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Clinical settings are often characterized by abundant unlabelled data andlimited labelled data. This is typically driven by the high burden placed onoracles (e.g., physicians) to provide annotations. One way to mitigate thisburden is via active learning (AL) which involves the (a) acquisition and (b)annotation of informative unlabelled instances. Whereas previous work addresseseither one of these elements independently, we propose an AL framework thataddresses both. For acquisition, we propose Bayesian Active Learning byConsistency (BALC), a sub-framework which perturbs both instances and networkparameters and quantifies changes in the network output probabilitydistribution. For annotation, we propose SoQal, a sub-framework thatdynamically determines whether, for each acquired unlabelled instance, torequest a label from an oracle or to pseudo-label it instead. We show that BALCcan outperform start-of-the-art acquisition functions such as BALD, and SoQaloutperforms baseline methods even in the presence of a noisy oracle.

SoQal: Selective Oracle Questioning for Consistency Based Active Learning of Cardiac Signals