Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology

2025-06-23

AI Summary

Digital pathological image anomaly detection faces unique challenges—including high resolution, multi-scale tissue structures, staining variability, and repetitive patterns—leading to insufficient generalization and robustness in existing methods. To address this, we introduce the first systematic benchmark specifically designed for digital pathology, comprising five real-world and synthetic datasets, and quantitatively evaluating over 20 state-of-the-art approaches spanning reconstruction-based, feature-embedding, contrastive learning, and self-supervised paradigms. Crucially, we conduct the first fine-grained analysis across three dimensions: image scale, anomaly type, and training strategy. Our empirical study delineates performance boundaries and applicability conditions of each method under diverse pathological scenarios. The benchmark provides a fully reproducible evaluation framework and evidence-based guidance for method selection, establishing a foundational resource for future research in pathological anomaly detection.

Technology Category

• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Machine Learning: Evaluation and Analysis
• Computer Vision: Medical and Biological Imaging

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

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 their large size, multi-scale structures, stain variability, and repetitive patterns, introduce new challenges that current anomaly detection algorithms struggle to address. In this quantitative study, we benchmark over 20 classical and prevalent anomaly detection methods through extensive experiments. We curated five digital pathology datasets, both real and synthetic, to systematically evaluate these approaches. 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 guide future research in anomaly detection for digital pathology images.

Problem

• Benchmarking anomaly detection methods for digital pathology images
• Addressing challenges like large size and stain variability in pathology
• Evaluating performance across image scales and anomaly types

Innovation

• Benchmarking 20+ anomaly detection methods
• Using real and synthetic pathology datasets
• Evaluating scale, anomaly types, training strategies