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Paper Abstract and Keywords
Presentation 2020-01-29 10:05
A study of generalized generation of image features for computer-aided detection systems based on unsupervised learning with normal datasets -- Experimental evaluations of feature generation by small datasets --
Kazuyuki Ushifusa, Mitsutaka Nemoto(, Yuichi Kimura, Takashi Nagaoka, Takahiro Yamada, Atsuko Tanaka (Kindai Uni.), Naoto Hayashi (The Uni of Tokyo Hosp) MI2019-68
Abstract (in Japanese) (See Japanese page) 
(in English) In a computer-aided detection system, image features are essential factors. In this study, we propose an image feature generation method that is based on unsupervised deep learning with only a normal dataset and could generate image features irrespective of the training dataset scale. To evaluate robustness against the scale of training data, we experimentally evaluate change of performance with the reduction of the scale of the training dataset. As a result of applied the proposed method to the identification of cerebral aneurysm on head MRA, the average ANODE score was 0.523 ± 0.0362. Furthermore, we also confirmed that our method could create useful features, even if the training data decrease.
Keyword (in Japanese) (See Japanese page) 
(in English) Image feature / Unsupervised deep learning / Convolutional autoencoder / Small training dataset / / / /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 399, MI2019-68, pp. 15-18, Jan. 2020.
Paper # MI2019-68 
Date of Issue 2020-01-22 (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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Conference Information
Committee MI  
Conference Date 2020-01-29 - 2020-01-30 
Place (in Japanese) (See Japanese page) 
Place (in English) OKINAWAKEN SEINENKAIKAN 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Medical Image Engineering, Analysis, Recognition, etc. 
Paper Information
Registration To MI 
Conference Code 2020-01-MI 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A study of generalized generation of image features for computer-aided detection systems based on unsupervised learning with normal datasets 
Sub Title (in English) Experimental evaluations of feature generation by small datasets 
Keyword(1) Image feature  
Keyword(2) Unsupervised deep learning  
Keyword(3) Convolutional autoencoder  
Keyword(4) Small training dataset  
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1st Author's Name Kazuyuki Ushifusa  
1st Author's Affiliation Kindai University (Kindai Uni.)
2nd Author's Name Mitsutaka Nemoto(  
2nd Author's Affiliation Kindai University (Kindai Uni.)
3rd Author's Name Yuichi Kimura  
3rd Author's Affiliation Kindai University (Kindai Uni.)
4th Author's Name Takashi Nagaoka  
4th Author's Affiliation Kindai University (Kindai Uni.)
5th Author's Name Takahiro Yamada  
5th Author's Affiliation Kindai University (Kindai Uni.)
6th Author's Name Atsuko Tanaka  
6th Author's Affiliation Kindai University (Kindai Uni.)
7th Author's Name Naoto Hayashi  
7th Author's Affiliation The University of Tokyo Hospital (The Uni of Tokyo Hosp)
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Speaker Author-1 
Date Time 2020-01-29 10:05:00 
Presentation Time 10 minutes 
Registration for MI 
Paper # MI2019-68 
Volume (vol) vol.119 
Number (no) no.399 
Page pp.15-18 
#Pages
Date of Issue 2020-01-22 (MI) 


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