  Large sets of unlabelled data within the healthcare domain remain
underutilized. Active learning offers a way to exploit these datasets by
iteratively requesting an oracle (e.g. medical professional) to label
instances. This process, which can be costly and time-consuming is
overly-dependent upon an oracle. To alleviate this burden, we propose SoQal, a
questioning strategy that dynamically determines when a label should be
requested from an oracle. We perform experiments on five publically-available
datasets and illustrate SoQal's superiority relative to baseline approaches,
including its ability to reduce oracle label requests by up to 35%. SoQal also
performs competitively in the presence of label noise: a scenario that
simulates clinicians' uncertain diagnoses when faced with difficult
classification tasks.

Machine Learning;Machine Learning