Fine-Grained Out-of-Distribution Detection of Medical Images using Combination of Feature Uncertainty and Mahalanobis Distance

Automatic medical image classification methods are often faced with out-of-distribution (OOD) testing samples belonging to disease classes that are unseen during training, and incorrectly predict these samples into one of the categories in the training dataset, i.e, in-distribution (ID) classes, leading to unreliable outputs. Therefore, it is essential to detect OOD samples for reliable prediction of models. To address this issue, we propose a simple yet effective approach that combines the uncertainty of multi-scale features of a test sample and the Mahalanobis distance between the test sample and distributions of training classes in the feature space through logistic regression. We evaluated our approach on a dataset containing five fine-grained lung conditions and designed three ID-OOD splits. The experimental results averaged over the three splits show that our method outperforms existing methods in detecting OOD samples in medical images.