Imbalance-Aware Robust Representation Learning for Medical Image Binary Classification

Abstract

Class imbalance is a prevalent issue in medical image classification that significantly degrades a model's capacity to recognize minority-class lesions, thereby restricting its applicability in real-world clinical screening scenarios. Existing studies typically address this problem through data resampling, loss re-weighting, or decision boundary adjustment strategies; however, these methods predominantly focus on compensation during the classification stage. In contrast, the representation learning process in earlier stages is often dominated by easy majority-class samples, and its impact on the feature quality of minority classes has not received adequate attention. To address this issue, we propose an Imbalance-Aware Robust Representation Learning (IRRL) framework for class-imbalanced medical image classification. IRRL prioritizes the refinement of minority-class-related local representations before global classification. Specifically, implicit local token representations are constructed from convolutional feature maps based on their receptive-field structure. Semantic confidence-guided reliability estimation, difficulty-adaptive supervised contrastive learning, and minority-class prototype regularization are then introduced to improve the learning of informative local representations and hard minority-class samples. Finally, a Transformer performs global context modeling for image-level classification. Experiments on four public datasets, including ISIC 2018, PAD-UFES-20, OCTID, and BUSI, show that IRRL achieves balanced classification performance, with favorable F1-score and Matthews Correlation Coefficient (MCC) results that reflect improved minority-class recognition quality. The results across datasets with different imaging modalities and imbalance conditions further demonstrate the robustness and consistency of the proposed representation learning strategy.