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Paper Abstract and Keywords
Presentation 2017-09-25 15:30
Classification of neoplasia and non-neoplasia for colon endocytoscopic images by convolutional neural network
Hayato Itoh (Nagoya Univ.), Yuichi Mori, Masashi Misawa (Showa Univ.), Masahiro Oda (Nagoya Univ.), Shin-ei Kudo (Showa Univ.), Kensaku Mori (Nagoya Univ.) MI2017-44
Abstract (in Japanese) (See Japanese page) 
(in English) Endocytoscopy is a new endoscope that enables us to perform conventional endoscopic observation and ultramagnified observation of cell level. This ultramagnified views (endocytoscopic images) make possible to perform pathological diagnosis only on endoscopic views of polyps during colonoscopy. However, accurate endocytoscopic image diagnosis requires higher experiences and knowledge for physicians. Therefore, computer-aided assistant system is required to prevent the overlooking of neoplastic lesions in endocytoscopy. For this purpose, we propose a new endocytoscopic image classification method that classifies neoplastic and non-neoplastic endocytoscopic images of polyps. We experimentally evaluate the classification performance of the proposed method. In this experiment, we use about 15,000 and 2,600 colorectal endocytoscopic images as training and test data, respectively. The results show that the proposed method achieves high sensitivity 89.4 %.
Keyword (in Japanese) (See Japanese page) 
(in English) endocytoscopy / convolutional neural network / automated pathological diagnosis / / / / /  
Reference Info. IEICE Tech. Rep., vol. 117, no. 220, MI2017-44, pp. 17-21, Sept. 2017.
Paper # MI2017-44 
Date of Issue 2017-09-18 (MI) 
ISSN Print edition: ISSN 0913-5685    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
Committee MI  
Conference Date 2017-09-25 - 2017-09-25 
Place (in Japanese) (See Japanese page) 
Place (in English) Chiba Univ. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Medical Imaging, etc. 
Paper Information
Registration To MI 
Conference Code 2017-09-MI 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Classification of neoplasia and non-neoplasia for colon endocytoscopic images by convolutional neural network 
Sub Title (in English)  
Keyword(1) endocytoscopy  
Keyword(2) convolutional neural network  
Keyword(3) automated pathological diagnosis  
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 (Showa Univ.)
3rd Author's Name Masashi Misawa  
3rd Author's Affiliation Showa University (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 (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 2017-09-25 15:30:00 
Presentation Time 30 minutes 
Registration for MI 
Paper # MI2017-44 
Volume (vol) vol.117 
Number (no) no.220 
Page pp.17-21 
Date of Issue 2017-09-18 (MI) 

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