Multimodal Skin Lesion Classification with Swin Transformer and Clinical Metadata Fusion

Published August 11, 2026 · Category: Robotics

Overview

arXiv:2608.07574v1 Announce Type: cross Abstract: Skin lesion classification plays an important role in supporting the early diagnosis of skin cancer. However, automated analysis remains challenging due to class imbalance, inter-class similarity, and intra-class variability in dermoscopic images. This paper proposes a multimodal classification framework that combines Swin Transformer-based image features with structured clinical metadata to improve diagnostic performance through integrated visual-context learning. Experiments on a publicly available dataset show that the proposed model achieves a test accuracy of 92.55% and a macro F1-score of 91.33%, with strong performance across minority classes. Temperature scaling is applied as a post-hoc calibration method, resulting in a reduction in expected calibration error and improving prediction reliability, while uncertainty estimation is incorporated to further assess the confidence of model predictions. Qualitative explainability analysis further shows that the model focuses on lesion regions during inference. Therefore, the results demonstrate that multimodal fusion, combined with calibration and interpretability analysis, provides an effective and trustworthy approach for automated skin lesion classification.

Source

Originally published at arxiv.org.

Related Articles

• PhysX-CoT: Structured Physical Reasoning from a Single Image to Simulation-Ready 3D Assets
• Anchor-Based AI Approach for Pre-Crash Object Detection Utilizing Micro-Doppler Signatures in Automotive Radar
• Removing Infrastructure Barriers in Human-Robot Collaboration Through Wireless Reconfigurable Cells