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Paper Abstract and Keywords
Presentation 2022-12-16 14:40
Data Augmentation
Shumpei Takezaki (Kyushu Univ.), Kiyohito Tanaka (Kyoto Second Red Cross Hospital), Seiichi Uchida, Takeaki Kadota (Kyushu Univ.) PRMU2022-50
Abstract (in Japanese) (See Japanese page) 
(in English) Disease severity regression by a convolutional neural network (CNN) for medical images requires a sufficient number of image samples labeled with severity levels. Conditional generative adversarial network (cGAN)-based data augmentation (DA) is a possible solution, but it encounters two issues. The first issue is that existing cGANs cannot deal with real-valued severity levels as their conditions, and the second is that the severity of the generated images is not fully reliable. We propose continuous DA as a solution to the two issues. Our method uses continuous severity GAN to generate images at real-valued severity levels and dataset-disjoint multi-objective optimization to deal with the second issue. Our method was evaluated for estimating ulcerative colitis (UC) severity of endoscopic images and achieved higher classification performance than conventional DA methods.
Keyword (in Japanese) (See Japanese page) 
(in English) data augmentation / generative adversarial network / endoscopic images / / / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 314, PRMU2022-50, pp. 95-99, Dec. 2022.
Paper # PRMU2022-50 
Date of Issue 2022-12-08 (PRMU) 
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)
Download PDF PRMU2022-50

Conference Information
Committee PRMU  
Conference Date 2022-12-15 - 2022-12-16 
Place (in Japanese) (See Japanese page) 
Place (in English) Toyama International Conference Center 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To PRMU 
Conference Code 2022-12-PRMU 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Data Augmentation 
Sub Title (in English)  
Keyword(1) data augmentation  
Keyword(2) generative adversarial network  
Keyword(3) endoscopic images  
1st Author's Name Shumpei Takezaki  
1st Author's Affiliation Kyushu University (Kyushu Univ.)
2nd Author's Name Kiyohito Tanaka  
2nd Author's Affiliation Kyoto Second Red Cross Hospital (Kyoto Second Red Cross Hospital)
3rd Author's Name Seiichi Uchida  
3rd Author's Affiliation Kyushu University (Kyushu Univ.)
4th Author's Name Takeaki Kadota  
4th Author's Affiliation Kyushu University (Kyushu Univ.)
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Speaker Author-1 
Date Time 2022-12-16 14:40:00 
Presentation Time 15 minutes 
Registration for PRMU 
Paper # PRMU2022-50 
Volume (vol) vol.122 
Number (no) no.314 
Page pp.95-99 
Date of Issue 2022-12-08 (PRMU) 

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