平均校准误差：一种用于提升图像分割可靠性的可微损失函数
Average Calibration Error: A Differentiable Loss for Improved Reliability in Image Segmentation
AI总结：
本文提出边缘L1平均校准误差（mL1-ACE）作为辅助损失函数，通过直接可微的硬分箱改善医学图像分割的逐像素校准，在保持分割质量的同时显著降低校准误差。
摘要：
用于医学图像分割的深度神经网络通常会产生与经验观察不符的过度自信结果。这种校准不当挑战了其临床转化。我们提出使用边缘L1平均校准误差（mL1-ACE）作为一种新颖的辅助损失函数，以在不影响分割质量的情况下改善逐像素校准。我们表明，尽管该损失函数使用硬分箱，但它是直接可微的，无需使用近似但可微的替代方法或软分箱方法。我们的工作还引入了数据集可靠性直方图的概念，它推广了标准可靠性图，用于在数据集级别聚合的语义分割校准进行精细的视觉评估。使用mL1-ACE，我们将平均和最大校准误差分别降低了45%和55%，同时在BraTS 2021数据集上保持了87%的Dice分数。我们在此分享代码：https://github.com/cai4cai/ACE-DLIRIS
英文摘要：
Deep neural networks for medical image segmentation often produce overconfident results misaligned with empirical observations. Such miscalibration, challenges their clinical translation. We propose to use marginal L1 average calibration error (mL1-ACE) as a novel auxiliary loss function to improve pixel-wise calibration without compromising segmentation quality. We show that this loss, despite using hard binning, is directly differentiable, bypassing the need for approximate but differentiable surrogate or soft binning approaches. Our work also introduces the concept of dataset reliability histograms which generalises standard reliability diagrams for refined visual assessment of calibration in semantic segmentation aggregated at the dataset level. Using mL1-ACE, we reduce average and maximum calibration error by 45% and 55% respectively, maintaining a Dice score of 87% on the BraTS 2021 dataset. We share our code here: https://github.com/cai4cai/ACE-DLIRIS
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
