target domains, and (3) a classification module for disease identification. Experimental results on 1, 506 subjects from ADNI1 (with 1.5 T structural MRI) and ADNI2 (with 3.0 T structural MRI) have demonstrated the effectiveness of the proposed UCAN method in brain disease identification, compared with state-of-the-art approaches.

Original languageEnglish
Title of host publicationMachine Learning in Medical Imaging - 10th International Workshop, MLMI 2019, Held in Conjunction with MICCAI 2019, Proceedings
EditorsHeung-Il Suk, Mingxia Liu, Chunfeng Lian, Pingkun Yan
PublisherSpringer
Pages391-399
Number of pages9
ISBN (Print)9783030326913
DOIs
Publication statusPublished - 2019
Externally publishedYes
Event10th International Workshop on Machine Learning in Medical Imaging, MLMI 2019 held in conjunction with the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 - Shenzhen, China
Duration: 2019 Oct 132019 Oct 13

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11861 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference10th International Workshop on Machine Learning in Medical Imaging, MLMI 2019 held in conjunction with the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
CountryChina
CityShenzhen
Period19/10/1319/10/13

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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