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Paper Abstract and Keywords
Presentation 2019-09-04 16:20
Domain-adversarial multiple instance learning for subtype classification of malignant lymphoma
Daisuke Fukushima, Ryoichi Koga, Noriaki Hashimoto, Kaho Ko (Nagoya Inst. of Tech), Masato Nakaguro, Kei Kohno, Shigeo Nakamura (Nagoya Univ. Hospital), Hidekata Hontani (Nagoya Inst of Tech), Ichiro Takeuchi (Nagoya Inst. of Tech/RIKEN/NIMS) PRMU2019-15 MI2019-34
Abstract (in Japanese) (See Japanese page) 
(in English) We classify subtypes of malignant lymphoma using convolutional neural network with digital pathological images as input for computer-aided diagnosis. Generally, when the input image is large, the patch image is extracted from the entire sample. However, when we have no information for tumor regions in the sample, it is difficult that correct labels are apprppriately given to each patch image. We address such a problem using multiple instance learning. In addition, it is known that the variety of staining condition of the input pathological image affects the performance of image analysis. We confirmed that the classification accuracy was improved using domain-adversarial learning.
Keyword (in Japanese) (See Japanese page) 
(in English) pathological image / malignant lymphoma / onvolutional neural network / multiple instance learning / domain-adversarial learning / / /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 193, MI2019-34, pp. 19-24, Sept. 2019.
Paper # MI2019-34 
Date of Issue 2019-08-28 (PRMU, MI) 
ISSN Online edition: ISSN 2432-6380
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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 MI IPSJ-CVIM  
Conference Date 2019-09-04 - 2019-09-05 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To MI 
Conference Code 2019-09-PRMU-MI-CVIM 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Domain-adversarial multiple instance learning for subtype classification of malignant lymphoma 
Sub Title (in English)  
Keyword(1) pathological image  
Keyword(2) malignant lymphoma  
Keyword(3) onvolutional neural network  
Keyword(4) multiple instance learning  
Keyword(5) domain-adversarial learning  
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1st Author's Name Daisuke Fukushima  
1st Author's Affiliation Nagoya Institute of Technology (Nagoya Inst. of Tech)
2nd Author's Name Ryoichi Koga  
2nd Author's Affiliation Nagoya Institute of Technology (Nagoya Inst. of Tech)
3rd Author's Name Noriaki Hashimoto  
3rd Author's Affiliation Nagoya Institute of Technology (Nagoya Inst. of Tech)
4th Author's Name Kaho Ko  
4th Author's Affiliation Nagoya Institute of Technology (Nagoya Inst. of Tech)
5th Author's Name Masato Nakaguro  
5th Author's Affiliation Nagoya University Hospital (Nagoya Univ. Hospital)
6th Author's Name Kei Kohno  
6th Author's Affiliation Nagoya University Hospital (Nagoya Univ. Hospital)
7th Author's Name Shigeo Nakamura  
7th Author's Affiliation Nagoya University Hospital (Nagoya Univ. Hospital)
8th Author's Name Hidekata Hontani  
8th Author's Affiliation Nagoya Institute of Technology (Nagoya Inst of Tech)
9th Author's Name Ichiro Takeuchi  
9th Author's Affiliation Nagoya Institute of Technology/RIKEN/National Institute for Materials Science (Nagoya Inst. of Tech/RIKEN/NIMS)
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Speaker Author-1 
Date Time 2019-09-04 16:20:00 
Presentation Time 15 minutes 
Registration for MI 
Paper # PRMU2019-15, MI2019-34 
Volume (vol) vol.119 
Number (no) no.192(PRMU), no.193(MI) 
Page pp.19-24 
#Pages
Date of Issue 2019-08-28 (PRMU, MI) 


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