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Deep Learning models are easily disturbed by variations in the input imagesthat were not observed during the training stage, resulting in unpredictablepredictions. Detecting such Out-of-Distribution (OOD) images is particularlycrucial in the context of medical image analysis, where the range of possibleabnormalities is extremely wide. Recently, a new category of methods hasemerged, based on the analysis of the intermediate features of a trained model.These methods can be divided into 2 groups: single-layer methods that considerthe feature map obtained at a fixed, carefully chosen layer, and multi-layermethods that consider the ensemble of the feature maps generated by the model.While promising, a proper comparison of these algorithms is still lacking. Inthis work, we compared various feature-based OOD detection methods on a largespectra of OOD (20 types), representing approximately 7800 3D MRIs. Ourexperiments shed the light on two phenomenons. First, multi-layer methodsconsistently outperform single-layer approaches, which tend to haveinconsistent behaviour depending on the type of anomaly. Second, the OODdetection performance highly depends on the architecture of the underlyingneural network.

Multi-layer Aggregation as a key to feature-based OOD detection