Quantitative benchmarking of anomaly detection methods in digital pathology images.

Anomaly detection has been widely studied in the context of industrial defect inspection, with numerous methods developed to tackle a range of challenges. In digital pathology, anomaly detection holds significant potential for applications such as rare disease identification, artifact detection, and biomarker discovery. However, the unique characteristics of pathology images-such as large size, multi-scale structures, stain variability, and repetitive patterns-pose new challenges that current anomaly detection algorithms struggle to overcome. In this quantitative study, we benchmark 23 classical anomaly detection methods through extensive experiments. We systematically evaluate these approaches using five digital pathology datasets, including both real and synthetic cases. Our experiments investigate the influence of image scale, anomaly pattern types, and training epoch selection strategies on detection performance. The results provide a detailed comparison of each method's strengths and limitations, establishing a comprehensive benchmark to inform future research in anomaly detection for digital pathology. In addition, we review the current applications of anomaly detection algorithms in the field of pathology images. The code and simulation data will be publicly available at https://github.com/hrlblab/PathAnomalyDetect.