Due to the complexity of annotation and inter-annotator variability, most lung nodule malignancy grading datasets contain label noise, which inevitably degrades the performance and generalizability of models. Although researchers adopt the label-noise-robust methods to handle label noise for lung nodule malignancy grading, they do not consider the inherent ordinal relation among classes of this task. To model the ordinal relation among classes to facilitate tackling label noise in this task, we propose a Unimodal-Regularized Label-noise-tolerant (URL) framework. Our URL contains two stages, the Supervised Contrastive Learning (SCL) stage and the Memory pseudo-labels generation and Unimodal regularization (MU) stage. In the SCL stage, we select reliable samples and adopt supervised contrastive learning to learn better representations. In the MU stage, we split samples with multiple annotations into multiple samples with a single annotation and shuffle them into different batches. To handle label noise, pseudo-labels are generated using the similarity between each sample and the central feature of each class, and temporal ensembling is used to obtain memory pseudo-labels that supervise the model training. To model the ordinal relation, we introduce unimodal regularization to keep the ordinal relation among classes in the predictions. Moreover, each lung nodule is characterized by three orthographic views. Experiments conducted on the LIDC-IDRI dataset indicate the superiority of our URL over other competing methods. Code is available at https://github.com/axz520/UR.

提出了Unimodal-Regularized Label-noise-tolerant (URL)框架，用于在肺结节恶性分级中处理标签噪声并建模类别之间的排序关系。通过使用受控对比学习和生成假标签的方法来训练模型，并引入了单模态正则化以保持预测中类别的排序关系。实验结果表明，该方法在LIDC-IDRI数据集上的表现优于其他竞争方法。

应对肺结节恶性程度分级的标签噪声问题