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Paper Abstract and Keywords
Presentation 2022-07-08 16:00
[Short Paper] Unsupervised Domain Adaptation for Liver Tumor Detection in Multi-Phase CT images Using Adversarial Learning with Maximum Square Loss
Rahul Kumar Jain (Ritsumeikan Univ.), Takahiro Sato, Taro Watasue, Tomohiro Nakagawa (tiwaki), Yutaro Iwamoto (Ritsumeikan Univ.), Xianhua Han (Yamaguchi Univ.), Lanfen Lin, Hongjie Hu (Zhejiang Univ.), Xiang Ruan (tiwaki), Yen-Wei Chen (Ritsumeikan Univ.) MI2022-37
Abstract (in Japanese) (See Japanese page) 
(in English) Liver tumor detection in multi-phase CT images is essential in computer-aided diagnosis. Deep learning has been widely used in medical applications. In medical field, acquiring sufficient training data with high quality annotations is a major challenge. To solve the lack of training data issue, domain adaptation-based methods have been developed as a technique to bridge the domain gap across datasets with different feature characteristics and data distributions. This paper presents a domain adaptation-based method for detecting liver tumors in multi-phase CT images. To minimize the domain gap, we employ an adversarial learning scheme with the maximum square loss for mid-level output feature maps using an anchorless detector.
Keyword (in Japanese) (See Japanese page) 
(in English) Liver tumor detection / multi-phase CT image / domain adaptation / adversarial learning / maximum square loss / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 98, MI2022-37, pp. 22-23, July 2022.
Paper # MI2022-37 
Date of Issue 2022-07-01 (MI) 
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 MI2022-37

Conference Information
Committee MI  
Conference Date 2022-07-08 - 2022-07-09 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English) Medical imaging, recoginition, etc. 
Paper Information
Registration To MI 
Conference Code 2022-07-MI 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Unsupervised Domain Adaptation for Liver Tumor Detection in Multi-Phase CT images Using Adversarial Learning with Maximum Square Loss 
Sub Title (in English)  
Keyword(1) Liver tumor detection  
Keyword(2) multi-phase CT image  
Keyword(3) domain adaptation  
Keyword(4) adversarial learning  
Keyword(5) maximum square loss  
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Keyword(7)  
Keyword(8)  
1st Author's Name Rahul Kumar Jain  
1st Author's Affiliation Ritsumeikan University (Ritsumeikan Univ.)
2nd Author's Name Takahiro Sato  
2nd Author's Affiliation tiwaki Co. Ltd., Shiga, Japan (tiwaki)
3rd Author's Name Taro Watasue  
3rd Author's Affiliation tiwaki Co. Ltd., Shiga, Japan (tiwaki)
4th Author's Name Tomohiro Nakagawa  
4th Author's Affiliation tiwaki Co. Ltd., Shiga, Japan (tiwaki)
5th Author's Name Yutaro Iwamoto  
5th Author's Affiliation Ritsumeikan University (Ritsumeikan Univ.)
6th Author's Name Xianhua Han  
6th Author's Affiliation Yamaguchi University, Japan (Yamaguchi Univ.)
7th Author's Name Lanfen Lin  
7th Author's Affiliation Zhejiang University, Hangzhou, China (Zhejiang Univ.)
8th Author's Name Hongjie Hu  
8th Author's Affiliation Zhejiang University, Hangzhou, China (Zhejiang Univ.)
9th Author's Name Xiang Ruan  
9th Author's Affiliation tiwaki Co. Ltd., Shiga, Japan (tiwaki)
10th Author's Name Yen-Wei Chen  
10th Author's Affiliation Ritsumeikan University (Ritsumeikan Univ.)
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Speaker Author-1 
Date Time 2022-07-08 16:00:00 
Presentation Time 20 minutes 
Registration for MI 
Paper # MI2022-37 
Volume (vol) vol.122 
Number (no) no.98 
Page pp.22-23 
#Pages 2 
Date of Issue 2022-07-01 (MI) 

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