Paper Abstract and Keywords |
Presentation |
2019-01-23 09:45
Feature Selection from Imbalanced Data
-- Pathological Pattern Classification in 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.) MI2018-87 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Endocytoscope gives ultramagnified observation that enables physicians to achieve minimally invasive and real-time diagnosis in colonoscopy. Since this modality is a quite new, a pathological image classifier is required for the support of non-expert physicians. In addition to the ununiformity of occurrence frequency of pathological
patterns, data sampling by physicians includes bias. We have to handle imbalances of data to design an accurate pathological classifier. We propose feature-selection method that selects discriminative feature from imbalanced data for training of pathological classifier. We experimentally evaluated the proposed method by comparing the classification accuracy between before and after feature selection with about 50,000 endocytoscopic images. Our method achieves 2.7% improvement of classification accuracy with more accurate likelihood estimation than original texture features. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Endocytoscopy / automated pathological diagnosis / feature selection / texture feature / manifold learning / definite canonicalisation / / |
Reference Info. |
IEICE Tech. Rep., vol. 118, no. 412, MI2018-87, pp. 109-114, Jan. 2019. |
Paper # |
MI2018-87 |
Date of Issue |
2019-01-15 (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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MI2018-87 |
Conference Information |
Committee |
MI |
Conference Date |
2019-01-22 - 2019-01-23 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
|
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
Medical Image Engineering, Analysis, Recognition, etc. |
Paper Information |
Registration To |
MI |
Conference Code |
2019-01-MI |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Feature Selection from Imbalanced Data |
Sub Title (in English) |
Pathological Pattern Classification in Endocytoscopic Images |
Keyword(1) |
Endocytoscopy |
Keyword(2) |
automated pathological diagnosis |
Keyword(3) |
feature selection |
Keyword(4) |
texture feature |
Keyword(5) |
manifold learning |
Keyword(6) |
definite canonicalisation |
Keyword(7) |
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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 |
2019-01-23 09:45:00 |
Presentation Time |
15 minutes |
Registration for |
MI |
Paper # |
MI2018-87 |
Volume (vol) |
vol.118 |
Number (no) |
no.412 |
Page |
pp.109-114 |
#Pages |
6 |
Date of Issue |
2019-01-15 (MI) |
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