  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
