A Novel Deep Learning Method for Nuclear Cataract Classification Based on Anterior Segment Optical Coherence Tomography Images
文献类型：会议论文
| 作者 | Zhang, Xiaoqing; Xiao, Zunjie; Higashita, Risa; Chen, Wan; Yuan, Jin; Fang, Jiansheng; Hu, Yan; Liu, Jiang |
| 出版日期 | 2020 |
| 会议日期 | OCT 11-14, 2020 |
| 英文摘要 | Nuclear cataract is one of the most common types of cataract. In the recent, ophthalmologists are increasingly using anterior segment optical coherence tomography (AS-OCT) images to diagnose many ocular diseases including cataract. The relationship between cataract and the lens opacity based on AS-OCT images has been being studied in clinical pioneer research. However, using AS-OCT images to classify cataract automatically based on computer-aided diagnosis (CAD) technique has not been seriously studied. This paper proposes a novel Convolutional Neural Network (CNN) model named GraNet for nuclear cataract classification based on AS-OCT images. In the GraNet, we introduce a grading block to learn high-level feature representations based on the pointwise convolution method. To further improve the classification performance, we propose a simple and efficient cross-training method is comprised of focal loss and cross-entropy loss. Extensive experiments are conducted on the AS-OCT image dataset, the results demonstrate that the proposed methods achieve better nuclear cataract classification results than baselines. |
| 会议录出版者 | IEEE International Conference on Systems Man and Cybernetics Conference Proceedings |
| 学科主题 | Computer Science |
| ISSN号 | 1062-922X |
| ISBN号 | 978-1-7281-8526-2 |
| 源URL | [http://ir.nimte.ac.cn/handle/174433/23264]  |
| 专题 | 会议专题 会议专题_会议论文 |
| 推荐引用方式 GB/T 7714 | Zhang, Xiaoqing,Xiao, Zunjie,Higashita, Risa,et al. A Novel Deep Learning Method for Nuclear Cataract Classification Based on Anterior Segment Optical Coherence Tomography Images[C]. 见:. OCT 11-14, 2020. |
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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