论文标题
关于图像分割的主动学习的不确定性估计
On uncertainty estimation in active learning for image segmentation
论文作者
论文摘要
不确定性估计对于在许多应用中解释机器学习模型的可信度很重要。这对于数据驱动的主动学习设置尤其重要，在该设置中，目标是通过最低标签工作实现一定的准确性。在这种情况下，该模型学会根据其估计的不确定性选择最有用的未标记样本进行注释。高度不确定的预测被认为是改善模型性能的信息更多。在本文中，我们探讨了用于医学图像分割的主动学习框架内的不确定性校准，该区域通常很少。研究了各种不确定性估计方法和获取策略（区域和完整图像）。我们观察到选择区域以注释而不是完整的图像会导致更精心校准的模型。此外，我们通过实验表明，注释区域可以减少50％需要被人类标记的像素与完整图像相比。
Uncertainty estimation is important for interpreting the trustworthiness of machine learning models in many applications. This is especially critical in the data-driven active learning setting where the goal is to achieve a certain accuracy with minimum labeling effort. In such settings, the model learns to select the most informative unlabeled samples for annotation based on its estimated uncertainty. The highly uncertain predictions are assumed to be more informative for improving model performance. In this paper, we explore uncertainty calibration within an active learning framework for medical image segmentation, an area where labels often are scarce. Various uncertainty estimation methods and acquisition strategies (regions and full images) are investigated. We observe that selecting regions to annotate instead of full images leads to more well-calibrated models. Additionally, we experimentally show that annotating regions can cut 50% of pixels that need to be labeled by humans compared to annotating full images.
