论文标题
功能空间变化推断，用于计算机辅助诊断中的不确定性估计
Functional Space Variational Inference for Uncertainty Estimation in Computer Aided Diagnosis
论文作者
论文摘要
深度神经网络已彻底改变了医学图像分析和疾病诊断。尽管其性能令人印象深刻，但很难为此类网络生成良好的概率输出，这使它们无法解释。贝叶斯神经网络为建模不确定性和增加患者的安全提供了一种原则的方法，但是它们具有较大的计算开销，并提供了有限的校准改进。在这项工作中，通过将皮肤病变分类作为一个示例任务，我们表明，通过将贝叶斯推断转移到功能空间，我们可以制作有意义的先验，从而以较低的计算成本提供更好的校准不确定性估计。
Deep neural networks have revolutionized medical image analysis and disease diagnosis. Despite their impressive performance, it is difficult to generate well-calibrated probabilistic outputs for such networks, which makes them uninterpretable black boxes. Bayesian neural networks provide a principled approach for modelling uncertainty and increasing patient safety, but they have a large computational overhead and provide limited improvement in calibration. In this work, by taking skin lesion classification as an example task, we show that by shifting Bayesian inference to the functional space we can craft meaningful priors that give better calibrated uncertainty estimates at a much lower computational cost.
