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| 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 |
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| 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 | 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 | MI2020-91 |
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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 |
| Keyword(7) | |
| Keyword(8) | |
| 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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| Speaker | Author-1 |
| Date Time | 2021-03-17 11:00:00 |
| Presentation Time | 15 minutes |
| Registration for | MI |
| Paper # | MI2020-91 |
| Volume (vol) | vol.120 |
| Number (no) | no.431 |
| Page | pp.186-190 |
| #Pages | 5 |
| Date of Issue | 2021-03-08 (MI) |
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