DH-OOD: A decoupled hybrid framework for robust skin lesion classification via semantic-structural fusion

Abstract

Real-world skin lesion classification faces three major challenges: severe class imbalance, high intra-class variability, and the need to reject out-of-distribution (OOD) samples. Conventional monolithic models often struggle to address these issues simultaneously. To mitigate this limitation, we propose a multi-stage decoupled hybrid framework that combines Supervised Contrastive Learning (SupCon) with structural reconstruction. First, representation learning is decoupled using SupCon. Compared with standard cross-entropy training, SupCon alleviates feature degradation under long-tailed distributions by encouraging a more balanced feature space. Second, to address open-set recognition, we integrate contrastive semantic features with structural anomaly scores derived from an independent Convolutional Autoencoder (CAE). These complementary signals—semantic confidence and reconstruction error—are fused through a linear boundary formulation to support both known-class classification and unknown-sample rejection. On the ISIC 2019 dataset, the proposed framework achieves a Balanced Accuracy of 78.5% on known classes and improves the unknown-class F1-score to 51.3%. These results indicate that semantic–structural fusion enhances robustness under long-tailed and open-set conditions.