A cost-effective approach to train a versatile medical image segmentation model using multi-source datasets with partial or sparse annotations, leveraging model self-disambiguation, prior knowledge incorporation, and imbalance mitigation strategies.
A novel weakly-supervised medical image segmentation framework, SimTxtSeg, that leverages simple text cues to generate high-quality pseudo-labels and integrates text and image features to enhance segmentation performance.