融合多数据集并量化不确定性，提升皮肤癌检测准确率。
Melanoma Detection with Uncertainty Quantification
- 整合多个公开数据集训练分类器，并引入不确定性量化。
- 应用不确定性拒绝后准确率达97.8%，误诊率降低超40.5%。
- 适合医疗影像辅助诊断系统开发者与临床研究者使用。
早期发现黑色素瘤对提高生存率至关重要。现有检测工具虽采用数据驱动的机器学习方法，但往往未能充分整合多源数据。本文结合多个公开数据集以增强数据多样性，开展多种分类器的训练与评估实验，并通过不确定性量化校准模型，减少误诊。在基准数据集上的实验表明，应用不确定性拒绝前准确率为93.2%，之后提升至97.8%，误诊率下降超过40.5%。代码与数据均已公开，并提供基于网页的图像快速检测界面，供用户上传图片进行检测。
原文摘要 · Abstract (English)
Early detection of melanoma is crucial for improving survival rates. Current detection tools often utilize data-driven machine learning methods but often overlook the full integration of multiple datasets. We combine publicly available datasets to enhance data diversity, allowing numerous experiments to train and evaluate various classifiers. We then calibrate them to minimize misdiagnoses by incorporating uncertainty quantification. Our experiments on benchmark datasets show accuracies of up to 93.2% before and 97.8% after applying uncertainty-based rejection, leading to a reduction in misdiagnoses by over 40.5%. Our code and data are publicly available, and a web-based interface for quick melanoma detection of user-supplied images is also provided.
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