The success of current deep learning models depends on a large number of precise labels. However, in the field of medical image segmentation, acquiring precise labels is labor-intensive and time-consuming. Hence, the challenge of achieving a high-performance model via datasets containing noisy labels has attracted significant research interest. Some existing methods are unable to exclude samples containing noisy labels and some methods still have high requirements on datasets. To solve this problem, we propose a noisy label learning method for medical image segmentation using a mixture of high and low quality labels based on the architecture of mean teacher. Firstly, considering the teacher model’s capacity to aggregate all previously learned information following each training step, we propose to leverage a teacher model to correct noisy label adaptively during the training phase. Secondly, to enhance the model’s robustness, we propose to infuse feature perturbations into the student model. This strategy aims to bolster the model’s ability to handle variations in input data and improve its resilience to noisy labels. Finally, we simulate noisy labels by destroying labels in two medical image datasets: the Automated Cardiac Diagnosis Challenge (ACDC) dataset and the 3D Left Atrium (LA) dataset. Experiments show that the proposed method demonstrates considerable effectiveness. With a noisy ratio of 0.8, compared with other methods, the mean Dice score of our proposed method is improved by 2.58% and 0.31% on ACDC and LA datasets, respectively.

当前深度学习模型的成功取决于大量的精确标签。然而，在医学图像分割领域，获取精确标签既费力又耗时。因此，通过包含噪声标签的数据集实现高性能模型的挑战引起了人们的广泛研究兴趣。一些现有方法无法排除包含噪声标签的样本，并且一些方法对数据集仍然有很高的要求。为了解决这个问题，我们提出了一种基于均值教师架构的混合高质量和低质量标签的医学图像分割噪声标签学习方法。首先，考虑到教师模型在每个训练步骤之后聚合所有先前学习的信息的能力，我们建议利用教师模型在训练阶段自适应地纠正噪声标签。其次，为了增强模型的鲁棒性，我们建议将特征扰动注入学生模型中。该策略旨在增强模型处理输入数据变化的能力，并提高其对噪声标签的适应能力。最后，我们通过破坏两个医学图像数据集中的标签来模拟噪声标签：自动心脏诊断挑战 (ACDC) 数据集和 3D 左心房 (LA) 数据集。实验表明，所提出的方法具有相当的有效性。在噪声比为 0.8 的情况下，与其他方法相比，我们提出的方法在 ACDC 和 LA 数据集上的平均 Dice 分数分别提高了 2.58% 和 0.31%。