Paper Abstract and Keywords |
Presentation |
2019-03-06 16:40
Study on data augmentation using Fourier transform for texture image classification Daigo Nitta, Hayaru Shouno (UEC) NC2018-87 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
In the field of medical imaging such like computed tomography analysis, it is difficult to prepare a sufficient amount of labeled data for learning, and there exists bias in the number of classes.
Applying such data into a learning machine, we may obtain .
In order to overcome this problem, we propose a new data augmentation framework for case image data set of diffuse lung disease.
Specifically, we propose a method of decomposing learning data images in Fourier space and combining amplitude characteristics and phase characteristics among other classes for data augmentation.
As a result of the learning of the lesion identification problem in Deep Convolutional Neural Network using this method, the accuracy was improved compared with the conventional method such as normal linear transformation and the method proposed in recent years. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Deep Learning / Data Augmentation / Lesion Identification / Medical image / Texture image / / / |
Reference Info. |
IEICE Tech. Rep., vol. 118, no. 470, NC2018-87, pp. 233-238, March 2019. |
Paper # |
NC2018-87 |
Date of Issue |
2019-02-25 (NC) |
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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NC2018-87 |
Conference Information |
Committee |
NC MBE |
Conference Date |
2019-03-04 - 2019-03-06 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
University of Electro Communications |
Topics (in Japanese) |
(See Japanese page) |
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Paper Information |
Registration To |
NC |
Conference Code |
2019-03-NC-MBE |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Study on data augmentation using Fourier transform for texture image classification |
Sub Title (in English) |
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Deep Learning |
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Data Augmentation |
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Lesion Identification |
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Medical image |
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Texture image |
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1st Author's Name |
Daigo Nitta |
1st Author's Affiliation |
The University of Electro-Communications (UEC) |
2nd Author's Name |
Hayaru Shouno |
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The University of Electro-Communications (UEC) |
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Speaker |
Author-1 |
Date Time |
2019-03-06 16:40:00 |
Presentation Time |
25 minutes |
Registration for |
NC |
Paper # |
NC2018-87 |
Volume (vol) |
vol.118 |
Number (no) |
no.470 |
Page |
pp.233-238 |
#Pages |
6 |
Date of Issue |
2019-02-25 (NC) |
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