BMAD: Benchmarks for Medical Anomaly Detection

Jinan Bao, Hanshi Sun, Hanqiu Deng, Yinsheng He, Zhaoxiang Zhang, Xingyu Li; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 4042-4053

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 the field of medical imaging AD plays a crucial role in identifying anomalies that may indicate rare diseases or conditions. However despite its importance there is currently 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 address this gap we present a comprehensive evaluation benchmark for assessing AD methods on medical images. This benchmark consists of 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 fifteen state-of-the-art AD algorithms. This standardized and well-curated medical benchmark with the well-structured codebase enables researchers to easily compare and evaluate different AD methods and ultimately leads to the development of more effective and robust AD algorithms for medical imaging. More information on BMAD is available in our GitHub repository: https://github.com/DorisBao/BMAD.