BenchReAD: 一种系统性的视网膜异常检测基准
BenchReAD: A systematic benchmark for retinal anomaly detection
AI总结：
本研究提出BenchReAD基准，旨在解决视网膜异常检测领域缺乏全面且公开的评估标准的问题，通过系统化的数据和算法分类，引入了全监督方法DRA，并改进为NFM-DRA，实现了SOTA性能。
摘要：
视网膜异常检测在筛查眼部和系统性疾病中起着关键作用。尽管其重要性，该领域的进展受到缺乏全面且公开可用的基准的阻碍，这对于公平评估和推进方法至关重要。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（1）异常类型有限且过于简单的限制，（2）测试集几乎饱和，以及（3）缺乏泛化评估的影响，导致实验设置说服力不足。此外，现有医学异常检测基准大多专注于单类监督方法（仅使用负样本训练），忽视了临床实践中大量可用的标记异常数据和未标记数据。为了填补这些差距，我们引入了视网膜异常检测的基准，该基准在数据和算法上都是全面且系统的。通过分类和评估先前方法，我们发现利用解耦异常表示的全监督方法（DRA）取得了最佳性能，但在遇到某些未见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，将其与正常特征记忆结合，以缓解性能下降，建立新的SOTA。该基准可在https://github.com/DopamineLcy/BenchReAD上公开获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
