CheXanatomy trains VLMs to generate 2D anatomical masks via next-token prediction on synthetic CXRs from CT, matching U-Net performance with better domain-shift robustness and sample efficiency.
arxiv 2024
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CLIP-Guided SAM injects CLIP-derived features into SAM via lightweight adapters for semantic conditioning, supporting text and spatial prompts while remaining parameter-efficient and achieving competitive performance.
citing papers explorer
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CheXanatomy: Anatomy-Aware Vision-Language Modeling for Chest Radiographs
CheXanatomy trains VLMs to generate 2D anatomical masks via next-token prediction on synthetic CXRs from CT, matching U-Net performance with better domain-shift robustness and sample efficiency.
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CLIP-Guided SAM: Parameter-Efficient Semantic Conditioning for Promptable Segmentation
CLIP-Guided SAM injects CLIP-derived features into SAM via lightweight adapters for semantic conditioning, supporting text and spatial prompts while remaining parameter-efficient and achieving competitive performance.