A unified autoregressive vision-language framework integrates segmentation, detection, and appearance reasoning for CT images via task-routing tokens and progressive refinement, with gains on public benchmarks.
Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge
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TCSA-UDA aligns image features to text-based class semantics and class prototypes to reduce cross-modality domain shift in unsupervised medical image segmentation.
citing papers explorer
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Segmentation, Detection and Explanation: A Unified Framework for CT Appearance Reasoning
A unified autoregressive vision-language framework integrates segmentation, detection, and appearance reasoning for CT images via task-routing tokens and progressive refinement, with gains on public benchmarks.
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TCSA-UDA: Text-Driven Cross-Semantic Alignment for Unsupervised Domain Adaptation in Medical Image Segmentation
TCSA-UDA aligns image features to text-based class semantics and class prototypes to reduce cross-modality domain shift in unsupervised medical image segmentation.