Novel disease detection using ensembles with regularized disagreement

Automated medical diagnosis systems need to be able to recognize when new diseases emerge, that are not represented in the training data (ID). Even though current out-of-distribution (OOD) detection algorithms can successfully distinguish completely different data sets, they fail to reliably identify samples from novel classes, that are similar to the training data. We develop a new ensemble-based procedure that promotes model diversity and exploits regularization to limit disagreement to only OOD samples, using a batch containing an unknown mixture of ID and OOD data. We show that our procedure significantly outperforms state-of-the-art methods, including those that have access, during training, to data that is known to be OOD. We run extensive comparisons of our approach on a variety of novel-class detection scenarios, on standard image data sets as well as on new disease detection on medical image data sets.