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Paper Abstract and Keywords
Presentation 2021-07-09 11:00
[Short Paper] Construction of Subtype Classifier for Malignant Lymphoma based on H&E-stained Images using Immuno-stainning Data
Yuki Hirono (NIT), Noriaki Hashimoto (RIKEN), Kugler Mauricio, Tatsuya Yokota (NIT), Miharu Nagaishi (Kurume Univ.), Hiroaki Miyoshi, Koichi Oshima (Kurume Univ./JSP), Ichiro Takeuchi (NIT/RIKEN), Hidekata Hontani (NIT) MI2021-16
Abstract (in Japanese) (See Japanese page) 
(in English) In pathological diagnosis of malignant lymphoma, a HE image is observed at first and then a set of immunostained images are observed to determine the subtype. Observing the HE image, pathologists infer the candidates of the subtypes, determine the set of immunostains needed for identifying the subtype, and finally identify the subtype by observing if the specimen is positively stained by each of the immunostains. The information from HE images is the candidates of the subtypes and a set of immunostains needed for the subtype identification. The proposed method hence constructs a decision tree for inferring the set of subtype candidates and the set of the immunostains from an input HE image. Each node of the decision tree infers if a set of specific immunostains is needed for the subtype identification. We used a set of pairs of a HE image and the text data that describes the diagnosed subtype and the set of immunostains. The multiple-instance learning (MIL) is employed for the training as we have no labels indicating the cancerous regions in the HE images. The outline of the proposed method and some results of initial studies are reported.
Keyword (in Japanese) (See Japanese page) 
(in English) pathological image / malignant lymphoma / H&E-stained image / immuno-staining data / neural network / multiple instance learning / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 98, MI2021-16, pp. 31-32, July 2021.
Paper # MI2021-16 
Date of Issue 2021-07-01 (MI) 
ISSN Online edition: ISSN 2432-6380
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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 MI  
Conference Date 2021-07-08 - 2021-07-09 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Medical imaging, physics, and recognition 
Paper Information
Registration To MI 
Conference Code 2021-07-MI 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Construction of Subtype Classifier for Malignant Lymphoma based on H&E-stained Images using Immuno-stainning Data 
Sub Title (in English)  
Keyword(1) pathological image  
Keyword(2) malignant lymphoma  
Keyword(3) H&E-stained image  
Keyword(4) immuno-staining data  
Keyword(5) neural network  
Keyword(6) multiple instance learning  
Keyword(7)  
Keyword(8)  
1st Author's Name Yuki Hirono  
1st Author's Affiliation Nagoya Institute of Technology (NIT)
2nd Author's Name Noriaki Hashimoto  
2nd Author's Affiliation Institute of Physical and Chemical Research (RIKEN)
3rd Author's Name Kugler Mauricio  
3rd Author's Affiliation Nagoya Institute of Technology (NIT)
4th Author's Name Tatsuya Yokota  
4th Author's Affiliation Nagoya Institute of Technology (NIT)
5th Author's Name Miharu Nagaishi  
5th Author's Affiliation Kurume Universiy (Kurume Univ.)
6th Author's Name Hiroaki Miyoshi  
6th Author's Affiliation Kurume Universiy/The Japanese Society of Pathology (Kurume Univ./JSP)
7th Author's Name Koichi Oshima  
7th Author's Affiliation Kurume Universiy/The Japanese Society of Pathology (Kurume Univ./JSP)
8th Author's Name Ichiro Takeuchi  
8th Author's Affiliation Nagoya Institute of Technology/Institute of Physical and Chemical Research (NIT/RIKEN)
9th Author's Name Hidekata Hontani  
9th Author's Affiliation Nagoya Institute of Technology (NIT)
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Speaker Author-1 
Date Time 2021-07-09 11:00:00 
Presentation Time 30 minutes 
Registration for MI 
Paper # MI2021-16 
Volume (vol) vol.121 
Number (no) no.98 
Page pp.31-32 
#Pages
Date of Issue 2021-07-01 (MI) 


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