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Effective semi-supervised learning (SSL) in medical image analysis (MIA) mustaddress two challenges: 1) work effectively on both multi-class (e.g., lesionclassification) and multi-label (e.g., multiple-disease diagnosis) problems,and 2) handle imbalanced learning (because of the high variance in diseaseprevalence). One strategy to explore in SSL MIA is based on the pseudolabelling strategy, but it has a few shortcomings. Pseudo-labelling has ingeneral lower accuracy than consistency learning, it is not specificallydesigned for both multi-class and multi-label problems, and it can bechallenged by imbalanced learning. In this paper, unlike traditional methodsthat select confident pseudo label by threshold, we propose a new SSLalgorithm, called anti-curriculum pseudo-labelling (ACPL), which introducesnovel techniques to select informative unlabelled samples, improving trainingbalance and allowing the model to work for both multi-label and multi-classproblems, and to estimate pseudo labels by an accurate ensemble of classifiers(improving pseudo label accuracy). We run extensive experiments to evaluateACPL on two public medical image classification benchmarks: Chest X-Ray14 forthorax disease multi-label classification and ISIC2018 for skin lesionmulti-class classification. Our method outperforms previous SOTA SSL methods onboth datasets

ACPL: Anti-curriculum Pseudo-labelling for Semi-supervised Medical Image Classification