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Paper Abstract and Keywords
Presentation 2026-02-18 14:30
Automated Deep Vein Thrombosis Detection Model Focused on Diversity of Ultrasound Imaging Data
Yusuke Sato, Naoki Negishi, Masaaki Omura, Mayumi Shiozaki, Aya Yokota, Hideki Niimi, Hideyuki Hasegawa, Shangce Gao (Univ. Toyama) US2025-60
Abstract (in Japanese) (See Japanese page) 
(in English) Ultrasound examination is the first-line modality for diagnosing deep vein thrombosis (DVT) because it is noninvasive, low cost, and repeatable. In the B-mode venous compression test, the examiner assesses whether the vein collapses under probe pressure and thereby determines the presence of thrombus. However, diagnostic performance and reproducibility
vary because the procedure depends on the examiner’s experience and technical skill. To address this issue, we developed an image classification model that takes paired B-mode images acquired before and during compression as input. The proposed two-branch CNN processes the non-compression and compression images with separate ResNet backbones, integrates the extracted features, and performs binary classification of DVT. We also compared and optimized multiple data augmentation methods to stabilize performance under limited data.
Keyword (in Japanese) (See Japanese page) 
(in English) Deep vein thrombosis / Ultrasound imaging / Image classification / Deep learning / Computer-aided diagnosis / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 354, US2025-60, pp. 10-15, Feb. 2026.
Paper # US2025-60 
Date of Issue 2026-02-11 (US) 
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 US2025-60

Conference Information
Committee US  
Conference Date 2026-02-18 - 2026-02-18 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English) Acoustic imaging, NDT, Ultrasound in medicine, Ultrasonics, etc. 
Paper Information
Registration To US 
Conference Code 2026-02-US 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Automated Deep Vein Thrombosis Detection Model Focused on Diversity of Ultrasound Imaging Data 
Sub Title (in English)  
Keyword(1) Deep vein thrombosis  
Keyword(2) Ultrasound imaging  
Keyword(3) Image classification  
Keyword(4) Deep learning  
Keyword(5) Computer-aided diagnosis  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Yusuke Sato  
1st Author's Affiliation University of Toyama (Univ. Toyama)
2nd Author's Name Naoki Negishi  
2nd Author's Affiliation University of Toyama (Univ. Toyama)
3rd Author's Name Masaaki Omura  
3rd Author's Affiliation University of Toyama (Univ. Toyama)
4th Author's Name Mayumi Shiozaki  
4th Author's Affiliation University of Toyama (Univ. Toyama)
5th Author's Name Aya Yokota  
5th Author's Affiliation University of Toyama (Univ. Toyama)
6th Author's Name Hideki Niimi  
6th Author's Affiliation University of Toyama (Univ. Toyama)
7th Author's Name Hideyuki Hasegawa  
7th Author's Affiliation University of Toyama (Univ. Toyama)
8th Author's Name Shangce Gao  
8th Author's Affiliation University of Toyama (Univ. Toyama)
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Speaker Author-1 
Date Time 2026-02-18 14:30:00 
Presentation Time 25 minutes 
Registration for US 
Paper # US2025-60 
Volume (vol) vol.125 
Number (no) no.354 
Page pp.10-15 
#Pages 6 
Date of Issue 2026-02-11 (US) 

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