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| Presentation | 2020-03-11 14:10 Accuracy of Brain Tumor Detection and Classification Based on Under Sampled k-Space Signals Tania Sultana, Sho Kurosaki, Yutaka Jitsumatsu, Junichi Takeuchi (Kyushu Univ.)  IBISML2019-46 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | The prime concern of Magnetic Resonance Imaging (MRI) is to optimize examination time by assuring a good quality of the images. In this aspect, a newly developed deep learning method, called multi-resolution CNN (MRCNN), was proposed by Kitazaki et al. The key focus of MRCNN is that, it can restore high quality image from under sampled $k$-space signals. Kitazaki et al. evaluated its performance in term of Peak Signal to Noise Ratio (PSNR). The aim of this study is to evaluate the performance of MRCNN in the field of brain tumor detection and classification based on transfer learning. This paper highlights the accuracy of detection and classification using mRCNN is significantly higher in contrast without MRCNN. |
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| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | MRI reconstruction / under sampled k-space signals / transfer learning / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 119, no. 476, IBISML2019-46, pp. 91-94, March 2020. | |
| Paper # | IBISML2019-46 | |
| Date of Issue | 2020-03-03 (IBISML) | |
| 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 | IBISML2019-46 |
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| Conference Information | |
| Committee | IBISML |
| Conference Date | 2020-03-10 - 2020-03-11 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Kyoto University |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Machine learning, etc. |
| Paper Information | |
| Registration To | IBISML |
| Conference Code | 2020-03-IBISML |
| Language | English |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Accuracy of Brain Tumor Detection and Classification Based on Under Sampled k-Space Signals |
| Sub Title (in English) | |
| Keyword(1) | MRI reconstruction |
| Keyword(2) | under sampled k-space signals |
| Keyword(3) | transfer learning |
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| 1st Author's Name | Tania Sultana |
| 1st Author's Affiliation | Kyushu University (Kyushu Univ.) |
| 2nd Author's Name | Sho Kurosaki |
| 2nd Author's Affiliation | Kyushu University (Kyushu Univ.) |
| 3rd Author's Name | Yutaka Jitsumatsu |
| 3rd Author's Affiliation | Kyushu University (Kyushu Univ.) |
| 4th Author's Name | Junichi Takeuchi |
| 4th Author's Affiliation | Kyushu University (Kyushu Univ.) |
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| Speaker | Author-1 |
| Date Time | 2020-03-11 14:10:00 |
| Presentation Time | 25 minutes |
| Registration for | IBISML |
| Paper # | IBISML2019-46 |
| Volume (vol) | vol.119 |
| Number (no) | no.476 |
| Page | pp.91-94 |
| #Pages | 4 |
| Date of Issue | 2020-03-03 (IBISML) |
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