CUAMT: A MRI semi-supervised medical image segmentation framework based on contextual information and mixed uncertainty.
Journal:
Computer methods and programs in biomedicine
Published Date:
Apr 27, 2025
Abstract
BACKGROUND AND OBJECTIVE: Semi-supervised medical image segmentation is a class of machine learning paradigms for segmentation model training and inference using both labeled and unlabeled medical images, which can effectively reduce the data labeling workload. However, existing consistency semi-supervised segmentation models mainly focus on investigating more complex consistency strategies and lack efficient utilization of volumetric contextual information, which leads to vague or uncertain understanding of the boundary between the object and the background by the model, resulting in ambiguous or even erroneous boundary segmentation results.
