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Paper Abstract and Keywords
Presentation 2021-03-17 13:45
Medical Image Style Translation by Adversarial Training with Paired Inputs
Kazuki Fujioka (Kobe Univ.), Takashi Matsubara (Osaka Univ.), Kuniaki Uehara (Osaka Gakuin Univ.) MI2020-96
Abstract (in Japanese) (See Japanese page) 
(in English) Medical image diagnosis by artificial intelligence requires a large amount of data for learning. However, preparing such data requires enormous cost and time. Image-to-image translation methods based on deep learning intend to learn the joint distribution of the two domains and find transformations between them. Especially, CycleGAN which is the Generative Adversarial Networks(GANs)-based method has shown convincing results. As a result, it is possible to generate an image having the same contrast characteristics as that of another modality from an image of one modality, so that it is possible to generate artificial data required for learning. However, it is difficult to maintain consistency before and after conversion of image modality by CycleGAN. To address this problem, we improve CycleGAN in our study. Our proposed method generates visually reasonable images. In addition, in order to compare our proposed method with CycleGAN, we quantitatively evaluated it using the image quality evaluation index and showed that our proposed method is effective.
Keyword (in Japanese) (See Japanese page) 
(in English) Deep Learning / Generative Adversarial Networks / Image-to-Image Translation / / / / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 431, MI2020-96, pp. 212-217, March 2021.
Paper # MI2020-96 
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)
Download PDF MI2020-96

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) Medical Image Style Translation by Adversarial Training with Paired Inputs 
Sub Title (in English)  
Keyword(1) Deep Learning  
Keyword(2) Generative Adversarial Networks  
Keyword(3) Image-to-Image Translation  
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1st Author's Name Kazuki Fujioka  
1st Author's Affiliation Kobe University (Kobe Univ.)
2nd Author's Name Takashi Matsubara  
2nd Author's Affiliation Osaka University (Osaka Univ.)
3rd Author's Name Kuniaki Uehara  
3rd Author's Affiliation Osaka Gakuin University (Osaka Gakuin Univ.)
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Date Time 2021-03-17 13:45:00 
Presentation Time 15 minutes 
Registration for MI 
Paper # MI2020-96 
Volume (vol) vol.120 
Number (no) no.431 
Page pp.212-217 
#Pages 6 
Date of Issue 2021-03-08 (MI) 

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