Self-attentive normalization for automated gleason grading system

Hong Kyu Shin, Sung Hoo Hong, Yeong Jin Choi, Yong Goo Shin, Seung Park, Sung Jea Ko

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

Abstract

Recently, convolutional neural networks (CNNs)- based automated Gleason grading system for prostate cancer has been widely researched. However, these systems still need further improvement to achieve pathologist-level performance. To this end, this paper introduces a novel self-attentive normalization (SAN) which is the first work to employ the attention mechanism for the automated Gleason grading system. Unlike conventional normalization techniques, e.g. batch normalization and instance normalization, which learn a single affine transformation, the proposed method can learn the elementwise affine transformation to focus on more informative regions of the feature map. Since SAN requires a small number of extra learning parameters, it can be integrated into existing automated Gleason grading systems seamlessly with negligible overheads. Extensive quantitative evaluations show that, by applying SAN to various CNN architectures, the diagnostic accuracy can be significantly improved. For instance, we raise VGG-16's diagnostic accuracy from 73.99% to 79.16% on the Harvard Dataverse.

Original languageEnglish
Title of host publication2020 IEEE Region 10 Conference, TENCON 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1101-1105
Number of pages5
ISBN (Electronic)9781728184555
DOIs
Publication statusPublished - 2020 Nov 16
Event2020 IEEE Region 10 Conference, TENCON 2020 - Virtual, Osaka, Japan
Duration: 2020 Nov 162020 Nov 19

Publication series

NameIEEE Region 10 Annual International Conference, Proceedings/TENCON
Volume2020-November
ISSN (Print)2159-3442
ISSN (Electronic)2159-3450

Conference

Conference2020 IEEE Region 10 Conference, TENCON 2020
Country/TerritoryJapan
CityVirtual, Osaka
Period20/11/1620/11/19

Keywords

  • Automated Gleason grading system
  • Convolutional neural networks
  • Self-attentive normalization

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering

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