A Noise Robust Framework via Uncertainty Guidance for Medical Image Segmentation with Noisy Label

In medical image segmentation, acquiring sufficient accurate pixel-level annotations demands substantial manual labor and expertise, making it difficult to be satisfied and yielding noisy annotations. Visually distinguishable random label noises and boundary uncertainties are two primary aspects of annotation noises, but most existing methods fail to handle their co-occurrence issue. In this paper, we present a novel frame-work that primarily consists of an adaptive uncertainty-based label revision method and a contrastive learning approach to address the above challenge. The label revision method can correct random label noises by utilizing low uncertainty predictions, while enhancing the model’s tolerance to boundary uncertainties through the use of soft labels. The contrastive learning method can assist the model in reliably learning intrinsic relationships between pixels in the presence of noisy annotations. Experiments on two public datasets demonstrate the superiority of our method.