MedMatch: Design of Semi-supervised learning Model with Curriculum Pseudo-Labels for Medical Image Classification

Deep neural networks are extensively employed in CAD (computer-aided diagnosis) to enhance diagnostic efficiency, accuracy, and reliability. However, relying on manually annotation, labeling medical images is costly and time-consuming. Consequently, obtaining labeled data of sufficient scale and high quality poses a challenge. In the field of data science, semi-supervised learning (SSL) methods have significantly reduced the burden of dataset labeling. However, two primary issues exist: (1) the multi-classification challenge involving simultaneous classification of multiple lesions and diagnosis of multiple diseases; (2) data imbalance results in significant variations in disease prevalence rates. In this paper, the design of Curriculum Pseudo-labels (CPL) methods is adopted to overcome the limitations of applying SSL methods across data science and CAD. Additionally, a novel strategy called MedAugment is proposed for enhancing the model's robustness and generalization ability. Experimental results show that the design of MedMatch model reaches 94.21% in AUC score compared with state-of-the-art SSL methods on CAD task.