In the last decades, many publicly available large fundus image datasets have been collected for diabetic retinopathy, glaucoma, and age-related macular degeneration, and a few other frequent pathologies. These publicly available datasets were used to develop a computer-aided disease diagnosis system by training deep learning models to detect these frequent pathologies. One challenge limiting the adoption of a such system by the ophthalmologist is, computer-aided disease diagnosis system ignores sight-threatening rare pathologies such as central retinal artery occlusion or anterior ischemic optic neuropathy and others that ophthalmologists currently detect. Aiming to advance the state-of-the-art in automatic ocular disease classification of frequent diseases along with the rare pathologies, a grand challenge on “Retinal Image Analysis for multi-Disease Detection” was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI - 2021). This paper, reports the challenge organization, dataset, top-performing participants solutions, evaluation measures, and results based on a new “Retinal Fundus Multi-disease Image Dataset” (RFMiD). There were two principal sub-challenges: disease screening (i.e. presence versus absence of pathology — a binary classification problem) and disease/pathology classification (a 28-class multi-label classification problem). It received a positive response from the scientific community with 74 submissions by individuals/teams that effectively entered in this challenge. The top-performing methodologies utilized a blend of data-preprocessing, data augmentation, pre-trained model, and model ensembling. This multi-disease (frequent and rare pathologies) detection will enable the development of generalizable models for screening the retina, unlike the previous efforts that focused on the detection of specific diseases.

中文翻译：

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RFMiD：用于多疾病检测挑战的视网膜图像分析


在过去的几十年中，已经收集了许多公开可用的大型眼底图像数据集，用于糖尿病视网膜病变、青光眼和年龄相关性黄斑变性，以及其他一些常见病症。这些公开可用的数据集用于开发计算机辅助疾病诊断系统，通过训练深度学习模型来检测这些常见的病理。限制眼科医生采用此类系统的一个挑战是，计算机辅助疾病诊断系统忽略了威胁视力的罕见病症，例如视网膜中央动脉阻塞或前缺血性视神经病变以及眼科医生目前检测到的其他病症。为了推进常见疾病和罕见病症的自动眼部疾病分类的最新技术，与 IEEE 生物医学成像国际研讨会 （ISBI - 2021） 联合组织了“用于多疾病检测的视网膜图像分析”的重大挑战。本文基于新的 “视网膜眼底多疾病图像数据集” （RFMiD） 报告了挑战组织、数据集、表现最好的参与者解决方案、评估措施和结果。有两个主要的子挑战：疾病筛查（即存在与不存在病理 — 二元分类问题）和疾病/病理分类（28 类多标签分类问题）。它收到了科学界的积极回应，有 74 份由个人/团队提交的作品有效参加了这项挑战赛。表现最好的方法混合使用了数据预处理、数据增强、预训练模型和模型集成。 这种多疾病（常见和罕见病理学）检测将使用于筛查视网膜的可推广模型的开发成为可能，这与以前专注于检测特定疾病的努力不同。