Inter-modality dependence induced data recovery for MCI conversion prediction

Tao Zhou, Kim Han Thung, Yu Zhang, Huazhu Fu, Jianbing Shen, Dinggang Shen, Ling Shao

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

Abstract

Learning complementary information from multi-modality data often improves diagnostic performance of brain disorders. However, it is challenging to obtain this complementary information when the data are incomplete. Existing methods, such as low-rank matrix completion (which imputes the missing data) and multi-task learning (which restructures the problem into the joint learning of multiple tasks, with each task associated with a subset of complete data), simply concatenate features from different modalities without considering their underlying correlations. Furthermore, most methods conduct multi-modality fusion and prediction model learning in separated steps, which may render to a sub-optimal solution. To address these issues, we propose a novel diagnostic model that integrates missing data recovery, latent space learning and prediction model learning into a unified framework. Specifically, we first recover the missing modality by maximizing the dependency among different modalities. Then, we further exploit the modality correlation by projecting different modalities into a common latent space. Besides, we employ an l1 -norm to our loss function to mitigate the influence of sample outliers. Finally, we map the learned latent representation into the label space. All these tasks are learned iteratively in a unified framework, where the label information (from the training samples) can also inherently guide the missing modality recovery. Experimental results on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset show the effectiveness of our method.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings
EditorsDinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou
PublisherSpringer
Pages186-195
Number of pages10
ISBN (Print)9783030322502
DOIs
Publication statusPublished - 2019 Jan 1
Externally publishedYes
Event22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 - Shenzhen, China
Duration: 2019 Oct 132019 Oct 17

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11767 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
CountryChina
CityShenzhen
Period19/10/1319/10/17

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Zhou, T., Thung, K. H., Zhang, Y., Fu, H., Shen, J., Shen, D., & Shao, L. (2019). Inter-modality dependence induced data recovery for MCI conversion prediction. In D. Shen, P-T. Yap, T. Liu, T. M. Peters, A. Khan, L. H. Staib, C. Essert, & S. Zhou (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings (pp. 186-195). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11767 LNCS). Springer. https://doi.org/10.1007/978-3-030-32251-9_21