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洞見 - Weakly-supervised medical image segmentation
Versatile Medical Image Segmentation Model Trained on Diverse Partially and Sparsely Labeled Datasets
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.
SimTxtSeg: Weakly-Supervised Medical Image Segmentation Using Simple Text Cues
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.
