Effective semi-supervised learning (SSL) in medical image analysis (MIA) must
address two challenges: 1) work effectively on both multi-class (e.g., lesion
classification) and multi-label (e.g., multiple-disease diagnosis) problems,
and 2) handle imbalanced learning (because of the high variance in disease
prevalence). One strategy to explore in SSL MIA is based on the pseudo
labelling strategy, but it has a few shortcomings. Pseudo-labelling has in
general lower accuracy than consistency learning, it is not specifically
designed for both multi-class and multi-label problems, and it can be
challenged by imbalanced learning. In this paper, unlike traditional methods
that select confident pseudo label by threshold, we propose a new SSL
algorithm, called anti-curriculum pseudo-labelling (ACPL), which introduces
novel techniques to select informative unlabelled samples, improving training
balance and allowing the model to work for both multi-label and multi-class
problems, and to estimate pseudo labels by an accurate ensemble of classifiers
(improving pseudo label accuracy). We run extensive experiments to evaluate
ACPL on two public medical image classification benchmarks: Chest X-Ray14 for
thorax disease multi-label classification and ISIC2018 for skin lesion
multi-class classification. Our method outperforms previous SOTA SSL methods on
both datasets

本文提出了一种反课程伪标记 (ACPL) 的半监督学习算法，它使用新技术选择信息丰富的未标记样本，改善了训练平衡性，并允许模型处理多标签和多类别问题，并通过准确的分类器集成来估计伪标签，该算法通过广泛实验在医学影像分类基准上取得了优异的效果。