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| 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 |
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| 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) | |
| Download PDF | PRMU2021-59 |
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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 |
| Keyword(6) | |
| Keyword(7) | |
| Keyword(8) | |
| 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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