Multi-layer Aggregation as a key to feature-based OOD detection
Benjamin Lambert Florence Forbes Senan Doyle Michel Dojat
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.