Focusing on Clinically Interpretable Features: Selective Attention Regularization for Liver Biopsy Image Classification

Chong Yin, Siqi Liu, Rui Shao, Pong Chi Yuen*

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

Abstract

Liver biopsy image analysis is the gold standard for early diagnosis of non-alcoholic fatty liver disease (NAFLD) worldwide. Deep neural networks offer an effective tool for image analysis. However, when applying deep learning methods to smaller histological image datasets, the model may be distracted by dominant normal tissues and ignore critical tissue alterations that pathologists focus on. In this paper, we propose a selective attention regularization module (SAttenReg) to mimic the diagnosis process of pathologists. Specifically, to explicitly encourage the model to focus on clinically interpretable features (e.g., nuclei and fat droplets), SAttenReg learns the attention map with the regularization of clinically interpretable features. Furthermore, with the different contributions of histological features, the model can selectively focus on different histological features based on the distribution of nuclei in each instance. Experiments conducted on the in-house Liver-NAS and public Biopsy4Grading biopsy image datasets show that our method achieves superior classification performance with promising localization results.
Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2021
Subtitle of host publication24th International Conference, Strasbourg, France, September 27 – October 1, 2021, Proceedings, Part V
EditorsMarleen de Bruijne, Philippe C. Cattin, Stéphane Cotin, Nicolas Padoy, Stefanie Speidel, Yefeng Zheng, Caroline Essert
PublisherSpringer Cham
Pages153-162
Number of pages10
Edition1st
ISBN (Electronic)9783030872403
ISBN (Print)9783030872397
DOIs
Publication statusPublished - 21 Sep 2021
Event24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021 - Strasbourg, France
Duration: 27 Sep 20211 Oct 2021

Publication series

NameLecture Notes in Computer Science
Volume12905
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349
NameImage Processing, Computer Vision, Pattern Recognition, and Graphics
Volume12905

Conference

Conference24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021
Country/TerritoryFrance
CityStrasbourg
Period27/09/211/10/21

User-Defined Keywords

  • Liver biopsy images
  • Selective attention regularization

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