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Paper Abstract and Keywords
Presentation 2020-12-18 16:20
[Short Paper] Case Discrimination: Self-supervised Learning for classification of Medical Image
Haohua Dong, Yutaro Iwamoto (Ritsumeikan Univ.), Xianhua Han (Yamaguchi Univ.), Lanfen Lin (Zhejiang Univ.), Hongjie Hu, Xiujun Cai (Sir Run Run Shaw Hospital), Yen-Wei Chen (Ritsumeikan Univ.) PRMU2020-64
Abstract (in Japanese) (See Japanese page) 
(in English) Deep Learning provides exciting solutions to problems in medical image analysis and is regarded as a key method for future applications. However, only a few annotated medical image datasets exist compared to numerous natural images. In this paper, we propose a model to investigate transfer learning by self-supervised learning using medical images. It is widely known that the results of Computerized Tomography (CT) scan are 3D volume images. There are lots of slices in CT or Magnetic Resonance Imaging scan images. So why not make these slices to a class? It is imperative to formulate this intuition as a self-supervised feature learning at the case-level. Our results of the experiment demonstrate that, under self-supervised feature learning settings, our method surpasses the transfer learning using ImageNet on classification. By experiment using unannotated dataset, our method is also remarkable for consistently improving test performance with a few annotated data. By fine-tuning the learned feature, we further obtain competitive results for self-supervised learning and classification tasks.
Keyword (in Japanese) (See Japanese page) 
(in English) Self-supervised learning / Medical image processing / Image classification / / / / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 300, PRMU2020-64, pp. 151-155, Dec. 2020.
Paper # PRMU2020-64 
Date of Issue 2020-12-10 (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 2020-12-17 - 2020-12-18 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Transfer learning and few shot learning 
Paper Information
Registration To PRMU 
Conference Code 2020-12-PRMU 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Case Discrimination: Self-supervised Learning for classification of Medical Image 
Sub Title (in English)  
Keyword(1) Self-supervised learning  
Keyword(2) Medical image processing  
Keyword(3) Image classification  
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1st Author's Name Haohua Dong  
1st Author's Affiliation Ritsumeikan University (Ritsumeikan Univ.)
2nd Author's Name Yutaro Iwamoto  
2nd Author's Affiliation Ritsumeikan University (Ritsumeikan Univ.)
3rd Author's Name Xianhua Han  
3rd Author's Affiliation Yamaguchi University (Yamaguchi Univ.)
4th Author's Name Lanfen Lin  
4th Author's Affiliation Zhejiang University (Zhejiang Univ.)
5th Author's Name Hongjie Hu  
5th Author's Affiliation Sir Run Run Shaw Hospital (Sir Run Run Shaw Hospital)
6th Author's Name Xiujun Cai  
6th Author's Affiliation Sir Run Run Shaw Hospital (Sir Run Run Shaw Hospital)
7th Author's Name Yen-Wei Chen  
7th Author's Affiliation Ritsumeikan University (Ritsumeikan Univ.)
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Speaker Author-1 
Date Time 2020-12-18 16:20:00 
Presentation Time 10 minutes 
Registration for PRMU 
Paper # PRMU2020-64 
Volume (vol) vol.120 
Number (no) no.300 
Page pp.151-155 
#Pages 5 
Date of Issue 2020-12-10 (PRMU) 

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