Abstract
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.
Abstract (translated)
conformal预测（CP）通过构建一个预定义概率的预测集来量化网络的不确定性。在本文中，我们解决了基于噪声标注的验证集的问题。我们引入了一个对标签噪声具有鲁棒性的 conformal分数。分数的噪声水平通过使用噪声标注数据和噪声水平进行估计。在测试阶段，无噪声分数被用作预测集。我们将所提出的算法应用于多个标准的医学成像分类数据集。我们证明了我们的方法在平均预测集大小方面优于现有方法，同时保持所需的覆盖范围。
URL
https://arxiv.org/abs/2405.02648
