ACPL: Anti-curriculum Pseudo-labelling for Semi-supervised Medical Image Classification
This work addresses semi-supervised learning for medical image classification, offering improvements for tasks like lesion and disease diagnosis, but it appears incremental as it builds on pseudo-labelling strategies.
The paper tackled the challenges of semi-supervised learning in medical image analysis, such as handling multi-class and multi-label problems and imbalanced data, by proposing ACPL, which outperformed previous state-of-the-art methods on benchmarks like Chest X-Ray14 and ISIC2018.
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