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| Presentation | 2019-09-05 13:55 Hierarchical Classification to Detect Type of Diseases and Abnormality Simultaneously in Optical Coherence Tomography Images Yudai Kato, Yuji Ayatsuka, Takaki Uta (CRESCO), Soichiro Kuwayama, Hideaki Usui, Aki Kato, Yuichiro Ogura, Tsutomu Yasukawa (Nagoya City University)  PRMU2019-26 MI2019-45 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Analyzing medical images with machine learning is useful not only for classifying types of diseases but for screening abnormality. Our previous work showed that a convolutional neural network (CNN) model which learned for classifying diseases detects abnormality better than a CNN model which just learned abnormality as one category. The result is regarded as that a type of disease is important information to find visual feature of abnormality in image. In this paper, we propose a hierarchical method in which a model is trained both types of diseases and abnormality simultaneously. In our method, losses for each diseases are used for training the lower layer, and a loss for abnormality calculated as simple accumulation of losses for each diseases is used for training the upper layer. Models trained by our method achieve better accuracy in both classifying diseases and screening. |
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| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | OCT / fundus diseases / machine learning / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 119, no. 193, MI2019-45, pp. 105-108, Sept. 2019. | |
| Paper # | MI2019-45 | |
| 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 | PRMU2019-26 MI2019-45 |
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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 | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Hierarchical Classification to Detect Type of Diseases and Abnormality Simultaneously in Optical Coherence Tomography Images |
| Sub Title (in English) | |
| Keyword(1) | OCT |
| Keyword(2) | fundus diseases |
| Keyword(3) | machine learning |
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| 1st Author's Name | Yudai Kato |
| 1st Author's Affiliation | CRESCO LTD. (CRESCO) |
| 2nd Author's Name | Yuji Ayatsuka |
| 2nd Author's Affiliation | CRESCO LTD. (CRESCO) |
| 3rd Author's Name | Takaki Uta |
| 3rd Author's Affiliation | CRESCO LTD. (CRESCO) |
| 4th Author's Name | Soichiro Kuwayama |
| 4th Author's Affiliation | Nagoya City University (Nagoya City University) |
| 5th Author's Name | Hideaki Usui |
| 5th Author's Affiliation | Nagoya City University (Nagoya City University) |
| 6th Author's Name | Aki Kato |
| 6th Author's Affiliation | Nagoya City University (Nagoya City University) |
| 7th Author's Name | Yuichiro Ogura |
| 7th Author's Affiliation | Nagoya City University (Nagoya City University) |
| 8th Author's Name | Tsutomu Yasukawa |
| 8th Author's Affiliation | Nagoya City University (Nagoya City University) |
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| Speaker | Author-1 |
| Date Time | 2019-09-05 13:55:00 |
| Presentation Time | 15 minutes |
| Registration for | MI |
| Paper # | PRMU2019-26, MI2019-45 |
| Volume (vol) | vol.119 |
| Number (no) | no.192(PRMU), no.193(MI) |
| Page | pp.105-108 |
| #Pages | 4 |
| Date of Issue | 2019-08-28 (PRMU, MI) |
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