Deep Learning models are easily disturbed by variations in the input images that were not observed during the training stage, resulting in unpredictable predictions. Detecting such Out-of-Distribution (OOD) images is particularly crucial in the context of medical image analysis, where the range of possible abnormalities is extremely wide. Recently, a new category of methods has emerged, based on the analysis of the intermediate features of a trained model. These methods can be divided into 2 groups: single-layer methods that consider the feature map obtained at a fixed, carefully chosen layer, and multi-layer methods that consider the ensemble of the feature maps generated by the model. While promising, a proper comparison of these algorithms is still lacking. In this work, we compared various feature-based OOD detection methods on a large spectra of OOD (20 types), representing approximately 7800 3D MRIs. Our experiments shed the light on two phenomenons. First, multi-layer methods consistently outperform single-layer approaches, which tend to have inconsistent behaviour depending on the type of anomaly. Second, the OOD detection performance highly depends on the architecture of the underlying neural network.

翻译：深度学习模型易受训练阶段未观测到的输入图像变化干扰，导致不可预测的输出。在医学图像分析领域，检测此类分布外（OOD）图像尤为重要，因为可能的异常范围极为广泛。近年来，一类基于分析训练模型中间特征的新方法涌现出来。这些方法可分为两组：单层方法（在固定且精心选择的层级上分析特征图）和多层方法（分析模型生成的所有特征图的集成）。尽管这些方法前景可观，但尚缺乏对其算法的系统比较。本研究在涵盖20种OOD类型的大范围数据（约7800例3D MRI）上比较了多种基于特征的OOD检测方法。实验揭示两大现象：首先，多层方法始终优于单层方法，后者对异常类型的检测表现具有不一致性；其次，OOD检测性能高度依赖于底层神经网络的架构。