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| 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 |
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| 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. |
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| 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 | |
| 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 | PRMU2019-29 MI2019-48 |
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| Conference Information | |
| Committee | PRMU MI IPSJ-CVIM |
| Conference Date | 2019-09-04 - 2019-09-05 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| 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 |
| Keyword(7) | |
| Keyword(8) | |
| 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 |
| #Pages | 6 |
| Date of Issue | 2019-08-28 (PRMU, MI) |
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