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Paper Abstract and Keywords
Presentation 2021-12-17 15:30
[Short Paper] Prediction Model of Early Recurrence of Hepatocellular Carcinoma Based on Deep Learning with Attention Module
Weibin Wang (Ritsumeikan Univ.), Fang Wang, Qingqing Chen (Zhejiang Univ.), Yutaro Iwamoto (Ritsumeikan Univ.), Xianhua Han (Yamaguchi Univ.), Yen-wei Chen (Ritsumeikan Univ.) PRMU2021-59
Abstract (in Japanese) (See Japanese page) 
(in English) Early recurrence of hepatocyte carcinoma (HCC) will still lead to a decrease in the survival rate of patients who have accepted surgical treatment. Preoperative early recurrence prediction of patients with hepatocellular carcinoma can assist the doctor to formulate treatment plans and post-operative guidance of follow-up patients.In this paper, we propose a prediction model based on deep learning that contains intra phase attention and inter phase attention. Intra phase attention can focus on important information of different channels and spatial in the same phase, while inter phase attention can focus on important information between different phases. We also propose a fusion model to combine the image features with clinical data. Experimental results show that our fusion model has superior performance over the model using clinical data only or CT image only, achieving a prediction accuracy of 81.2% and the area under the curve (AUC) of 0.869 on our HCC database.
Keyword (in Japanese) (See Japanese page) 
(in English) Early recurrence / Deep learning / Multi-phase CT images / Intra phase attention / Inter phase attention / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 304, PRMU2021-59, pp. 195-198, Dec. 2021.
Paper # PRMU2021-59 
Date of Issue 2021-12-09 (PRMU) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
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Conference Information
Committee PRMU  
Conference Date 2021-12-16 - 2021-12-17 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To PRMU 
Conference Code 2021-12-PRMU 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Prediction Model of Early Recurrence of Hepatocellular Carcinoma Based on Deep Learning with Attention Module 
Sub Title (in English)  
Keyword(1) Early recurrence  
Keyword(2) Deep learning  
Keyword(3) Multi-phase CT images  
Keyword(4) Intra phase attention  
Keyword(5) Inter phase attention  
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1st Author's Name Weibin Wang  
1st Author's Affiliation Ritsumeikan University (Ritsumeikan Univ.)
2nd Author's Name Fang Wang  
2nd Author's Affiliation Zhejiang University (Zhejiang Univ.)
3rd Author's Name Qingqing Chen  
3rd Author's Affiliation Zhejiang University (Zhejiang Univ.)
4th Author's Name Yutaro Iwamoto  
4th Author's Affiliation Ritsumeikan University (Ritsumeikan Univ.)
5th Author's Name Xianhua Han  
5th Author's Affiliation Yamaguchi University (Yamaguchi Univ.)
6th Author's Name Yen-wei Chen  
6th Author's Affiliation Ritsumeikan University (Ritsumeikan Univ.)
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Speaker Author-1 
Date Time 2021-12-17 15:30:00 
Presentation Time 10 minutes 
Registration for PRMU 
Paper # PRMU2021-59 
Volume (vol) vol.121 
Number (no) no.304 
Page pp.195-198 
#Pages 4 
Date of Issue 2021-12-09 (PRMU) 

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