Dual-confidence-guided feature learning for semi-supervised medical image segmentation

Motivation: Obtaining a large medical image dataset with accurate annotations is challenging, thus limiting the practical application of deep learning in clinical practice. Goal(s): Developing a novel semi-supervised algorithm for a limited set of labeled images. Approach: Building a dual-branch network with dual-confidence-guided constraints for tumor feature learning, enabling the model to learn accurate and comprehensive feature representations. Results: In brain tumor segmentation, this algorithm achieved accurate tumor boundary segmentation using only 1% and 10% of labeled training data, and obtained segmentation results close to fully supervised learning when 20% of the training data was labeled. Impact: Our dual-confidence-guided semi-supervised feature learning model can achieve accurate brain tumor region segmentation with limited labeled training data, speeding up the application of deep learning technology in clinical research and providing assistance for clinical diagnosis.