发表机构
Wilfrid Laurier University（威尔弗里德劳里埃大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对皮肤癌诊断的难点，提出集成MaxViT-Tiny与两类CNN模型的深度学习框架，结合MC Dropout估测不确定性、Grad-CAM++提供可解释性，在HAM10000数据集上取得优异分类性能，支撑可信计算机辅助诊断。
AI 中文摘要
由于类内差异大、类间相似性高、类别不平衡以及深度学习模型可解释性有限，基于皮肤镜图像的皮肤癌诊断仍具挑战性。本文提出一种用于多类皮肤病变分类的不确定性感知可解释深度学习框架。该框架通过深度集成学习，将视觉Transformer模型MaxViT-Tiny与基于CNN的模型ConvNeXt-Tiny、EfficientNetV2-B0相结合。采用蒙特卡洛（MC）Dropout估计预测不确定性，识别不可靠预测；同时使用可解释AI（XAI）技术Grad-CAM++，通过高亮影响模型决策的病变区域提供可视化解释。在HAM10000数据集上评估，在不确定性感知过滤（熵<1.0、置信度≥0.7）条件下，该框架达到96%的准确率和99%的ROC-AUC，宏平均精确率、召回率、F1值分别为94%、95%、95%，三项指标的加权平均得分均为96%。结果表明，该框架可实现准确、可解释且具备不确定性感知能力的皮肤病变分类，适用于可信的计算机辅助诊断。
英文摘要
Skin cancer diagnosis from dermoscopic images remains challenging due to high intra-class variability, inter-class similarity, class imbalance, and the limited interpretability of deep learning models. This paper proposes an uncertainty-aware and explainable deep learning framework for multi-class skin lesion classification. The framework combines a vision transformer model (MaxViT-Tiny) with CNN-based models (ConvNeXt-Tiny and EfficientNetV2-B0) through deep ensemble learning. Monte Carlo (MC) Dropout estimates predictive uncertainty and identifies unreliable predictions, while Grad-CAM++, an explainable AI (XAI) technique, provides visual explanations by highlighting lesion regions that influence model decisions. Evaluated on the HAM10000 dataset, the framework achieves 96% accuracy and 99% ROC-AUC under uncertainty-aware filtering (entropy < 1.0, confidence >= 0.7), with macro-average precision, recall, and F1-score of 94%, 95%, and 95%, respectively, and 96% weighted-average scores across all three metrics. The results demonstrate accurate, interpretable, and uncertainty-aware skin lesion classification for trustworthy computer-aided diagnosis.
Comments5 pages, 3 figures, IEEE AIBThings 2026
