PEIL learns unobservable parameters by embedding them in a physics-based reconstruction loop, outperforming supervised baselines with ground-truth access while enabling zero-shot generalization and major data reduction in wireless and MRI tasks.
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PASS combines a vision-language model with physics-based deep unrolling to create personalized, anomaly-aware fast MRI that improves image quality and downstream diagnostic tasks.
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Discovery of unobservable parameters via physical embedding
PEIL learns unobservable parameters by embedding them in a physics-based reconstruction loop, outperforming supervised baselines with ground-truth access while enabling zero-shot generalization and major data reduction in wireless and MRI tasks.
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Vision-Language Model-Guided Deep Unrolling Enables Personalized, Fast MRI
PASS combines a vision-language model with physics-based deep unrolling to create personalized, anomaly-aware fast MRI that improves image quality and downstream diagnostic tasks.