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| Presentation | 2019-01-22 14:50 [Short Paper] Towards Annotating Less Medical Images: -- PGGAN-based MR Image Augmentation for Brain Tumor Detection -- Changhee Han (UTokyo), Hideaki Hayashi (Kyushu Univ.), Leonardo Rundo (Univ. Cambridge), Ryosuke Araki (Chubu Univ.), Yudai Nagano (UTokyo), Yujiro Furukawa (Kanto Rosai Hosp.), Giancarlo Mauri (Univ. Milano-Bicocca), Hideki Nakayama (UTokyo)  MI2018-82 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | How can we tackle the lack of available annotated medical image data through Data Augmentation (DA) techniques for accurate computer-assisted diagnosis? To fill the data lack in the real image distribution, we synthesize brain contrast-enhanced Magnetic Resonance (MR) images---realistic but completely different from the original ones---using Generative Adversarial Networks (GANs). Especially, we exploit Progressive Growing of GANs (PGGANs) to generate original-sized 256 × 256 brain MR images. Our results show that this novel PGGAN-based medical DA method can achieve better performance, when combined with classical DA and GAN-based refinement, in convolutional neural network-based tumor detection and also in other medical imaging tasks. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Data Augmentation / Generative Adversarial Networks / Deep Learning / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 118, no. 412, MI2018-82, pp. 93-94, Jan. 2019. | |
| Paper # | MI2018-82 | |
| Date of Issue | 2019-01-15 (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 | MI2018-82 |
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| Conference Information | |
| Committee | MI |
| Conference Date | 2019-01-22 - 2019-01-23 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Medical Image Engineering, Analysis, Recognition, etc. |
| Paper Information | |
| Registration To | MI |
| Conference Code | 2019-01-MI |
| Language | English |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Towards Annotating Less Medical Images: |
| Sub Title (in English) | PGGAN-based MR Image Augmentation for Brain Tumor Detection |
| Keyword(1) | Data Augmentation |
| Keyword(2) | Generative Adversarial Networks |
| Keyword(3) | Deep Learning |
| Keyword(4) | |
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| 1st Author's Name | Changhee Han |
| 1st Author's Affiliation | The University of Tokyo (UTokyo) |
| 2nd Author's Name | Hideaki Hayashi |
| 2nd Author's Affiliation | Kyushu University (Kyushu Univ.) |
| 3rd Author's Name | Leonardo Rundo |
| 3rd Author's Affiliation | University of Cambridge (Univ. Cambridge) |
| 4th Author's Name | Ryosuke Araki |
| 4th Author's Affiliation | Chubu University (Chubu Univ.) |
| 5th Author's Name | Yudai Nagano |
| 5th Author's Affiliation | The University of Tokyo (UTokyo) |
| 6th Author's Name | Yujiro Furukawa |
| 6th Author's Affiliation | Kanto Rosai Hospital (Kanto Rosai Hosp.) |
| 7th Author's Name | Giancarlo Mauri |
| 7th Author's Affiliation | University of Milano-Bicocca (Univ. Milano-Bicocca) |
| 8th Author's Name | Hideki Nakayama |
| 8th Author's Affiliation | The University of Tokyo (UTokyo) |
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| Speaker | Author-1 |
| Date Time | 2019-01-22 14:50:00 |
| Presentation Time | 15 minutes |
| Registration for | MI |
| Paper # | MI2018-82 |
| Volume (vol) | vol.118 |
| Number (no) | no.412 |
| Page | pp.93-94 |
| #Pages | 2 |
| Date of Issue | 2019-01-15 (MI) |
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