Paper Abstract and Keywords |
Presentation |
2022-12-16 14:40
Data Augmentation Shumpei Takezaki (Kyushu Univ.), Kiyohito Tanaka (Kyoto Second Red Cross Hospital), Seiichi Uchida, Takeaki Kadota (Kyushu Univ.) PRMU2022-50 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Disease severity regression by a convolutional neural network (CNN) for medical images requires a sufficient number of image samples labeled with severity levels. Conditional generative adversarial network (cGAN)-based data augmentation (DA) is a possible solution, but it encounters two issues. The first issue is that existing cGANs cannot deal with real-valued severity levels as their conditions, and the second is that the severity of the generated images is not fully reliable. We propose continuous DA as a solution to the two issues. Our method uses continuous severity GAN to generate images at real-valued severity levels and dataset-disjoint multi-objective optimization to deal with the second issue. Our method was evaluated for estimating ulcerative colitis (UC) severity of endoscopic images and achieved higher classification performance than conventional DA methods. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
data augmentation / generative adversarial network / endoscopic images / / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 122, no. 314, PRMU2022-50, pp. 95-99, Dec. 2022. |
Paper # |
PRMU2022-50 |
Date of Issue |
2022-12-08 (PRMU) |
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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PRMU2022-50 |
Conference Information |
Committee |
PRMU |
Conference Date |
2022-12-15 - 2022-12-16 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Toyama International Conference Center |
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Paper Information |
Registration To |
PRMU |
Conference Code |
2022-12-PRMU |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Data Augmentation |
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data augmentation |
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generative adversarial network |
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endoscopic images |
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1st Author's Name |
Shumpei Takezaki |
1st Author's Affiliation |
Kyushu University (Kyushu Univ.) |
2nd Author's Name |
Kiyohito Tanaka |
2nd Author's Affiliation |
Kyoto Second Red Cross Hospital (Kyoto Second Red Cross Hospital) |
3rd Author's Name |
Seiichi Uchida |
3rd Author's Affiliation |
Kyushu University (Kyushu Univ.) |
4th Author's Name |
Takeaki Kadota |
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Kyushu University (Kyushu Univ.) |
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Speaker |
Author-1 |
Date Time |
2022-12-16 14:40:00 |
Presentation Time |
15 minutes |
Registration for |
PRMU |
Paper # |
PRMU2022-50 |
Volume (vol) |
vol.122 |
Number (no) |
no.314 |
Page |
pp.95-99 |
#Pages |
5 |
Date of Issue |
2022-12-08 (PRMU) |
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