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Paper Abstract and Keywords
Presentation 2018-09-21 10:00
[Short Paper] Computer-Aided Diagnosis of Liver Cancers Using Deep Learning with Fine-tuning
Weibin Wang (Ritsumeikan Univ.), Dong Liang, Lanfen Lin, Hongjie Hu, Qiaowei Zhang, Qingqing Chen (Zhejiang Univ.), Yutaro lwamoto, Xianhua Han, Yen-Wei Chen (Ritsumeikan Univ.) PRMU2018-57 IBISML2018-34
Abstract (in Japanese) (See Japanese page) 
(in English) Liver cancer is one of the leading causes of death world-wide. Computer-aided diagnosis plays an important role in liver lesion diagnosis (classification). Recently, several deep learning-based computer-aided diagnosis systems have been proposed for classification of liver lesions and their effectiveness have been demonstrated. The main challenge in deep learning-based medical image classification is the lack of annotated training samples. In this paper, we demonstrated that fine-tuning can significantly improve the liver lesion classification accuracy especially for the small training samples. We used the residual convolutional neural network (ResNet), which is the state-of-the-art network, as our baseline network for focal liver lesion classification on multi-phase CT images. The fine-tuning significantly improved the classification accuracy from 83.7% to 91.2%. The classification accuracy (91.2%) is higher than the accuracy of the state-of-the-art methods.
Keyword (in Japanese) (See Japanese page) 
(in English) ResNet / Liver cancer classification / Multi-phase CT / Fine-tuning / / / /  
Reference Info. IEICE Tech. Rep., vol. 118, no. 219, PRMU2018-57, pp. 139-140, Sept. 2018.
Paper # PRMU2018-57 
Date of Issue 2018-09-13 (PRMU, IBISML) 
ISSN Online edition: ISSN 2432-6380
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
Conference Date 2018-09-20 - 2018-09-21 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
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Paper Information
Registration To PRMU 
Conference Code 2018-09-PRMU-IBISML-CVIM 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Computer-Aided Diagnosis of Liver Cancers Using Deep Learning with Fine-tuning 
Sub Title (in English)  
Keyword(1) ResNet  
Keyword(2) Liver cancer classification  
Keyword(3) Multi-phase CT  
Keyword(4) Fine-tuning  
1st Author's Name Weibin Wang  
1st Author's Affiliation Ritsumeikan University (Ritsumeikan Univ.)
2nd Author's Name Dong Liang  
2nd Author's Affiliation Zhejiang University (Zhejiang Univ.)
3rd Author's Name Lanfen Lin  
3rd Author's Affiliation Zhejiang University (Zhejiang Univ.)
4th Author's Name Hongjie Hu  
4th Author's Affiliation Zhejiang University (Zhejiang Univ.)
5th Author's Name Qiaowei Zhang  
5th Author's Affiliation Zhejiang University (Zhejiang Univ.)
6th Author's Name Qingqing Chen  
6th Author's Affiliation Zhejiang University (Zhejiang Univ.)
7th Author's Name Yutaro lwamoto  
7th Author's Affiliation Ritsumeikan University (Ritsumeikan Univ.)
8th Author's Name Xianhua Han  
8th Author's Affiliation Ritsumeikan University (Ritsumeikan Univ.)
9th Author's Name Yen-Wei Chen  
9th Author's Affiliation Ritsumeikan University (Ritsumeikan Univ.)
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Speaker Author-1 
Date Time 2018-09-21 10:00:00 
Presentation Time 10 minutes 
Registration for PRMU 
Paper # PRMU2018-57, IBISML2018-34 
Volume (vol) vol.118 
Number (no) no.219(PRMU), no.220(IBISML) 
Page pp.139-140 
Date of Issue 2018-09-13 (PRMU, IBISML) 

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