3D deformable convolutions for MRI classification

Marina Pominova, Ekaterina Kondrateva, Maksim Sharaev, Alexander Bernstein, Sergey Pavlov, Evgeny Burnaev

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    7 Citations (Scopus)

    Abstract

    Deep learning convolution neural networks have proved to be a powerful tool for MRI analysis. In current work, we explore the potential of the deformable convolution deep neural network layers for MRI data classification. We propose new 3D deformable convolutions (d-convolutions), implement them in VoxResNet architecture and apply for structural MRI data classification. We show that 3D d-convolutions outperform standard ones and are effective for unprocessed 3D MR images being robust to particular geometrical properties of the data. Firstly proposed dVoxResNet architecture exhibits high potential for the use in MRI data classification.

    Original languageEnglish
    Title of host publicationProceedings - 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019
    EditorsM. Arif Wani, Taghi M. Khoshgoftaar, Dingding Wang, Huanjing Wang, Naeem Seliya
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages1710-1716
    Number of pages7
    ISBN (Electronic)9781728145495
    DOIs
    Publication statusPublished - Dec 2019
    Event18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019 - Boca Raton, United States
    Duration: 16 Dec 201919 Dec 2019

    Publication series

    NameProceedings - 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019

    Conference

    Conference18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019
    Country/TerritoryUnited States
    CityBoca Raton
    Period16/12/1919/12/19

    Keywords

    • Biomarkers
    • Bipolar disorder
    • Convolutional neural networks
    • Deep learning
    • MRI
    • Neuroimaging
    • Schizophrenia

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