URL: Combating Label Noise for Lung Nodule Malignancy Grading
Xianze Ai Zehui Liao Yong Xia
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.