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Paper Abstract and Keywords
Presentation 2026-03-06 15:40
Delineation Model with Swin UNETR for Large Gross Tumor Volumes in Radiotherapy Patients with Stage III NSCLC
Qijing Lin, Hidetaka Arimura, Yunhao Cui, Hidetake Yabuuchi (Kyudai) MI2025-112
Abstract (in Japanese) (See Japanese page) 
(in English) This study aimed to develop a delineation model with Swin UNETR for large gross tumor volumes (GTVs) in stage III NSCLC patients. A dataset of 116 patients with large tumors (5–13.3 cm) was selected. The Swin UNETR shifted-window self-attention to capture multi-scale features. Using nested 5-fold cross-validation, the delineation model achieved a Dice similarity coefficient (DSC) of 80.9% and a 95th percentile Hausdorff distance (HD95) of 8.8 mm, outperforming dense V-net and UNETR. A correlation analysis revealed that the segmentation accuracy of the proposed model could not be affected by the effective diameter (ED) of the large tumors used in this study. The proposed model could have the potential to improve the robustness for GTV delineation of large tumors with various sizes.
Keyword (in Japanese) (See Japanese page) 
(in English) lung cancer / segmentation / deep learning / radiotherapy / / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 395, MI2025-112, pp. 193-194, March 2026.
Paper # MI2025-112 
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 MI2025-112

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 English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Delineation Model with Swin UNETR for Large Gross Tumor Volumes in Radiotherapy Patients with Stage III NSCLC 
Sub Title (in English)  
Keyword(1) lung cancer  
Keyword(2) segmentation  
Keyword(3) deep learning  
Keyword(4) radiotherapy  
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1st Author's Name Qijing Lin  
1st Author's Affiliation Kyushu University (Kyudai)
2nd Author's Name Hidetaka Arimura  
2nd Author's Affiliation Kyushu University (Kyudai)
3rd Author's Name Yunhao Cui  
3rd Author's Affiliation Kyushu University (Kyudai)
4th Author's Name Hidetake Yabuuchi  
4th Author's Affiliation Kyushu University (Kyudai)
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Speaker Author-1 
Date Time 2026-03-06 15:40:00 
Presentation Time 13 minutes 
Registration for MI 
Paper # MI2025-112 
Volume (vol) vol.125 
Number (no) no.395 
Page pp.193-194 
#Pages 2 
Date of Issue 2026-02-26 (MI) 

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