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MIT Critical Data, Massachusetts Institute of Technology, Cambridge, MA, USA, School of Medicine, Johns Hopkins University, Baltimore, MD, USA, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA, AI for Responsible, Generalizable, and Open Surgical (ARGOS) Research Group, Baltimore, MD, USA
MIT CSAIL2
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Quantum kernels unlock signal in medical image embeddings where classical methods fail, suggesting a new path for extracting value from medical foundation models.
You can boost medical image super-resolution fidelity by over 3dB just by swapping in a domain-specific VAE, no fancy diffusion architecture needed.
