ADSA-Net: addressing intra- and inter-class variabilities for severity assessment of atopic dermatitis

Atopic dermatitis (AD) is a chronic inflammatory skin disorder characterized by recurrent itching, erythema, dryness, and eczematous lesions. Automated AD severity assessment is crucial for cost-effective and precision clinical decision-making but remains challenging. This is due to the subtle contrast variations between key dermatological signs and significant variations in lesion sizes across patients and disease stages. To address these issues, we propose ADSA-Net, which is designed to handle both intra- and inter-class variabilities. ADSA-Net first extracts multi-scale texture-aware features to effectively model variations in lesion size and texture. It then leverages contrastive learning to enhance intra- and inter-class differentiation, strengthening model's discriminatory ability for samples that are difficult to distinguish. Finally, ADSA-Net refines the learning process by leveraging a dynamic feature pool of correctly classified samples to guide the calibration of misclassified instances, enhancing overall accuracy. We further establish a dataset for AD severity assessment. Comprehensive experiments on this dataset show that ADSA-Net significantly outperforms existing state-of-the-art methods.