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Paper Abstract and Keywords
Presentation 2019-09-05 14:10
Analysis and Feature Selection of CNN Features -- Recognition of Neoplasia by using Endocytoscopic Images --
Hayato Itoh (Nagoya Univ.), Yuichi Mori, Masashi Misawa (Showa Univ.), Masahiro Oda (Nagoya Univ.), Shin-Ei Kudo (Showa Univ.), Kensaku Mori (Nagoya Univ.) PRMU2019-29 MI2019-48
Abstract (in Japanese) (See Japanese page) 
(in English) Pathological pattern classification is based on texture patterns in ultra magnified view of polyp surfaces.
Deep learning is known as an useful representation learning method with large dataset in several fields including pathological classification of medical images.This representation learning method achieves an optimal representation of patterns for predefined architecture by minimising a value of loss function. However, this is the optimisation in the meaning of maximum likelihood estimation with train data for the given architecture and loss function.Therefore, whether the extracted feature is really discriminative feature or not is unclear. In this work, we analyse discriminative and generalisation ability of deep-learning based feature by comparing with texture future for colorectal endocytoscopic images of polyp surfaces.
Keyword (in Japanese) (See Japanese page) 
(in English) Endocytoscopy / automated pathological diagnosis / deep learning / feature selection / manifold learning / definite canonicalisation / /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 193, MI2019-48, pp. 129-134, Sept. 2019.
Paper # MI2019-48 
Date of Issue 2019-08-28 (PRMU, MI) 
ISSN Online edition: ISSN 2432-6380
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
Conference Date 2019-09-04 - 2019-09-05 
Place (in Japanese) (See Japanese page) 
Place (in English)  
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Paper Information
Registration To MI 
Conference Code 2019-09-PRMU-MI-CVIM 
Language English (Japanese title is available) 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Analysis and Feature Selection of CNN Features 
Sub Title (in English) Recognition of Neoplasia by using Endocytoscopic Images 
Keyword(1) Endocytoscopy  
Keyword(2) automated pathological diagnosis  
Keyword(3) deep learning  
Keyword(4) feature selection  
Keyword(5) manifold learning  
Keyword(6) definite canonicalisation  
1st Author's Name Hayato Itoh  
1st Author's Affiliation Nagoya University (Nagoya Univ.)
2nd Author's Name Yuichi Mori  
2nd Author's Affiliation Showa University Northern Yokohama Hospital (Showa Univ.)
3rd Author's Name Masashi Misawa  
3rd Author's Affiliation Showa University Northern Yokohama Hospital (Showa Univ.)
4th Author's Name Masahiro Oda  
4th Author's Affiliation Nagoya University (Nagoya Univ.)
5th Author's Name Shin-Ei Kudo  
5th Author's Affiliation Showa University Northern Yokohama Hospital (Showa Univ.)
6th Author's Name Kensaku Mori  
6th Author's Affiliation Nagoya University (Nagoya Univ.)
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Speaker Author-1 
Date Time 2019-09-05 14:10:00 
Presentation Time 15 minutes 
Registration for MI 
Paper # PRMU2019-29, MI2019-48 
Volume (vol) vol.119 
Number (no) no.192(PRMU), no.193(MI) 
Page pp.129-134 
Date of Issue 2019-08-28 (PRMU, MI) 

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