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Paper Abstract and Keywords
Presentation 2021-03-17 11:00
Optimal Design and Quality Assessment of Color Laparoscopic Super-Resolution Image by Generative Adversarial Networks
Norifumi Kawabata (Tokyo Univ. of Science), Toshiya Nakaguchi (Chiba Univ.) MI2020-91
Abstract (in Japanese) (See Japanese page) 
(in English) The Generative Adversarial Networks (GAN) is unsupervised learning enabled to transform according to data characteristics, though this generate unreal data by learning characteristics from data. As past our study, we discussed from the viewpoint of image quality for super-resolution of color laparoscopic image including SRCNN (Super-Resolution Convolutional Neural Network). However, it was not enough to compare to other neural network methods in our discussion. We consider that it is possible to support the medical image diagnosis by measuring whether the difference of both neural network method and image contents is affected or not for image quality. In this paper, first we carried out the objective image quality assessment by designing optimally of color laparoscopic super-resolution image using Generative Adversarial Networks (GAN). And then, we discussed for performance between methods comparing to result of SRCNN.
Keyword (in Japanese) (See Japanese page) 
(in English) Generative Adversarial Networks (GAN) / Unsupervised Learning / Laparoscopic Image / Super-Resolution / Image Quality Assessment / Medical Image Diagnosis / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 431, MI2020-91, pp. 186-190, March 2021.
Paper # MI2020-91 
Date of Issue 2021-03-08 (MI) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
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) Optimal Design and Quality Assessment of Color Laparoscopic Super-Resolution Image by Generative Adversarial Networks 
Sub Title (in English)  
Keyword(1) Generative Adversarial Networks (GAN)  
Keyword(2) Unsupervised Learning  
Keyword(3) Laparoscopic Image  
Keyword(4) Super-Resolution  
Keyword(5) Image Quality Assessment  
Keyword(6) Medical Image Diagnosis  
1st Author's Name Norifumi Kawabata  
1st Author's Affiliation Tokyo University of Science (Tokyo Univ. of Science)
2nd Author's Name Toshiya Nakaguchi  
2nd Author's Affiliation Chiba University (Chiba Univ.)
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Date Time 2021-03-17 11:00:00 
Presentation Time 15 
Registration for MI 
Paper # MI2020-91 
Volume (vol) 120 
Number (no) no.431 
Page pp.186-190 
Date of Issue 2021-03-08 (MI) 

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