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Found 4 papers, 2 papers with code
Segment Anything Model for Medical Image Segmentation: Current Applications and Future Directions
Due to the inherent flexibility of prompting, foundation models have emerged as the predominant force in the fields of natural language processing and computer vision.
Towards Segment Anything Model (SAM) for Medical Image Segmentation: A Survey
Due to the flexibility of prompting, foundation models have become the dominant force in the domains of natural language processing and image generation.
Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation
Medical image segmentation is a fundamental and critical step in many image-guided clinical approaches.
Uncertainty-Guided Mutual Consistency Learning for Semi-Supervised Medical Image Segmentation
In this paper, we propose a novel uncertainty-guided mutual consistency learning framework to effectively exploit unlabeled data by integrating intra-task consistency learning from up-to-date predictions for self-ensembling and cross-task consistency learning from task-level regularization to exploit geometric shape information.
