Conformal Prediction (CP) quantifies network uncertainty by building a small prediction set with a pre-defined probability that the correct class is within this set. In this study we tackle the problem of CP calibration based on a validation set with noisy labels. We introduce a conformal score that is robust to label noise. The noise-free conformal score is estimated using the noisy labeled data and the noise level. In the test phase the noise-free score is used to form the prediction set. We applied the proposed algorithm to several standard medical imaging classification datasets. We show that our method outperforms current methods by a large margin, in terms of the average size of the prediction set, while maintaining the required coverage.

翻译：保形预测（CP）通过构建一个包含正确类别的预定义概率的小型预测集来量化网络的不确定性。本研究基于含噪声标签的验证集解决了CP校准问题。我们提出了一种对标签噪声具有鲁棒性的保形分数。该无噪声保形分数通过含噪声标签数据和噪声水平进行估计。在测试阶段，使用无噪声分数构成预测集。我们将所提算法应用于多个标准医学影像分类数据集。结果表明，在保持所需覆盖率的同时，本方法在预测集平均规模上显著优于现有方法。