Anomaly detection (AD) is a fundamental research problem in machine learning and computer vision, with practical applications in industrial inspection, video surveillance, and medical diagnosis. In medical imaging, AD is especially vital for detecting and diagnosing anomalies that may indicate rare diseases or conditions. However, there is a lack of a universal and fair benchmark for evaluating AD methods on medical images, which hinders the development of more generalized and robust AD methods in this specific domain. To bridge this gap, we introduce a comprehensive evaluation benchmark for assessing anomaly detection methods on medical images. This benchmark encompasses six reorganized datasets from five medical domains (i.e. brain MRI, liver CT, retinal OCT, chest X-ray, and digital histopathology) and three key evaluation metrics, and includes a total of fourteen state-of-the-art AD algorithms. This standardized and well-curated medical benchmark with the well-structured codebase enables comprehensive comparisons among recently proposed anomaly detection methods. It will facilitate the community to conduct a fair comparison and advance the field of AD on medical imaging. More information on BMAD is available in our GitHub repository: https://github.com/DorisBao/BMAD

翻译：异常检测（AD）是机器学习和计算机视觉领域的一个基础研究问题，在工业检测、视频监控和医学诊断中具有实际应用。在医学影像中，AD对于检测和诊断可能指示罕见疾病或病症的异常尤为重要。然而，目前缺乏一个通用且公平的基准来评估医学图像上的AD方法，这阻碍了该特定领域内更通用、更鲁棒的AD方法的发展。为弥补这一空白，我们引入了一个用于评估医学图像异常检测方法的综合性评估基准。该基准涵盖了来自五个医学领域（即脑部MRI、肝脏CT、视网膜OCT、胸部X线和数字组织病理学）的六个重组数据集，以及三个关键评估指标，并包含总共十四种最先进的AD算法。这个标准化且精心整理的医学基准，配合结构完善的代码库，能够对近期提出的异常检测方法进行全面比较。它将促进学界进行公平比较，并推动医学影像AD领域的发展。更多关于BMAD的信息请参见我们的GitHub仓库：https://github.com/DorisBao/BMAD