Supplementary material for: An Explainable Deep Learning Model Classifies Eight Categories of Pigmented Skin Lesions on Clinical Photographs: A Multicenter Retrospective Internal Validation Study in Japan

This dataset contains the Supplementary Materials for the manuscript "An Explainable Deep Learning Model Classifies Eight Categories of Pigmented Skin Lesions on Clinical Photographs: A Multicenter Retrospective Internal Validation Study in Japan," accepted for publication in JAAD International. The Supplementary Materials provide extended methodological details and additional results supporting the findings reported in the main manuscript, including: - Supplementary Methods: ensemble architecture and prediction aggregation (20 models comprising EfficientNet-B4, EfficientNet-B7, Vision Transformer, and Swin Transformer across five cross-validation folds), dataset and reference standard, patient-level data splitting, and model interpretability analysis using Gradient-weighted Class Activation Mapping (Grad-CAM). - Supplementary Table 1: Accuracy of individual architectures and the full ensemble on the independent test set. - Supplementary Figure 1: (a) Receiver operating characteristic curves and (b) precision-recall curves for each pigmented lesion category. - Supplementary Table 2: Detailed classification performance metrics for each pigmented lesion category on the independent test set. - Supplementary Table 3: Quantitative Grad-CAM-based attention metrics across lesion categories. - Supplementary Figure 2: Representative Grad-CAM visualizations demonstrating lesion-centered, disease-specific activation patterns. - Supplementary Figure 3: Representative misclassified cases between seborrheic keratosis and basal cell carcinoma. Note: This dataset contains supplementary documentation only. The underlying clinical image dataset is not publicly available due to institutional and ethical restrictions and is available from the corresponding author upon reasonable request.本数据集为已被《JAAD International》接收发表的手稿《可解释深度学习模型对临床照片中的8类色素性皮肤病变进行分类：日本一项多中心回顾性内部验证研究》的补充材料。 本补充材料提供了支撑主手稿中研究结果的拓展方法学细节与额外结果，具体包括： - 补充方法：集成架构与预测聚合方案（基于5折交叉验证的20个模型，涵盖EfficientNet-B4、EfficientNet-B7、Vision Transformer及Swin Transformer）、数据集与参考标准、患者层级的数据拆分，以及基于梯度加权类激活映射（Gradient-weighted Class Activation Mapping）的模型可解释性分析。 - 补充表1：独立测试集上单模型架构与完整集成模型的分类准确率。 - 补充图1：(a) 各类色素性皮肤病变的受试者工作特征曲线，(b) 各类色素性皮肤病变的精确率-召回率曲线。 - 补充表2：独立测试集上各类色素性皮肤病变的详细分类性能指标。 - 补充表3：基于梯度加权类激活映射的各类色素性皮肤病变的量化注意力指标。 - 补充图2：展示以病变为中心、疾病特异性激活模式的典型梯度加权类激活映射可视化结果。 - 补充图3：脂溢性角化病与基底细胞癌之间的典型误分类病例。 注：本数据集仅包含补充文档。其依托的临床图像数据集因机构与伦理限制无法公开获取，仅可在提出合理请求后向通讯作者申请获取。