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| Presentation | 2022-01-27 13:28 Study of Automatic Diagnosis of Early Oral Cancers Based on Fluorescence Images by 5-ALA and Deep Learning -- Automatic Generation of Fluorescence Images Using GAN and Classification of Stages by CNN -- Taro Fujimoto (Waseda Univ.), Eiji Fukuzawa (Waseda Univ./Yazaki), Seiko Tatehara, Kazuhito Satomura (Tsurumi Univ.), Jun Ohya (Waseda Univ.)  MI2021-76 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | A screening system for early-stage oral cancers should be established because detecting them is difficult even for specialists, and therefore they are often detected late. In this paper, we propose a method to automatically classify fluorescence images acquired by ALA-PDD (Photodynamic Diagnosis using 5-Aminolevulinic Acid) into three classes (normal, low-grade, high-grade). We augment small fluorescence image datasets by training GAN with Differentiable Augmentation, and then train CNN for the classification. As a result, we obtained good classification output, and this suggests that the combination of ALA-PDD and CNN classification is a promising method for oral cancer screening. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Oral Cancer / 5-ALA / Photodynamic Diagnosis / CNN / GAN / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 121, no. 347, MI2021-76, pp. 135-140, Jan. 2022. | |
| Paper # | MI2021-76 | |
| Date of Issue | 2022-01-18 (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 | MI2021-76 |
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| Conference Information | |
| Committee | MI |
| Conference Date | 2022-01-25 - 2022-01-27 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | MI |
| Conference Code | 2022-01-MI |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Study of Automatic Diagnosis of Early Oral Cancers Based on Fluorescence Images by 5-ALA and Deep Learning |
| Sub Title (in English) | Automatic Generation of Fluorescence Images Using GAN and Classification of Stages by CNN |
| Keyword(1) | Oral Cancer |
| Keyword(2) | 5-ALA |
| Keyword(3) | Photodynamic Diagnosis |
| Keyword(4) | CNN |
| Keyword(5) | GAN |
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| Keyword(8) | |
| 1st Author's Name | Taro Fujimoto |
| 1st Author's Affiliation | Waseda University (Waseda Univ.) |
| 2nd Author's Name | Eiji Fukuzawa |
| 2nd Author's Affiliation | Waseda University/Yazaki Corporation (Waseda Univ./Yazaki) |
| 3rd Author's Name | Seiko Tatehara |
| 3rd Author's Affiliation | Tsurumi University (Tsurumi Univ.) |
| 4th Author's Name | Kazuhito Satomura |
| 4th Author's Affiliation | Tsurumi University (Tsurumi Univ.) |
| 5th Author's Name | Jun Ohya |
| 5th Author's Affiliation | Waseda University (Waseda Univ.) |
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| Speaker | Author-1 |
| Date Time | 2022-01-27 13:28:00 |
| Presentation Time | 13 minutes |
| Registration for | MI |
| Paper # | MI2021-76 |
| Volume (vol) | vol.121 |
| Number (no) | no.347 |
| Page | pp.135-140 |
| #Pages | 6 |
| Date of Issue | 2022-01-18 (MI) |
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