| Presentation | 2019-01-22 [Short Paper] Towards Annotating Less Medical Images: Changhee Han, Hideaki Hayashi, Leonardo Rundo, Ryosuke Araki, Yudai Nagano, Yujiro Furukawa, Giancarlo Mauri, Hideki Nakayama, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(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) |
| Keyword(in English) | Data Augmentation / Generative Adversarial Networks / Deep Learning |
| Paper # | MI2018-82 |
| Date of Issue | 2019-01-15 (MI) |
| Conference Information | |
| Committee | MI |
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| Conference Date | 2019/1/22(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Medical Image Engineering, Analysis, Recognition, etc. |
| Chair | Kensaku Mori(Nagoya Univ.) |
| Vice Chair | Yoshiki Kawata(Tokushima Univ.) / Yuichi Kimura(Kinki Univ.) |
| Secretary | Yoshiki Kawata(Aichi Inst. of Tech.) / Yuichi Kimura(Nagoya Inst. of Tech.) |
| Assistant | Ryo Haraguchi(Univ. of Hyogo) / Yasushi Hirano(Yamaguchi Univ.) |
| Paper Information | |
| Registration To | Medical Imaging |
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| Language | ENG |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | [Short Paper] 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 |
| 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) |
| Date | 2019-01-22 |
| Paper # | MI2018-82 |
| Volume (vol) | vol.118 |
| Number (no) | MI-412 |
| Page | pp.pp.93-94(MI), |
| #Pages | 2 |
| Date of Issue | 2019-01-15 (MI) |