标题： BenchReAD：视网膜异常检测的系统基准 显示英文标题

标题： BenchReAD: A systematic benchmark for retinal anomaly detection

摘要： 视网膜异常检测在筛查眼部和全身疾病中起着关键作用。尽管其重要性，该领域的发展一直受到缺乏全面且公开可用的基准的阻碍，这对于方法的公平评估和进步是必不可少的。由于这一限制，与视网膜图像相关的先前异常检测工作受到以下约束：(1) 异常类型有限且过于简单，(2) 测试集几乎饱和，(3) 缺乏泛化评估，导致实验设置不够令人信服。此外，现有的医学异常检测基准主要关注单类监督方法（仅使用负样本进行训练），忽视了临床上常见的大量标记异常数据和未标记数据。为了弥补这些差距，我们引入了一个全面且系统的视网膜异常检测基准。通过分类和基准测试以前的方法，我们发现一种利用异常解耦表示（DRA）的完全监督方法表现最佳，但在遇到某些未见过的异常时性能显著下降。受单类监督学习中记忆库机制的启发，我们提出了NFM-DRA，它将DRA与正常特征内存相结合，以减轻性能下降，建立了新的最先进水平。该基准可在https://github.com/DopamineLcy/BenchReAD公开获取。 显示英文摘要

摘要： Retinal anomaly detection plays a pivotal role in screening ocular and systemic diseases. Despite its significance, progress in the field has been hindered by the absence of a comprehensive and publicly available benchmark, which is essential for the fair evaluation and advancement of methodologies. Due to this limitation, previous anomaly detection work related to retinal images has been constrained by (1) a limited and overly simplistic set of anomaly types, (2) test sets that are nearly saturated, and (3) a lack of generalization evaluation, resulting in less convincing experimental setups. Furthermore, existing benchmarks in medical anomaly detection predominantly focus on one-class supervised approaches (training only with negative samples), overlooking the vast amounts of labeled abnormal data and unlabeled data that are commonly available in clinical practice. To bridge these gaps, we introduce a benchmark for retinal anomaly detection, which is comprehensive and systematic in terms of data and algorithm. Through categorizing and benchmarking previous methods, we find that a fully supervised approach leveraging disentangled representations of abnormalities (DRA) achieves the best performance but suffers from significant drops in performance when encountering certain unseen anomalies. Inspired by the memory bank mechanisms in one-class supervised learning, we propose NFM-DRA, which integrates DRA with a Normal Feature Memory to mitigate the performance degradation, establishing a new SOTA. The benchmark is publicly available at https://github.com/DopamineLcy/BenchReAD.