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| Paper Abstract and Keywords | ||
| Presentation | 2020-01-30 15:20 Detection and Classification of Cervical Intraepithelial Lesions using Deep Learning Margaret Manalo, Kota Aoki, Yutaka Ueda, Yu Ito, Yasushi Yagi (Osaka Univ.)  MI2019-121 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Cervical cancer remains to have high occurrence and mortality rates in less developed regions due to the lack of diagnostic resources for early detection and cure. Colposcopy, which is considered to be the least invasive screening procedure, would still require the examination of a medical professional. This research focuses on the potential use of deep learning for detecting intraepithelial lesions and cancer from colposcopy images. The goal is to localize and classify these areas, as different stages of the disease correspond to varying rates of progression into cancer as well as appropriate medical treatment. A total of 672 colposcopy images were collected and annotated by a medical staff, with classes ranging from cervical intraepithelial neoplasia (CIN) to cancer. A fully convolutional network (FCN) was used to detect the concerned areas as objects by accessing each image as a whole, utilizing the additional context of epithelial location and color contrast from acetowhite lesions relative to the surrounding tissue. Object detection was performed at three scales for lesions of varying sizes, and logistic regression with a classification threshold was used to label the detections. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | colposcopy / cervical cancer / YOLO / object detection / deep learning / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 119, no. 399, MI2019-121, pp. 237-242, Jan. 2020. | |
| Paper # | MI2019-121 | |
| Date of Issue | 2020-01-22 (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 | MI2019-121 |
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| Conference Information | |
| Committee | MI |
| Conference Date | 2020-01-29 - 2020-01-30 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | OKINAWAKEN SEINENKAIKAN |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Medical Image Engineering, Analysis, Recognition, etc. |
| Paper Information | |
| Registration To | MI |
| Conference Code | 2020-01-MI |
| Language | English |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Detection and Classification of Cervical Intraepithelial Lesions using Deep Learning |
| Sub Title (in English) | |
| Keyword(1) | colposcopy |
| Keyword(2) | cervical cancer |
| Keyword(3) | YOLO |
| Keyword(4) | object detection |
| Keyword(5) | deep learning |
| Keyword(6) | |
| Keyword(7) | |
| Keyword(8) | |
| 1st Author's Name | Margaret Manalo |
| 1st Author's Affiliation | Osaka University (Osaka Univ.) |
| 2nd Author's Name | Kota Aoki |
| 2nd Author's Affiliation | Osaka University (Osaka Univ.) |
| 3rd Author's Name | Yutaka Ueda |
| 3rd Author's Affiliation | Osaka University (Osaka Univ.) |
| 4th Author's Name | Yu Ito |
| 4th Author's Affiliation | Osaka University (Osaka Univ.) |
| 5th Author's Name | Yasushi Yagi |
| 5th Author's Affiliation | Osaka University (Osaka Univ.) |
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| Speaker | Author-1 |
| Date Time | 2020-01-30 15:20:00 |
| Presentation Time | 10 minutes |
| Registration for | MI |
| Paper # | MI2019-121 |
| Volume (vol) | vol.119 |
| Number (no) | no.399 |
| Page | pp.237-242 |
| #Pages | 6 |
| Date of Issue | 2020-01-22 (MI) |
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