SoCal: Selective Oracle Questioning for Consistency-based Active Learning of Cardiac Signals

Dani Kiyasseh, Tingting Zhu, David A. Clifton

Keywords: Active learning, consistency-training, cardiac signals, healthcare

Abstract: The ubiquity and rate of collection of cardiac signals produce large, unlabelled datasets. Active learning (AL) can exploit such datasets by incorporating human annotators (oracles) to improve generalization performance. However, the over-reliance of existing algorithms on oracles continues to burden physicians. To minimize this burden, we propose SoCal, a consistency-based AL framework that dynamically determines whether to request a label from an oracle or to generate a pseudo-label instead. We show that our framework decreases the labelling burden while maintaining strong performance, even in the presence of a noisy oracle.

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Reviewed Version (pdf): https://openreview.net/references/pdf?id=FDziIorwas