Semi-Supervised GANs with Complementary Generator Pair for Retinopathy Screening
文献类型：会议论文
| 作者 | Xie, Yingpeng; Wan, Qiwei; Xie, Hai; Lei, Baiying; Tan, Ee-Leng; Xu, Yanwu |
| 出版日期 | 2021 |
| 会议日期 | JAN 10-15, 2021 |
| DOI | 10.1109/ICPR48806.2021.9412059 |
| 英文摘要 | Several typical types of retinopathy are major causes of blindness. However, early detection of retinopathy is quite not easy since few symptoms are observable in the early stage, attributing to the development of non-mydriatic retinal cameras, these cameras produce high-resolution retinal fundus images that provide the possibility of Computer-Aided-Diagnosis (CAD) via deep learning to assist diagnosing retinopathy. Deep learning algorithms usually rely on a large number of labeled images that are expensive and time-consuming to obtain in the medical imaging area. Moreover, the random distribution of various lesions that often vary greatly in size also brings significant challenges to learn discriminative information from high-resolution fundus images. In this paper, we present generative adversarial networks simultaneously equipped with a good generator and a bad generator (GBGANs) to make up for the incomplete data distribution given limited fundus images. To improve the generative feasibility of the generator, we introduce a pre-trained feature extractor to acquire condensed features for each fundus image in advance. Experimental results on integrated three public iChallenge datasets show that the proposed GBGANs could fully utilize the available fundus images to identify retinopathy with little label cost. |
| 会议录出版者 | International Conference on Pattern Recognition |
| 学科主题 | Computer Science ; Engineering ; Imaging Science & Photographic Technology |
| ISSN号 | 1051-4651 |
| ISBN号 | 978-1-7281-8808-9 |
| 源URL | [http://ir.nimte.ac.cn/handle/174433/23265]  |
| 专题 | 会议专题 会议专题_会议论文 |
| 推荐引用方式 GB/T 7714 | Xie, Yingpeng,Wan, Qiwei,Xie, Hai,et al. Semi-Supervised GANs with Complementary Generator Pair for Retinopathy Screening[C]. 见:. JAN 10-15, 2021. |
入库方式： OAI收割
来源：宁波材料技术与工程研究所
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