Uncertainty quantification is a pivotal field that contributes to the realization of reliable and robust systems. By providing complementary information, it becomes instrumental in fortifying safe decisions, particularly within high-risk applications. Nevertheless, a comprehensive understanding of the advantages and limitations inherent in various methods within the medical imaging field necessitates further research coupled with in-depth analysis. In this paper, we explore Conformal Prediction, an emerging distribution-free uncertainty quantification technique, along with Monte Carlo Dropout and Evidential Deep Learning methods. Our comprehensive experiments provide a comparative performance analysis for skin lesion classification tasks across the three quantification methods. Furthermore, We present insights into the effectiveness of each method in handling Out-of-Distribution samples from domain-shifted datasets. Based on our experimental findings, our conclusion highlights the robustness and consistent performance of conformal prediction across diverse conditions. This positions it as the preferred choice for decision-making in safety-critical applications.

通过进行全面的实验和比较性能分析，我们研究了皮肤病变分类任务中三种不确定性量化方法：Conformal Prediction、Monte Carlo Dropout和Evidential Deep Learning，发现Conformal Prediction在各种条件下表现出的鲁棒性和一致性，使其成为安全关键应用中决策的首选。

可信皮肤病变分类的合规预测的实证验证