Towards Realistic Semi-supervised Medical Image Classification

Existing semi-supervised learning (SSL) approaches follow the idealized closed-world assumption, neglecting the challenges present in realistic medical scenarios, such as open-set distribution and imbalanced class distribution. Although some methods in natural domains attempt to address the open-set problem, they are insufficient for medical domains, where intertwined challenges like class imbalance and small inter-class lesion discrepancies persist. Thus, this paper presents a novel self-recalibrated semantic training framework, which is tailored for SSL in medical imaging by ingeniously harvesting realistic unlabeled samples. Inspired by the observation that certain open-set samples share some similar disease-related representations with in-distribution samples, we first propose an informative sample selection strategy that identifies high-value samples to serve as augmentations, thereby effectively enriching the semantics of known categories. Furthermore, we adopt a compact semantic clustering strategy to address the semantic confusion raised by the above newly introduced open-set semantics. Moreover, to mitigate the interference of class imbalance in open-set SSL, we introduce a less biased dual-balanced classifier with similarity pseudo-label regularization and category-customized regularization. Extensive experiments on a variety of medical image datasets demonstrate the superior performance of our proposed method over state-of-the-art Closed-set and Open-set SSL methods.