AI 中文总结
该研究针对皮肤病变分类的自动化挑战，提出结合Swin Transformer图像特征与临床元数据的多模态框架，在公开数据集上实现92.55%测试准确率，兼具强少数类性能与预测可信度。
AI 中文摘要
皮肤病变分类对支持皮肤癌早期诊断具有重要作用，但由于皮肤镜图像存在类别不平衡、类间相似性及类内变异性，自动化分析仍具挑战性。本文提出一种多模态分类框架，将基于Swin Transformer的图像特征与结构化临床元数据相结合，通过整合视觉-上下文学习提升诊断性能。在公开数据集上的实验表明，所提模型测试准确率达92.55%，宏F1值为91.33%，且在少数类别中表现强劲。采用温度缩放作为事后校准方法，可降低预期校准误差、提升预测可靠性，同时引入不确定性估计进一步评估模型预测的置信度。定性可解释性分析进一步显示，模型在推理过程中会聚焦于病变区域。因此，结果表明多模态融合结合校准与可解释性分析，为自动化皮肤病变分类提供了有效且可信的方法。
英文摘要
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.
