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Paper Abstract and Keywords
Presentation 2024-03-03 16:30
Assessment of the Utility of Tumor Location Information in MR Image Classification
Tsukasa Nishinakagawa, Yoshinari Takeishi, Jun'ichi Takeuchi (Kyushu Univ.) IBISML2023-43
Abstract (in Japanese) (See Japanese page) 
(in English) MRI, or magnetic resonance imaging, is a medical imaging technique widely used in various healthcare settings. It utilizes the magnetic resonance phenomenon of hydrogen nuclei within the body to obtain cross-sectional images of the body. In this research, we explore the classification problem of tumor types in head MR images using neural networks. Achieving high accuracy in such classifications is anticipated to contribute to automated diagnosis in clinical settings. However, a challenge in MR image classification is the limited availability of real-world data for training. To address this issue, our study employs fine-tuning with existing models to create an accurate model from a restricted dataset. Additionally, since the distribution of tumor occurrence varies based on tumor types, we investigate whether utilizing not only tumor shape but also its positional information can enhance the performance of MR image classification. We evaluate this by conducting experiments using publicly available MR images and their degraded versions. The results confirm that as images degrade, the utility of positional information increases. Notably, providing the positional information as an image proves more effective than presenting it as a 2D coordinate vector for enhancing the usefulness of positional information in the context of tumor classification.
Keyword (in Japanese) (See Japanese page) 
(in English) CNN / MRI / fine-tuning / image classification / / / /  
Reference Info. IEICE Tech. Rep., vol. 123, no. 410, IBISML2023-43, pp. 21-28, March 2024.
Paper # IBISML2023-43 
Date of Issue 2024-02-25 (IBISML) 
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 IBISML2023-43

Conference Information
Committee PRMU IBISML IPSJ-CVIM  
Conference Date 2024-03-03 - 2024-03-04 
Place (in Japanese) (See Japanese page) 
Place (in English) Hiroshima Univ. Higashi-Hiroshima campus 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To IBISML 
Conference Code 2024-03-PRMU-IBISML-CVIM 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Assessment of the Utility of Tumor Location Information in MR Image Classification 
Sub Title (in English)  
Keyword(1) CNN  
Keyword(2) MRI  
Keyword(3) fine-tuning  
Keyword(4) image classification  
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1st Author's Name Tsukasa Nishinakagawa  
1st Author's Affiliation Kyushu University (Kyushu Univ.)
2nd Author's Name Yoshinari Takeishi  
2nd Author's Affiliation Kyushu University (Kyushu Univ.)
3rd Author's Name Jun'ichi Takeuchi  
3rd Author's Affiliation Kyushu University (Kyushu Univ.)
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Speaker Author-1 
Date Time 2024-03-03 16:30:00 
Presentation Time 15 minutes 
Registration for IBISML 
Paper # IBISML2023-43 
Volume (vol) vol.123 
Number (no) no.410 
Page pp.21-28 
#Pages 8 
Date of Issue 2024-02-25 (IBISML) 

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