A systematic comparison of 23 anomaly detection methods on pathology and industrial image datasets shows that feature distribution methods generally outperform reconstruction and distillation methods, and that epoch selection strategy and image scale strongly affect results.
Machine learning for anomaly detection: A systematic review,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
eess.IV 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology
A systematic comparison of 23 anomaly detection methods on pathology and industrial image datasets shows that feature distribution methods generally outperform reconstruction and distillation methods, and that epoch selection strategy and image scale strongly affect results.