Since radiologists have different training and clinical experiences, they may
provide various segmentation annotations for a lung nodule. Conventional
studies choose a single annotation as the learning target by default, but they
waste valuable information of consensus or disagreements ingrained in the
multiple annotations. This paper proposes an Uncertainty-Guided Segmentation
Network (UGS-Net), which learns the rich visual features from the regions that
may cause segmentation uncertainty and contributes to a better segmentation
result. With an Uncertainty-Aware Module, this network can provide a
Multi-Confidence Mask (MCM), pointing out regions with different segmentation
uncertainty levels. Moreover, this paper introduces a Feature-Aware Attention
Module to enhance the learning of the nodule boundary and density differences.
Experimental results show that our method can predict the nodule regions with
different uncertainty levels and achieve superior performance in LIDC-IDRI
dataset.Comment: 10 pages, 4 figures, 30 reference