Automated ROI Selection for Fatty Liver Disease Severity Classification Using Attention Map Analysis-Reference-Cited by-同舟云学术

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Automated ROI Selection for Fatty Liver Disease Severity Classification Using Attention Map Analysis

Published:2023-06 Issue: Volume: Page:
ISSN:
Container-title:2023 Sixth International Symposium on Computer, Consumer and Control (IS3C)
language:
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Author:

Wang Hao-Jen¹,Cheng Kai-Wen¹,Ye Hung²,Lin Hong-Kun²,Chen Jin-De³,Chen Tsung-Po⁴,Lee Chia-Yen¹

Affiliation:

1. National United University,Department of Electrical Engineering,Miaoli,Taiwan

2. Institute of Biomedical Engineering, National Yang Ming Chiao Tung University,Hsinchu,Taiwan

3. National Taiwan University Hospital,Bei-Hu Branch,Department of Internal Medicine,Taipei,Taiwan

4. China Medical University Hospital,Department of Community and Family Medicine,Taichung,Taiwan

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10219186/10219411/10219457.pdf?arnumber=10219457

Reference15 articles.

1. Data mining framework for fatty liver disease classification in ultrasound: A hybrid feature extraction paradigm

2. Computer assisted characterization of liver tissue using image texture analysis techniques on B-scan images

3. RepVGG: Making VGG-style ConvNets Great Again

4. An information fusion based method for liver classification using texture analysis of ultrasound images

5. Liver steatosis classification using high-frequency ultrasound

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