Interpretation of 3D CNNs for Brain MRI Data Classification

Maxim Kan, Ruslan Aliev, Anna Rudenko, Nikita Drobyshev, Nikita Petrashen, Ekaterina Kondrateva, Maxim Sharaev, Alexander Bernstein, Evgeny Burnaev

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

Abstract

Deep learning shows high potential for many medical image analysis tasks. Neural networks can work with full-size data without extensive preprocessing and feature generation and, thus, information loss. Recent work has shown that the morphological difference in specific brain regions can be found on MRI with the means of Convolution Neural Networks (CNN). However, interpretation of the existing models is based on a region of interest and can not be extended to voxel-wise image interpretation on a whole image. In the current work, we consider the classification task on a large-scale open-source dataset of young healthy subjects—an exploration of brain differences between men and women. In this paper, we extend the previous findings in gender differences from diffusion-tensor imaging on T1 brain MRI scans. We provide the voxel-wise 3D CNN interpretation comparing the results of three interpretation methods: Meaningful Perturbations, Grad CAM and Guided Backpropagation, and contribute with the open-source library.

Original languageEnglish
Title of host publicationRecent Trends in Analysis of Images, Social Networks and Texts - 9th International Conference, AIST 2020, Revised Supplementary Proceedings
EditorsWil M. van der Aalst, Vladimir Batagelj, Alexey Buzmakov, Dmitry I. Ignatov, Anna Kalenkova, Michael Khachay, Olessia Koltsova, Andrey Kutuzov, Sergei O. Kuznetsov, Irina A. Lomazova, Natalia Loukachevitch, Ilya Makarov, Amedeo Napoli, Alexander Panchenko, Panos M. Pardalos, Marcello Pelillo, Andrey V. Savchenko, Elena Tutubalina
PublisherSpringer Science and Business Media Deutschland GmbH
Pages229-241
Number of pages13
ISBN (Print)9783030712136
DOIs
Publication statusPublished - 2021
Event9th International Conference on Analysis of Images, Social Networks, and Texts, AIST 2020 - Virtual, Online
Duration: 15 Oct 202016 Oct 2020

Publication series

NameCommunications in Computer and Information Science
Volume1357 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference9th International Conference on Analysis of Images, Social Networks, and Texts, AIST 2020
CityVirtual, Online
Period15/10/2016/10/20

Keywords

  • 3D CNN
  • CNN interpretation
  • Deep learning
  • Grad CAM
  • Guided Back-propagation
  • Meaningful perturbation
  • MRI

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