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| Presentation | 2022-01-27 16:13 [Short Paper] utomatic Extraction of Regions of Vascular Lesions Including Diffuse Lesions in MR Images Using Weakly Supervised Deep Learning Koki Fukaya, Takeshi Hara (Gifu Univ.), Taiki Nozaki, Masaki Matsusako (St.Luke's International Hosp.), Tetsuro Katafuchi (Gifu Medical Univ.), Xiangrong Zhou, Hiroshi Fujita (Gifu Univ.)  MI2021-87 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Klippel-Trenaunay-Weber syndrome (KTS) is a type of vascular lesion for which there is no quantitative diagnostic method because the degree of lesion progression is judged subjectively by the physician.The purpose of this study is to develop a method for automatic extraction of lesion regions in MRI images, which will lead to quantitative evaluation.Since it is difficult to obtain annotation labels for KTS, we used labels for each image to extract the regions.We applied Grad-Cam to two types of classification: one is whether the lesion occurs on the left or right thigh, and the other is whether the lesion is included in the patched image.Pseudo-labels were created based on the obtained attention maps, and segmentation was performed using CNN. As a result, the agreement rate was 75.3%, indicating the effectiveness of the method. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | MRI / Deep Learning / Grad-Cam / weakly supervised / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 121, no. 347, MI2021-87, pp. 186-187, Jan. 2022. | |
| Paper # | MI2021-87 | |
| 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 | MI2021-87 |
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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) | utomatic Extraction of Regions of Vascular Lesions Including Diffuse Lesions in MR Images Using Weakly Supervised Deep Learning |
| Sub Title (in English) | |
| Keyword(1) | MRI |
| Keyword(2) | Deep Learning |
| Keyword(3) | Grad-Cam |
| Keyword(4) | weakly supervised |
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| 1st Author's Name | Koki Fukaya |
| 1st Author's Affiliation | Gifu University (Gifu Univ.) |
| 2nd Author's Name | Takeshi Hara |
| 2nd Author's Affiliation | Gifu University (Gifu Univ.) |
| 3rd Author's Name | Taiki Nozaki |
| 3rd Author's Affiliation | St.Luke's International Hospital (St.Luke's International Hosp.) |
| 4th Author's Name | Masaki Matsusako |
| 4th Author's Affiliation | St.Luke's International Hospital (St.Luke's International Hosp.) |
| 5th Author's Name | Tetsuro Katafuchi |
| 5th Author's Affiliation | Gifu University of Medical Science (Gifu Medical Univ.) |
| 6th Author's Name | Xiangrong Zhou |
| 6th Author's Affiliation | Gifu University (Gifu Univ.) |
| 7th Author's Name | Hiroshi Fujita |
| 7th Author's Affiliation | Gifu University (Gifu Univ.) |
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| Speaker | Author-1 |
| Date Time | 2022-01-27 16:13:00 |
| Presentation Time | 13 minutes |
| Registration for | MI |
| Paper # | MI2021-87 |
| Volume (vol) | vol.121 |
| Number (no) | no.347 |
| Page | pp.186-187 |
| #Pages | 2 |
| Date of Issue | 2022-01-18 (MI) |
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