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Paper Abstract and Keywords
Presentation 2025-12-05 15:00
Residual Tumor Cellularity Assessment in Breast Cancer Using a Vision Transformer with Graph-Based Embedding -- Tumor Cellularity Assessment in Breast Cancer Using a Vision Transformer with Graph-Based Embedding --
Hossain Md Shakhawat (Kochi-Tech), Md. Sazib Ahmed (AIM Lab) MICT2025-48 MI2025-56
Abstract (in Japanese) (See Japanese page) 
(in English) Residual Tumor Cellularity (RTC) assessment is performed to quantify the amount of viable tumor remaining after a therapy, providing a critical indication of treatment effectiveness and patient prognosis. Traditionally, this evaluation is performed manually by pathologists through microscopic inspection and counting tumor cells. This process is time-consuming and subject to intra- and inter-observer variability, particularly when there is a worldwide shortage of pathologists. To address these limitations, we propose an automated framework for RTC classification into low, mid, and high categories. The method utilizes hematoxylin and eosin (H&E)-stained biopsy images. It employs a Vision Transformer (DeiT-B) to extract contextual image embeddings, which are further refined using a GraphSAGE-based neural network to model inter-patch relationships and spatial dependencies within the tissue architecture. Experimental results demonstrate that our approach achieves an accuracy of 93.1%, highlighting its potential as a robust tool to assist pathologists in automated RTC assessment.
Keyword (in Japanese) (See Japanese page) 
(in English) Whole slide image / Digital pathology / Breast cancer / Tumor cellularity / / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 270, MI2025-56, pp. 66-69, Dec. 2025.
Paper # MI2025-56 
Date of Issue 2025-11-27 (MICT, 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 MICT2025-48 MI2025-56

Conference Information
Committee MICT MI  
Conference Date 2025-12-04 - 2025-12-05 
Place (in Japanese) (See Japanese page) 
Place (in English) Okayama Convention Center 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Medical imaging technology, healthcare and medical information communication technology, etc. 
Paper Information
Registration To MI 
Conference Code 2025-12-MICT-MI 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Residual Tumor Cellularity Assessment in Breast Cancer Using a Vision Transformer with Graph-Based Embedding 
Sub Title (in English) Tumor Cellularity Assessment in Breast Cancer Using a Vision Transformer with Graph-Based Embedding 
Keyword(1) Whole slide image  
Keyword(2) Digital pathology  
Keyword(3) Breast cancer  
Keyword(4) Tumor cellularity  
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1st Author's Name Hossain Md Shakhawat  
1st Author's Affiliation School of Informatics, Kochi University of Technology (Kochi-Tech)
2nd Author's Name Md. Sazib Ahmed  
2nd Author's Affiliation AIM Lab, Kochi University of Technology (AIM Lab)
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Speaker Author-1 
Date Time 2025-12-05 15:00:00 
Presentation Time 20 minutes 
Registration for MI 
Paper # MICT2025-48, MI2025-56 
Volume (vol) vol.125 
Number (no) no.269(MICT), no.270(MI) 
Page pp.66-69 
#Pages 4 
Date of Issue 2025-11-27 (MICT, MI) 

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