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

doi:10.1109/BioCAS54905.2022.9948588

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

研究成果: Conference contribution › 同行評審

2 引文斯高帕斯（Scopus）

摘要

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.

原文	English
主出版物標題	BioCAS 2022 - IEEE Biomedical Circuits and Systems Conference
主出版物子標題	Intelligent Biomedical Systems for a Better Future, Proceedings
發行者	Institute of Electrical and Electronics Engineers Inc.
頁面	600-604
頁數	5
ISBN（電子）	9781665469173
DOIs	https://doi.org/10.1109/BioCAS54905.2022.9948588
出版狀態	Published - 2022
事件	2022 IEEE Biomedical Circuits and Systems Conference, BioCAS 2022 - Taipei, 台灣持續時間: 13 10月 2022 → 15 10月 2022

出版系列

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

Conference

Conference	2022 IEEE Biomedical Circuits and Systems Conference, BioCAS 2022
國家/地區	台灣
城市	Taipei
期間	13/10/22 → 15/10/22

存取文件

10.1109/BioCAS54905.2022.9948588

其它文件與鏈接

Link to publication in Scopus

引用此

Shuo Chang, C., Sheuan Chang, T., Lin Yan, J., & Ko, L. (2022). All Attention U-NET for Semantic Segmentation of Intracranial Hemorrhages In Head CT Images. 於 BioCAS 2022 - IEEE Biomedical Circuits and Systems Conference: Intelligent Biomedical Systems for a Better Future, Proceedings (頁 600-604). (BioCAS 2022 - IEEE Biomedical Circuits and Systems Conference: Intelligent Biomedical Systems for a Better Future, Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BioCAS54905.2022.9948588

Shuo Chang, Chia ; Sheuan Chang, Tian ; Lin Yan, Jiun 等. / All Attention U-NET for Semantic Segmentation of Intracranial Hemorrhages In Head CT Images. BioCAS 2022 - IEEE Biomedical Circuits and Systems Conference: Intelligent Biomedical Systems for a Better Future, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2022. 頁 600-604 (BioCAS 2022 - IEEE Biomedical Circuits and Systems Conference: Intelligent Biomedical Systems for a Better Future, Proceedings).

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title = "All Attention U-NET for Semantic Segmentation of Intracranial Hemorrhages In Head CT Images",

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.",

keywords = "Deep Learning, Head CT Scan, Intracranial Hemorrhage, Semantic Segmentation",

author = "{Shuo Chang}, Chia and {Sheuan Chang}, Tian and {Lin Yan}, Jiun and Li Ko",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE Biomedical Circuits and Systems Conference, BioCAS 2022 ; Conference date: 13-10-2022 Through 15-10-2022",

year = "2022",

doi = "10.1109/BioCAS54905.2022.9948588",

language = "English",

series = "BioCAS 2022 - IEEE Biomedical Circuits and Systems Conference: Intelligent Biomedical Systems for a Better Future, Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "600--604",

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}

Shuo Chang, C, Sheuan Chang, T, Lin Yan, J & Ko, L 2022, All Attention U-NET for Semantic Segmentation of Intracranial Hemorrhages In Head CT Images. 於 BioCAS 2022 - IEEE Biomedical Circuits and Systems Conference: Intelligent Biomedical Systems for a Better Future, Proceedings. BioCAS 2022 - IEEE Biomedical Circuits and Systems Conference: Intelligent Biomedical Systems for a Better Future, Proceedings, Institute of Electrical and Electronics Engineers Inc., 頁 600-604, 2022 IEEE Biomedical Circuits and Systems Conference, BioCAS 2022, Taipei, 台灣, 13/10/22. https://doi.org/10.1109/BioCAS54905.2022.9948588

All Attention U-NET for Semantic Segmentation of Intracranial Hemorrhages In Head CT Images. / Shuo Chang, Chia; Sheuan Chang, Tian; Lin Yan, Jiun 等.
BioCAS 2022 - IEEE Biomedical Circuits and Systems Conference: Intelligent Biomedical Systems for a Better Future, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2022. p. 600-604 (BioCAS 2022 - IEEE Biomedical Circuits and Systems Conference: Intelligent Biomedical Systems for a Better Future, Proceedings).

研究成果: Conference contribution › 同行評審

TY - GEN

T1 - All Attention U-NET for Semantic Segmentation of Intracranial Hemorrhages In Head CT Images

AU - Shuo Chang, Chia

AU - Sheuan Chang, Tian

AU - Lin Yan, Jiun

AU - Ko, Li

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

KW - Deep Learning

KW - Head CT Scan

KW - Intracranial Hemorrhage

KW - Semantic Segmentation

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U2 - 10.1109/BioCAS54905.2022.9948588

DO - 10.1109/BioCAS54905.2022.9948588

M3 - Conference contribution

AN - SCOPUS:85142936553

T3 - BioCAS 2022 - IEEE Biomedical Circuits and Systems Conference: Intelligent Biomedical Systems for a Better Future, Proceedings

SP - 600

EP - 604

BT - BioCAS 2022 - IEEE Biomedical Circuits and Systems Conference

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2022 IEEE Biomedical Circuits and Systems Conference, BioCAS 2022

Y2 - 13 October 2022 through 15 October 2022

ER -

Shuo Chang C, Sheuan Chang T, Lin Yan J, Ko L. All Attention U-NET for Semantic Segmentation of Intracranial Hemorrhages In Head CT Images. 於 BioCAS 2022 - IEEE Biomedical Circuits and Systems Conference: Intelligent Biomedical Systems for a Better Future, Proceedings. Institute of Electrical and Electronics Engineers Inc. 2022. p. 600-604. (BioCAS 2022 - IEEE Biomedical Circuits and Systems Conference: Intelligent Biomedical Systems for a Better Future, Proceedings). doi: 10.1109/BioCAS54905.2022.9948588

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

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