论文标题
诊断不确定性校准：迈向医疗领域的可靠机器预测
Diagnostic Uncertainty Calibration: Towards Reliable Machine Predictions in Medical Domain
论文作者
论文摘要
我们在存在标签不确定性的情况下提出了一个类概率估计值（CPE）的评估框架，通常将其视为医疗领域专家之间的诊断分歧。我们还对高阶统计数据（包括评估者分歧）的评估指标进行了正式的评估指标，以评估标签不确定性的预测。此外，我们提出了一种新型的事后方法，称为$ alpha $ actabibration，该方法使神经网络分类器在CPE上具有校准的分布。使用合成实验和大规模的医学成像应用，我们表明我们的方法显着提高了不确定性估计的可靠性：分歧概率和后CPE。
We propose an evaluation framework for class probability estimates (CPEs) in the presence of label uncertainty, which is commonly observed as diagnosis disagreement between experts in the medical domain. We also formalize evaluation metrics for higher-order statistics, including inter-rater disagreement, to assess predictions on label uncertainty. Moreover, we propose a novel post-hoc method called $alpha$-calibration, that equips neural network classifiers with calibrated distributions over CPEs. Using synthetic experiments and a large-scale medical imaging application, we show that our approach significantly enhances the reliability of uncertainty estimates: disagreement probabilities and posterior CPEs.
