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Paper Abstract and Keywords
Presentation 2019-10-19 10:45
Localization of Diffuse Lung Deseases' Lesions and Quantification of Their Volumes Using Deep Learning
Hiroaki Takebe, Yasutaka Moriwaki, Nobuhiro Miyazaki, Takayuki Baba (FUJITSU LAB.), Hiroaki Terada, Toru Higaki, Kazuo Awai (Hiroshima Univ.), Hirotaka Kobayashi, Machiko Nakagawa, Masahiko Shimada, Kenji Kitayama (FUJITSU) PRMU2019-44
Abstract (in Japanese) (See Japanese page) 
(in English) Changes in the amount of lesion over time are important information for diagnostic imaging of diffuse lung disease in which lesions appear throughout a lung. Then a technique that can automatically quantify the amount of lesion is desired. The quantification of the amount of lesion is performed by classifying the lesion from the image. A method using deep learning (DL) is effective for classifying lesions in diffuse lung disease. However, there is a problem that it is difficult to classify micro nodules that are typical lesions. We propose a method for accurately classifying micro nodules using DL by focusing on the 3D structure of a micro nodule. We confirmed that as for the proposed method, the classification accuracy was improved compared to the conventional method, and as an application of this method, the temporal change in the amount of lesions was accurately quantified for cases taken at multiple time points of the same patient.
Keyword (in Japanese) (See Japanese page) 
(in English) Deep Learning / Diffuse Lung Disease / Lesion Classification / Micro Nodule / / / /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 235, PRMU2019-44, pp. 67-72, Oct. 2019.
Paper # PRMU2019-44 
Date of Issue 2019-10-11 (PRMU) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
Copyright
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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. (No. 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF PRMU2019-44

Conference Information
Committee PRMU  
Conference Date 2019-10-18 - 2019-10-19 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To PRMU 
Conference Code 2019-10-PRMU 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Localization of Diffuse Lung Deseases' Lesions and Quantification of Their Volumes Using Deep Learning 
Sub Title (in English)  
Keyword(1) Deep Learning  
Keyword(2) Diffuse Lung Disease  
Keyword(3) Lesion Classification  
Keyword(4) Micro Nodule  
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1st Author's Name Hiroaki Takebe  
1st Author's Affiliation FUJITSU LABORATORIES LTD. (FUJITSU LAB.)
2nd Author's Name Yasutaka Moriwaki  
2nd Author's Affiliation FUJITSU LABORATORIES LTD. (FUJITSU LAB.)
3rd Author's Name Nobuhiro Miyazaki  
3rd Author's Affiliation FUJITSU LABORATORIES LTD. (FUJITSU LAB.)
4th Author's Name Takayuki Baba  
4th Author's Affiliation FUJITSU LABORATORIES LTD. (FUJITSU LAB.)
5th Author's Name Hiroaki Terada  
5th Author's Affiliation Hiroshima University (Hiroshima Univ.)
6th Author's Name Toru Higaki  
6th Author's Affiliation Hiroshima University (Hiroshima Univ.)
7th Author's Name Kazuo Awai  
7th Author's Affiliation Hiroshima University (Hiroshima Univ.)
8th Author's Name Hirotaka Kobayashi  
8th Author's Affiliation FUJITSU LIMITED (FUJITSU)
9th Author's Name Machiko Nakagawa  
9th Author's Affiliation FUJITSU LIMITED (FUJITSU)
10th Author's Name Masahiko Shimada  
10th Author's Affiliation FUJITSU LIMITED (FUJITSU)
11th Author's Name Kenji Kitayama  
11th Author's Affiliation FUJITSU LIMITED (FUJITSU)
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Speaker
Date Time 2019-10-19 10:45:00 
Presentation Time 15 
Registration for PRMU 
Paper # IEICE-PRMU2019-44 
Volume (vol) IEICE-119 
Number (no) no.235 
Page pp.67-72 
#Pages IEICE-6 
Date of Issue IEICE-PRMU-2019-10-11 


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