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Paper Abstract and Keywords
Presentation 2026-03-06 14:39
[Short Paper] Uncertainty Quantification for Multi-Class Classification of Malignant Lymphoma Pathology Images Using Conformal Prediction
Noriaki Hashimoto (RIKEN), Hiroaki Miyoshi (Kurume Univ.), Ichiro Takeuchi (Nagoya Univ./RIKEN) MI2025-108
Abstract (in Japanese) (See Japanese page) 
(in English) Ensuring the reliability of predictions is a critical challenge in the clinical application of machine learning models. In this study, we focus on conformal prediction as a method for uncertainty quantification and apply it to multi-class classification of malignant lymphoma using whole slide images. Through computational experiments, we evaluated an attention-based multiple instance learning model on real-world data with class imbalance. The results demonstrate that class-conditional conformal prediction can stably achieve the desired coverage rate for each disease subtype. The proposed approach is expected to contribute to safe AI-assisted diagnosis by presenting differential diagnosis candidates as a prediction set.
Keyword (in Japanese) (See Japanese page) 
(in English) Digital pathology / Conformal prediction / Uncertainty quantification / Multiple instance learning / / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 395, MI2025-108, pp. 177-180, March 2026.
Paper # MI2025-108 
Date of Issue 2026-02-26 (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 MI2025-108

Conference Information
Committee MI  
Conference Date 2026-03-05 - 2026-03-06 
Place (in Japanese) (See Japanese page) 
Place (in English) OKINAWAKEN SEINENKAIKAN 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Medical Imaging, etc. 
Paper Information
Registration To MI 
Conference Code 2026-03-MI 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Uncertainty Quantification for Multi-Class Classification of Malignant Lymphoma Pathology Images Using Conformal Prediction 
Sub Title (in English)  
Keyword(1) Digital pathology  
Keyword(2) Conformal prediction  
Keyword(3) Uncertainty quantification  
Keyword(4) Multiple instance learning  
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1st Author's Name Noriaki Hashimoto  
1st Author's Affiliation RIKEN (RIKEN)
2nd Author's Name Hiroaki Miyoshi  
2nd Author's Affiliation Kurume University (Kurume Univ.)
3rd Author's Name Ichiro Takeuchi  
3rd Author's Affiliation Nagoya University/RIKEN (Nagoya Univ./RIKEN)
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Speaker Author-1 
Date Time 2026-03-06 14:39:00 
Presentation Time 13 minutes 
Registration for MI 
Paper # MI2025-108 
Volume (vol) vol.125 
Number (no) no.395 
Page pp.177-180 
#Pages 4 
Date of Issue 2026-02-26 (MI) 

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