Confidence-Aware and Self-Supervised Image Anomaly Localisation

2023-03-23 · Johanna P. Müller, Matthew Baugh, Jeremy Tan, Mischa Dombrowski, Bernhard Kainz

Universal anomaly detection still remains a challenging problem in machine learning and medical image analysis. It is possible to learn an expected distribution from a single class of normative samples, e.g., through epistemic uncertainty estimates, auto-encoding models, or from synthetic anomalies in a self-supervised way. The performance of self-supervised anomaly detection approaches is still inferior compared to methods that use examples from known unknown classes to shape the decision boundary. However, outlier exposure methods often do not identify unknown unknowns. Here we discuss an improved self-supervised single-class training strategy that supports the approximation of probabilistic inference with loosen feature locality constraints. We show that up-scaling of gradients with histogram-equalised images is beneficial for recently proposed self-supervision tasks. Our method is integrated into several out-of-distribution (OOD) detection models and we show evidence that our method outperforms the state-of-the-art on various benchmark datasets.

📄 PDF Abstract BibTeX arXiv:2303.13227

Code (1)

ividja/Probabilistic-PII 공식 구현 pytorch

Tasks

Anomaly DetectionMedical Image AnalysisOut of Distribution (OOD) Detection Self-Supervised Anomaly Detection Supervised Anomaly Detection

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