
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.


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versatile-medical-image-segmentation-model-trained-on-diverse-partially-and-sparsely-labeled-datasets


Versatile Medical Image Segmentation Model Trained on Diverse Partially and Sparsely Labeled Datasets


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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.


simtxtseg-weakly-supervised-medical-image-segmentation-using-simple-text-cues


SimTxtSeg: Weakly-Supervised Medical Image Segmentation Using Simple Text Cues
