论文标题
研究糖尿病性视网膜病的基准：分割，分级和可转移性
A Benchmark for Studying Diabetic Retinopathy: Segmentation, Grading, and Transferability
论文作者
论文摘要
糖尿病患者有患眼科疾病的风险，称为糖尿病性视网膜病（DR）。当高血糖水平损害视网膜血管时，就会发生这种疾病。由于深度学习的巨大成功，计算机辅助DR诊断是早期检测DR和严重性分级的有前途的工具。但是，由于缺乏一致且细粒度的注释，大多数当前的DR诊断系统无法为眼科医生带来令人满意的性能或可解释性。为了解决这个问题，我们构建了一个包含2,842张图像（FGADR）的大颗粒注释的DR数据集。该数据集具有1,842张图像，具有像素级DR相关的病变注释，以及1,000张图像，图像由六位具有评估者一致性的六位董事会认证的眼科医生分级的图像级标签。拟议的数据集将对DR诊断进行广泛的研究。我们设置了三个基准任务以进行评估：1。Dr病变细分； 2。通过联合分类和分割进行DR分级； 3。针对眼部多疾病识别的转移学习。此外，为第三任任务引入了一种新型的归纳转移学习方法。使用不同最先进方法的大量实验在我们的FGADR数据集上进行，该数据集可以作为未来研究的基准。
People with diabetes are at risk of developing an eye disease called diabetic retinopathy (DR). This disease occurs when high blood glucose levels cause damage to blood vessels in the retina. Computer-aided DR diagnosis is a promising tool for early detection of DR and severity grading, due to the great success of deep learning. However, most current DR diagnosis systems do not achieve satisfactory performance or interpretability for ophthalmologists, due to the lack of training data with consistent and fine-grained annotations. To address this problem, we construct a large fine-grained annotated DR dataset containing 2,842 images (FGADR). This dataset has 1,842 images with pixel-level DR-related lesion annotations, and 1,000 images with image-level labels graded by six board-certified ophthalmologists with intra-rater consistency. The proposed dataset will enable extensive studies on DR diagnosis. We set up three benchmark tasks for evaluation: 1. DR lesion segmentation; 2. DR grading by joint classification and segmentation; 3. Transfer learning for ocular multi-disease identification. Moreover, a novel inductive transfer learning method is introduced for the third task. Extensive experiments using different state-of-the-art methods are conducted on our FGADR dataset, which can serve as baselines for future research.
