Early diagnosis of autism disease by multi-channel CNNs

Guannan Li, Mingxia Liu, Quansen Sun, Dinggang Shen, Li Wang

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

    37 Citations (Scopus)

    Abstract

    Currently there are still no early biomarkers to detect infants with risk of autism spectrum disorder (ASD), which is mainly diagnosed based on behavior observations at three or four years old. Since intervention efforts may miss a critical developmental window after 2 years old, it is significant to identify imaging-based biomarkers for early diagnosis of ASD. Although some methods using magnetic resonance imaging (MRI) for brain disease prediction have been proposed in the last decade, few of them were developed for predicting ASD in early age. Inspired by deep multi-instance learning, in this paper, we propose a patch-level data-expanding strategy for multi-channel convolutional neural networks to automatically identify infants with risk of ASD in early age. Experiments were conducted on the National Database for Autism Research (NDAR), with results showing that our proposed method can significantly improve the performance of early diagnosis of ASD.

    Original languageEnglish
    Title of host publicationMachine Learning in Medical Imaging - 9th International Workshop, MLMI 2018, Held in Conjunction with MICCAI 2018, Proceedings
    EditorsMingxia Liu, Heung-Il Suk, Yinghuan Shi
    PublisherSpringer Verlag
    Pages303-309
    Number of pages7
    ISBN (Print)9783030009182
    DOIs
    Publication statusPublished - 2018
    Event9th International Workshop on Machine Learning in Medical Imaging, MLMI 2018 held in conjunction with the 21st International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018 - Granada, Spain
    Duration: 2018 Sept 162018 Sept 16

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume11046 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Other

    Other9th International Workshop on Machine Learning in Medical Imaging, MLMI 2018 held in conjunction with the 21st International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018
    Country/TerritorySpain
    CityGranada
    Period18/9/1618/9/16

    Bibliographical note

    Funding Information:
    This work was supported in part by National Institutes of Health grants MH109773, MH100217, MH070890, EB006733, EB008374, EB009634, AG041721, AG042599, MH088520, MH108914, MH107815, and MH113255.

    Publisher Copyright:
    © Springer Nature Switzerland AG 2018.

    Keywords

    • Autism
    • Convolutional neural network
    • Deep multi-instance learning
    • Early diagnosis

    ASJC Scopus subject areas

    • Theoretical Computer Science
    • General Computer Science

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