Paper Abstract and Keywords |
Presentation |
2022-01-26 13:00
Relationship between Image Quality and Learning Effect in Color Laparoscopic Images Generation by Generative Adversarial Networks Norifumi Kawabata (Hokkaido Univ.), Toshiya Nakaguchi (Chiba Univ.) MI2021-59 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Improving of personal computer performance, it is possible for healthcare workers and related researchers to support for medical image diagnosis at a low price and high performance. Particularly, in the medical image engineering field after 2010s, it is necessary to utilize artificial intelligence technology actively such as deep convolutional neural network. As past our study, we studied fundamentally on optimal design of color laparoscopic super-resolution image by Generative Adversarial Networks (GAN) which is one of unsupervised learning. As a result, for super-resolution image design method, we were able to verify by carrying out experiments. However, it is not enough for generated laparoscopic image quality from a view of both observation and objective. Therefore, from a view of information science, we consider that we need to verify experimentally for affect between network learning effect and generated image, and then to improve method. In this paper, we discussed relationship between image quality and learning effect in color laparoscopic image generation using super-resolution GAN. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Generative Adversarial Networks (GAN) / Unsupervised Learning / Laparoscopic Image / Super-Resolution / Image Quality Assessment / Medical Image Diagnosis / Learning Effect / |
Reference Info. |
IEICE Tech. Rep., vol. 121, no. 347, MI2021-59, pp. 59-64, Jan. 2022. |
Paper # |
MI2021-59 |
Date of Issue |
2022-01-18 (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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MI2021-59 |
Conference Information |
Committee |
MI |
Conference Date |
2022-01-25 - 2022-01-27 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Online |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
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Paper Information |
Registration To |
MI |
Conference Code |
2022-01-MI |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Relationship between Image Quality and Learning Effect in Color Laparoscopic Images Generation by Generative Adversarial Networks |
Sub Title (in English) |
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Keyword(1) |
Generative Adversarial Networks (GAN) |
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Unsupervised Learning |
Keyword(3) |
Laparoscopic Image |
Keyword(4) |
Super-Resolution |
Keyword(5) |
Image Quality Assessment |
Keyword(6) |
Medical Image Diagnosis |
Keyword(7) |
Learning Effect |
Keyword(8) |
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1st Author's Name |
Norifumi Kawabata |
1st Author's Affiliation |
Hokkaido University (Hokkaido Univ.) |
2nd Author's Name |
Toshiya Nakaguchi |
2nd Author's Affiliation |
Chiba University (Chiba Univ.) |
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Speaker |
Author-1 |
Date Time |
2022-01-26 13:00:00 |
Presentation Time |
13 minutes |
Registration for |
MI |
Paper # |
MI2021-59 |
Volume (vol) |
vol.121 |
Number (no) |
no.347 |
Page |
pp.59-64 |
#Pages |
6 |
Date of Issue |
2022-01-18 (MI) |
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