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Cross-Modal Conditioned Reconstruction for Language-guided Medical Image Segmentation
Recent developments underscore the potential of textual information in enhancing learning models for a deeper understanding of medical visual semantics.
SegICL: A Universal In-context Learning Framework for Enhanced Segmentation in Medical Imaging
Extensive experimental validation of SegICL demonstrates a positive correlation between the number of prompt samples and segmentation performance on OOD modalities and tasks.
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