The success of Deep Neural Network (DNN) models significantly depends on the quality of provided annotations. In medical image segmentation, for example, having multiple expert annotations for each data point is common to minimize subjective annotation bias. Then, the goal of estimation is to filter out the label noise and recover the ground-truth masks, which are not explicitly given. This paper proposes a probabilistic model for noisy observations that allows us to build a confident classification and segmentation models. To accomplish it, we explicitly model label noise and introduce a new information-based regularization that pushes the network to recover the ground-truth labels. In addition, for segmentation task we adjust the loss function by prioritizing learning in high-confidence regions where all the annotators agree on labeling. We evaluate the proposed method on a series of classification tasks such as noisy versions of MNIST, CIFAR-10, Fashion-MNIST datasets as well as CIFAR-10N, which is real-world dataset with noisy human annotations. Additionally, for segmentation task, we consider several medical imaging datasets, such as, LIDC and RIGA that reflect real-world inter-variability among multiple annotators. Our experiments show that our algorithm outperforms state-of-the-art solutions for the considered classification and segmentation problems.

翻译：深度神经网络(DNN)模型的有效性高度依赖于标注数据的质量。以医学图像分割为例，为减少主观标注偏差，通常会对每个数据点提供多个专家标注。此时，估计的目标是滤除标签噪声并恢复未显式给出的真实掩码。本文提出了一种针对噪声观测的概率模型，使我们能够构建高置信度的分类与分割模型。为实现这一目标，我们显式建模了标签噪声，并引入一种基于信息的新型正则化项，推动网络恢复真实标签。此外，针对分割任务，我们通过优先学习所有标注者一致标注的高置信度区域来调整损失函数。我们在分类任务（含噪声版本的MNIST、CIFAR-10、Fashion-MNIST数据集及包含真实人类噪声标注的CIFAR-10N数据集）上评估了所提方法。同时，针对分割任务，我们采用多个反映真实世界多标注者间变异性的医学影像数据集（如LIDC和RIGA）。实验表明，在所述分类与分割问题上，本算法性能优于现有最优方案。