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Paper Abstract and Keywords
Presentation 2023-03-06 09:57
Multi-label multi-class estimation of pathology in high-resolution chest CT images using SRGAN
Tetsuya Asakawa, Riku Tsuneda, Yuki Sugimoto (TUT), Kazuki Shimizu, Takuyuki Komoda (THC), Masaki Aono (TUT) MI2022-76
Abstract (in Japanese) (See Japanese page) 
(in English) The purpose of this research, three pathologies (thickening, calcification, and cavitation) were accurately estimated as a multi-label problem from 3D chest CT data of tuberculosis patients. We extracted 2D image data of only the lung (excluding space, fat, bone, etc.) from the 3D chest CT data of a tuberculosis patient, and generated super-resolution images of the CT image using SRGAN. We developed a unique model that combined features using three DNN models using the whole CT image and the disease-only image. As a result, the average AUC was 0.658 for EfficientNetB07.
Keyword (in Japanese) (See Japanese page) 
(in English) Computed Tomography / Tuberculosis / Deep Learning / Nulti-label classification / Super-resolution / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 417, MI2022-76, pp. 14-19, March 2023.
Paper # MI2022-76 
Date of Issue 2023-02-27 (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 MI2022-76

Conference Information
Committee MI  
Conference Date 2023-03-06 - 2023-03-07 
Place (in Japanese) (See Japanese page) 
Place (in English) OKINAWA SEINENKAIKAN 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To MI 
Conference Code 2023-03-MI 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Multi-label multi-class estimation of pathology in high-resolution chest CT images using SRGAN 
Sub Title (in English)  
Keyword(1) Computed Tomography  
Keyword(2) Tuberculosis  
Keyword(3) Deep Learning  
Keyword(4) Nulti-label classification  
Keyword(5) Super-resolution  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Tetsuya Asakawa  
1st Author's Affiliation Toyohashi University of Technology (TUT)
2nd Author's Name Riku Tsuneda  
2nd Author's Affiliation Toyohashi University of Technology (TUT)
3rd Author's Name Yuki Sugimoto  
3rd Author's Affiliation Toyohashi University of Technology (TUT)
4th Author's Name Kazuki Shimizu  
4th Author's Affiliation Toyohashi Heart Center (THC)
5th Author's Name Takuyuki Komoda  
5th Author's Affiliation Toyohashi Heart Center (THC)
6th Author's Name Masaki Aono  
6th Author's Affiliation Toyohashi University of Technology (TUT)
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Speaker Author-1 
Date Time 2023-03-06 09:57:00 
Presentation Time 13 minutes 
Registration for MI 
Paper # MI2022-76 
Volume (vol) vol.122 
Number (no) no.417 
Page pp.14-19 
#Pages 6 
Date of Issue 2023-02-27 (MI) 

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