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Paper Abstract and Keywords
Presentation 2021-03-16 13:45
Deep Learning prediction of lung transplant rejection from FDG-PET and visualization of the basis for the decision
Keisuke Hori (Chiba Univ.), Yuma Iwao, Miwako Takahashi (QST), Haruhiko Shiiya (UTokyo/Hokkaido Univ.), Masaaki Sato (UTokyo), Taiga Yamaya (QST) MI2020-73
Abstract (in Japanese) (See Japanese page) 
(in English) We conducted a study to correlate FDG-PET images with pathological diagnosis of inflammation in lung transplantation (LTx) model rats, with the task of detecting early signs of chronic rejection from FDG-PET after LTx. In this study, we used VGG16 pre-trained on ImageNet to predict the pathological diagnosis at 6 weeks from FDG-PET images at 3 weeks after LTx, and the most accurate epoch was 96% in sensitivity and 91% in specificity. Furthermore, we analyzed the basis for deep learning from the change in prediction accuracy by reducing the features in the input image, and showed that the information in the lower part of the left lung may be important for pathological prediction.
Keyword (in Japanese) (See Japanese page) 
(in English) Lung Transplantation / FDG-PET / Deep Learning / / / / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 431, MI2020-73, pp. 108-111, March 2021.
Paper # MI2020-73 
Date of Issue 2021-03-08 (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)
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Conference Information
Committee MI  
Conference Date 2021-03-15 - 2021-03-17 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Medical Imaging 
Paper Information
Registration To MI 
Conference Code 2021-03-MI 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Deep Learning prediction of lung transplant rejection from FDG-PET and visualization of the basis for the decision 
Sub Title (in English) * 
Keyword(1) Lung Transplantation  
Keyword(2) FDG-PET  
Keyword(3) Deep Learning  
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1st Author's Name Keisuke Hori  
1st Author's Affiliation Chiba University (Chiba Univ.)
2nd Author's Name Yuma Iwao  
2nd Author's Affiliation National Institutes for Quantum and Radiological Science and Technology, National Institute of Radiological Sciences (QST)
3rd Author's Name Miwako Takahashi  
3rd Author's Affiliation National Institutes for Quantum and Radiological Science and Technology, National Institute of Radiological Sciences (QST)
4th Author's Name Haruhiko Shiiya  
4th Author's Affiliation University of Tokyo/Hokkaido University (UTokyo/Hokkaido Univ.)
5th Author's Name Masaaki Sato  
5th Author's Affiliation University of Tokyo (UTokyo)
6th Author's Name Taiga Yamaya  
6th Author's Affiliation National Institutes for Quantum and Radiological Science and Technology, National Institute of Radiological Sciences (QST)
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Speaker Author-1 
Date Time 2021-03-16 13:45:00 
Presentation Time 15 minutes 
Registration for MI 
Paper # MI2020-73 
Volume (vol) vol.120 
Number (no) no.431 
Page pp.108-111 
#Pages 4 
Date of Issue 2021-03-08 (MI) 

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