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Paper Abstract and Keywords
Presentation 2023-03-06 16:25
Domain Generalization for Mitosis Detection
Yusuke Kurose (U-Tokyo), Yuko Hiroshima, Hiroshi Nanjo (Akita-U), Tatsuya Harada (U-Tokyo) MI2022-93
Abstract (in Japanese) (See Japanese page) 
(in English) In pathological image analysis, it is known that the existence of inter-institutional differences (domain shift) causes a significant loss of accuracy when a model trained on data from one institution is applied to data from another institution. Domain adaptation and domain generalization are tasks that deal with such problems. In this study, we deal with domain generalization in consideration of real-world clinical applications and develop a method for mitosis detection in pathology. Many methods for pathology can be applied to a wider range of domains by creating stains that do not exist in the domain of the training data through data augmentation. However, such methods can only deal with inter-facility differences due to staining. In this study, we developed a method that is robust to inter-facility differences by having the model learn facility-independent features in the feature dimension. The effectiveness of the method was confirmed through experiments using pathology images collected from multiple hospitals.
Keyword (in Japanese) (See Japanese page) 
(in English) Pathology / Mitosis Detection / Domain Generalization / / / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 417, MI2022-93, pp. 100-104, March 2023.
Paper # MI2022-93 
Date of Issue 2023-02-27 (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 MI2022-93

Conference Information
Committee MI  
Conference Date 2023-03-06 - 2023-03-07 
Place (in Japanese) (See Japanese page) 
Place (in English) OKINAWA SEINENKAIKAN 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To MI 
Conference Code 2023-03-MI 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Domain Generalization for Mitosis Detection 
Sub Title (in English)  
Keyword(1) Pathology  
Keyword(2) Mitosis Detection  
Keyword(3) Domain Generalization  
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1st Author's Name Yusuke Kurose  
1st Author's Affiliation The University of Tokyo (U-Tokyo)
2nd Author's Name Yuko Hiroshima  
2nd Author's Affiliation Akita University (Akita-U)
3rd Author's Name Hiroshi Nanjo  
3rd Author's Affiliation Akita University (Akita-U)
4th Author's Name Tatsuya Harada  
4th Author's Affiliation The University of Tokyo (U-Tokyo)
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Speaker Author-1 
Date Time 2023-03-06 16:25:00 
Presentation Time 13 minutes 
Registration for MI 
Paper # MI2022-93 
Volume (vol) vol.122 
Number (no) no.417 
Page pp.100-104 
#Pages 5 
Date of Issue 2023-02-27 (MI) 

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