Multi-Label Retinal Disease (MuReD)

Early detection of retinal diseases is one of the most important means of preventing partial or permanent blindness in patients. One of the major stumbling blocks for manual retinal examination is the lack of a sufficient number of qualified medical personnel per capita to diagnose diseases. Computer-aided diagnosis systems (CAD) have proven to be very effective in helping physicians reduce the time taken to make a diagnosis and minimize variability in image interpretation, but they are not flexible enough to accommodate the simultaneous presence of multiple retinal diseases, which is a common situation in real-world applications. In the past years, few datasets that focus on the classification of multiple retinal pathologies present at the same time, i.e., multi-label classification have been proposed, but there are some shared problems with all of them, such as a narrow range of pathologies to classify, high level of class imbalance, low amount of samples for the underrepresented labels, no assurance in image quality, among others. All these problems hinder the performance of any model trained with these datasets, which leads to poor robustness, lack of generalization, and reduced trustability in its predictions.To address these problems, we constructed the Multi-Label Retinal Diseases (MuReD) dataset, using images collected from three different state-of-the-art sources, i.e., ARIA, STARE, and RFMiD datasets, and performing a sequence of post-processing steps to ensure the quality of the images, a wide range of diseases to classify, and a sufficient number of samples per disease label.The MuReD dataset consists of 2208 images with 20 different labels, with varying image quality and resolution, and at the same time, ensuring a minimal degree of quality in the data, with a sufficient number of samples per label. To the best of our knowledge, the MuReD dataset, is the only publicly available dataset that applies a sequence of post-processing steps to ensure the quality of the images, the variety of pathologies, and the number of samples per label, resulting in increased data quality and a significant reduction of the class imbalance present in the publicly available datasets.It is envisaged that the MuReD dataset will enable the creation of more robust, general, and trustable models for the automatic detection and classification of retinal diseases.

视网膜疾病的早期检测是预防患者部分或永久性失明的最重要手段之一。人工视网膜检查的主要障碍之一是人均合格医疗人员数量不足，难以完成疾病诊断。计算机辅助诊断系统（CAD）已被证明能有效帮助医生缩短诊断时间，减少图像解读的变异性，但它们的灵活性不足，无法应对多种视网膜疾病同时存在的情况——这在实际应用中十分常见。过去几年中，针对多种视网膜病变同时存在的分类（即多标签分类，multi-label classification）的数据集较少，但这些现有数据集存在一些共性问题，例如可分类的病变范围狭窄、类别不平衡程度高、代表性不足的标签样本量少、图像质量无保证等。所有这些问题都会影响基于这些数据集训练的模型性能，导致模型鲁棒性差、泛化能力不足，预测结果的可信度降低。为解决这些问题，我们构建了多标签视网膜疾病数据集（MuReD），其图像来源于三个先进的数据集：ARIA、STARE和RFMiD，并通过一系列后处理步骤确保图像质量、扩大可分类疾病范围，同时保证每个疾病标签的样本量充足。MuReD数据集包含2208张图像，涵盖20种不同标签，图像质量和分辨率各异，同时确保数据达到最低质量标准，且每个标签的样本量充足。据我们所知，MuReD是目前唯一公开可用的、通过一系列后处理步骤保障图像质量、病变多样性及标签样本量的数据集，这不仅提升了数据质量，还显著缓解了现有公开数据集存在的类别不平衡问题。我们期望MuReD数据集能助力构建更鲁棒、更通用、更可信的视网膜疾病自动检测与分类模型。