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| 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 |
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| 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 | |
| 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) | |
| Download PDF | MI2017-44 |
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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 |
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| 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 |
| #Pages | 5 |
| Date of Issue | 2017-09-18 (MI) |
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