HRS-Swin: A Hierarchical Representation Separation Swin Transformer for Automated Neonatal Auricular Deformity Classification

Abstract

The incidence of auricular deformities in newborns is notably high, and even experienced clinicians may encounter issues such as misdiagnosis and missed diagnosis due to subjective judgment. Although several studies have explored the use of deep learning methods for auxiliary diagnosis, the highly complex and individualized characteristics of auricular morphology pose significant challenges to existing approaches in achieving automated identification and fine-grained subtype classification. To address this issue, we propose HRS-Swin, a progressive representation reconstruction framework built upon a Swin Transformer backbone. The model integrates a Class Token Fusion module to enhance global semantic representation, a Stable Semantic Enhancement and Residual Compression mechanism for compact and discriminative embedding learning, and a Dynamic Margin Enhancer to enlarge inter-class separability in the embedding space. Experiments on the BabyEar4K dataset (1,926 newborns) demonstrate that HRS-Swin outperforms representative CNN and Transformer baselines. The proposed method achieves an accuracy of 0.8009 and a macro F1-score of 0.7024, showing consistent improvements over standard Swin Transformer. These results indicate that the proposed framework provides a robust and effective solution for automated auricular deformity classification and early clinical assistance.