All Attention U-NET for Semantic Segmentation of Intracranial Hemorrhages In Head CT Images

Chia Shuo Chang, Tian Sheuan Chang, Jiun Lin Yan, Li Ko

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

2 Scopus citations

Abstract

Intracranial hemorrhages in head CT scans serve as a first line tool to help specialists diagnose different types. However, their types have diverse shapes in the same type but similar confusing shape, size and location between types. To solve this problem, this paper proposes an all attention U-Net. It uses channel attentions in the U-Net encoder side to enhance class specific feature extraction, and space and channel attentions in the U-Net decoder side for more accurate shape extraction and type classification. The simulation results show up to a 31.8% improvement compared to baseline, ResNet50 + U-Net, and better performance than in cases with limited attention.

Original languageEnglish
Title of host publicationBioCAS 2022 - IEEE Biomedical Circuits and Systems Conference
Subtitle of host publicationIntelligent Biomedical Systems for a Better Future, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages600-604
Number of pages5
ISBN (Electronic)9781665469173
DOIs
StatePublished - 2022
Event2022 IEEE Biomedical Circuits and Systems Conference, BioCAS 2022 - Taipei, Taiwan
Duration: 13 Oct 202215 Oct 2022

Publication series

NameBioCAS 2022 - IEEE Biomedical Circuits and Systems Conference: Intelligent Biomedical Systems for a Better Future, Proceedings

Conference

Conference2022 IEEE Biomedical Circuits and Systems Conference, BioCAS 2022
Country/TerritoryTaiwan
CityTaipei
Period13/10/2215/10/22

Keywords

  • Deep Learning
  • Head CT Scan
  • Intracranial Hemorrhage
  • Semantic Segmentation

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