  Medical image classification is often challenging for two reasons: a lack of
labelled examples due to expensive and time-consuming annotation protocols, and
imbalanced class labels due to the relative scarcity of disease-positive
individuals in the wider population. Semi-supervised learning (SSL) methods
exist for dealing with a lack of labels, but they generally do not address the
problem of class imbalance. In this study we propose Adaptive Blended
Consistency Loss (ABCL), a drop-in replacement for consistency loss in
perturbation-based SSL methods. ABCL counteracts data skew by adaptively mixing
the target class distribution of the consistency loss in accordance with class
frequency. Our experiments with ABCL reveal improvements to unweighted average
recall on two different imbalanced medical image classification datasets when
compared with existing consistency losses that are not designed to counteract
class imbalance.
