Abstract
Accurate and reliable skin cancer diagnosis is critical for early treatment and improved patient outcomes. Deep learning (DL) models have shown promise in automating skin cancer classification, but their performance can be limited by data scarcity and a lack of uncertainty awareness. In this study, we present a comprehensive evaluation of DL-based skin lesion classification using transfer learning and uncertainty quantification (UQ) on the HAM10000 dataset. In the first phase, we benchmarked several pre-trained feature extractors-including Contrastive Language-Image Pretraining (CLIP) variants, Residual Network-50 (ResNet50), Densely Connected Convolutional Network (DenseNet121), Visual Geometry Group network (VGG16), and EfficientNet-V2-Large-combined with a range of traditional classifiers such as Support Vector Machine (SVM), eXtreme Gradient Boosting (XGBoost), and logistic regression. Our results show that CLIP-based vision transformers, particularly LAION CLIP ViT-H/14 with SVM, deliver the highest classification performance. In the second phase, we incorporated UQ using Monte Carlo Dropout (MCD), Ensemble, and Ensemble Monte Carlo Dropout (EMCD) to assess not only prediction accuracy but also the reliability of model outputs. We evaluated these models using uncertainty-aware metrics such as uncertainty accuracy(UAcc), uncertainty sensitivity(USen), uncertainty specificity(USpe), and uncertainty precision(UPre). The results demonstrate that ensemble methods offer a good trade-off between accuracy and uncertainty handling, while EMCD is more sensitive to uncertain predictions. This study highlights the importance of integrating UQ into DL-based medical diagnosis to enhance both performance and trustworthiness in real-world clinical applications.
Abstract (translated)
准确且可靠的皮肤癌诊断对于早期治疗和改善患者预后至关重要。深度学习（DL）模型在自动化皮肤癌分类方面显示出前景，但其性能可能因数据稀缺及不确定性意识不足而受限。本研究中，我们使用迁移学习和不确定性量化（UQ）对HAM10000数据集进行了基于深度学习的皮肤病变分类的全面评估。 第一阶段，我们基准测试了几种预训练特征提取器，包括对比语言图像预训练（CLIP）变体、残差网络-50 (ResNet50)、密集连接卷积网络(DenseNet121)、视觉几何组网络(VGG16)，以及EfficientNet-V2-Large，并将其与传统的分类器如支持向量机(SVM)、极端梯度提升(XGBoost)和逻辑回归相结合。结果显示，基于CLIP的视觉变换器（特别是LAION CLIP ViT-H/14 与 SVM结合）提供了最高的分类性能。 第二阶段，我们使用蒙特卡洛丢弃法(MCD)、集成学习以及集成蒙特卡洛丢弃法(EMCD)，将不确定性量化应用于模型中，以评估不仅预测准确性还包括模型输出的可靠性。我们使用了不确定度准确率(UAcc)、不确定度敏感性(USen)、不确定度特异性(USpe)和不确定度精确度(UPre)等基于不确定性的指标来评价这些模型。结果显示，集成方法在精度与处理不确定性之间提供了良好的权衡，而EMCD对于处理不确定预测更为灵敏。 本研究强调了将不确定性量化整合到基于深度学习的医学诊断中以提高实际临床应用中的性能和可信度的重要性。
URL
https://arxiv.org/abs/2506.10302
