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Paper Abstract and Keywords
Presentation 2022-01-26 15:00
[Special Talk] TBA
Ryoma Bise (Kyushu Univ.) MI2021-66
Abstract (in Japanese) (See Japanese page) 
(in English) Supervised learning (e.g., deep learning) has been used for various tasks in biomedical image analysis. While supervised learning requires a lot of supervised data to achieve high performance in general, annotation costs in biomedical image analysis are much higher than those in general image analysis since it requires expert’s knowledge for annotation. Therefore, even if there is a large amount of data, a few data are only annotated and learning is often performed using a few supervised data. Semi-supervised learning tries to use unlabeled data in addition to the labeled data for improvement of performance. In addition, although it is not direct supervision for a task, there is related information about the task, which can be easily obtained without additional human annotation. Such information is often useful for learning as weak labels (weakly-supervised learning). Domain shift problem is also one of the major problems that lack labeled data. For example, model parameters trained using a dataset collected from a specific hospital (hospital A) cannot work well for the data collected from a different hospital (hospital B) due to different imaging setups. In order to address domain shift problem, domain adaptation methods use unlabeled data in hospital B in addition to the labeled data. In biomedical image analysis, there are many such problems. In this presentation, I will show effective semi-, weakly-supervised learning methods for cell image analysis.
Keyword (in Japanese) (See Japanese page) 
(in English) Cell image analysis / Semi-supervised learning / Weakly-supervised learning / Unsupervised domain adaptation / / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 347, MI2021-66, pp. 88-88, Jan. 2022.
Paper # MI2021-66 
Date of Issue 2022-01-18 (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 MI2021-66

Conference Information
Committee MI  
Conference Date 2022-01-25 - 2022-01-27 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To MI 
Conference Code 2022-01-MI 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) TBA 
Sub Title (in English)  
Keyword(1) Cell image analysis  
Keyword(2) Semi-supervised learning  
Keyword(3) Weakly-supervised learning  
Keyword(4) Unsupervised domain adaptation  
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1st Author's Name Ryoma Bise  
1st Author's Affiliation Kyushu University (Kyushu Univ.)
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Speaker Author-1 
Date Time 2022-01-26 15:00:00 
Presentation Time 55 minutes 
Registration for MI 
Paper # MI2021-66 
Volume (vol) vol.121 
Number (no) no.347 
Page p.88 
#Pages 1 
Date of Issue 2022-01-18 (MI) 

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