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

TL;DR

This paper introduces SoQal, a novel active learning framework for cardiac signal analysis that intelligently balances oracle questioning and pseudo-labeling, reducing annotation effort while maintaining high accuracy.

Contribution

It proposes BALC for improved instance acquisition and SoQal for dynamic annotation decision-making, addressing both in a unified active learning framework.

Findings

BALC outperforms BALD in acquisition tasks.

SoQal maintains accuracy with noisy oracles.

Framework reduces annotation burden in clinical settings.

Abstract

Clinical settings are often characterized by abundant unlabelled data and limited labelled data. This is typically driven by the high burden placed on oracles (e.g., physicians) to provide annotations. One way to mitigate this burden is via active learning (AL) which involves the (a) acquisition and (b) annotation of informative unlabelled instances. Whereas previous work addresses either one of these elements independently, we propose an AL framework that addresses both. For acquisition, we propose Bayesian Active Learning by Consistency (BALC), a sub-framework which perturbs both instances and network parameters and quantifies changes in the network output probability distribution. For annotation, we propose SoQal, a sub-framework that dynamically determines whether, for each acquired unlabelled instance, to request a label from an oracle or to pseudo-label it instead. We show that BALC can outperform start-of-the-art acquisition functions such as BALD, and SoQal outperforms baseline methods even in the presence of a noisy oracle.