| Presentation | 2022-12-16 Data Augmentation Shumpei Takezaki, Kiyohito Tanaka, Seiichi Uchida, Takeaki Kadota, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Disease severity regression by a convolutional neural network (CNN) for medical images requires a sufficient number of image samples labeled with severity levels. Conditional generative adversarial network (cGAN)-based data augmentation (DA) is a possible solution, but it encounters two issues. The first issue is that existing cGANs cannot deal with real-valued severity levels as their conditions, and the second is that the severity of the generated images is not fully reliable. We propose continuous DA as a solution to the two issues. Our method uses continuous severity GAN to generate images at real-valued severity levels and dataset-disjoint multi-objective optimization to deal with the second issue. Our method was evaluated for estimating ulcerative colitis (UC) severity of endoscopic images and achieved higher classification performance than conventional DA methods. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | data augmentation / generative adversarial network / endoscopic images |
| Paper # | PRMU2022-50 |
| Date of Issue | 2022-12-08 (PRMU) |
| Conference Information | |
| Committee | PRMU |
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| Conference Date | 2022/12/15(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Toyama International Conference Center |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Chair | Seiichi Uchida(Kyushu Univ.) |
| Vice Chair | Takuya Funatomi(NAIST) / Mitsuru Anpai(Denso IT Lab.) |
| Secretary | Takuya Funatomi(CyberAgent) / Mitsuru Anpai(Univ. of Tokyo) |
| Assistant | Nakamasa Inoue(Tokyo Inst. of Tech.) / Yasutomo Kawanishi(Riken) |
| Paper Information | |
| Registration To | Technical Committee on Pattern Recognition and Media Understanding |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Data Augmentation |
| Sub Title (in English) | |
| Keyword(1) | data augmentation |
| Keyword(2) | generative adversarial network |
| Keyword(3) | endoscopic images |
| 1st Author's Name | Shumpei Takezaki |
| 1st Author's Affiliation | Kyushu University(Kyushu Univ.) |
| 2nd Author's Name | Kiyohito Tanaka |
| 2nd Author's Affiliation | Kyoto Second Red Cross Hospital(Kyoto Second Red Cross Hospital) |
| 3rd Author's Name | Seiichi Uchida |
| 3rd Author's Affiliation | Kyushu University(Kyushu Univ.) |
| 4th Author's Name | Takeaki Kadota |
| 4th Author's Affiliation | Kyushu University(Kyushu Univ.) |
| Date | 2022-12-16 |
| Paper # | PRMU2022-50 |
| Volume (vol) | vol.122 |
| Number (no) | PRMU-314 |
| Page | pp.pp.95-99(PRMU), |
| #Pages | 5 |
| Date of Issue | 2022-12-08 (PRMU) |