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.

翻译：通用异常检测仍是机器学习和医学图像分析中的一个挑战性问题。目前，可以通过单一类别的正常样本学习预期分布，例如通过认知不确定性估计、自编码模型或自监督方式生成的合成异常。与利用已知未知类别样本构建决策边界的方法相比，自监督异常检测方法的性能仍显不足。然而，异常暴露方法通常无法识别未知的未知样本。本文提出一种改进的自监督单类别训练策略，该策略通过放宽特征局部性约束来支持概率推断的近似。我们证明，将直方图均衡化图像用于梯度上采样对最近提出的自监督任务有益。该方法被集成到多种分布外检测模型中，实验结果表明，该方法在多个基准数据集上的性能优于现有最先进技术。