Joint Neuroimage Synthesis and Representation Learning for Conversion Prediction of Subjective Cognitive Decline

Yunbi Liu, Yongsheng Pan, Wei Yang, Zhenyuan Ning, Ling Yue, Mingxia Liu, Dinggang Shen

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

Abstract

Predicting the progression of preclinical Alzheimer’s disease (AD) such as subjective cognitive decline (SCD) is fundamental for the effective intervention of pathological cognitive decline. Even though multimodal neuroimaging has been widely used in automated AD diagnosis, there are few studies dedicated to SCD progression prediction, due to challenges of incomplete and limited data. To this end, we propose a Joint neuroimage Synthesis and Representation Learning (JSRL) framework with transfer learning for SCD conversion prediction using incomplete multimodal neuroimaging data. Specifically, JSRL consists of two major components: 1) a generative adversarial network for synthesizing missing neuroimaging data, and 2) a classification network for learning neuroimage representations and predicting the progression of SCD. These two subnetworks share the same feature encoding module, encouraging that the to-be-generated representations are prediction-oriented and also the underlying association among multimodal images can be effectively modeled for accurate prediction. To handle the limited data problem, we further leverage both image synthesis and prediction models learned from a large-scale ADNI database (with MRI and PET acquired from 863 subjects) to a small-scale SCD database (with only MRI acquired from 113 subjects) in a transfer learning manner. Experimental results show that the proposed JSRL can synthesize reasonable PET scans and is superior to several state-of-the-art methods in SCD conversion prediction.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings
EditorsAnne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz
PublisherSpringer Science and Business Media Deutschland GmbH
Pages583-592
Number of pages10
ISBN (Print)9783030597276
DOIs
Publication statusPublished - 2020
Event23rd International Conference on 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)
Volume12267 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020
CountryPeru
CityLima
Period20/10/420/10/8

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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