MediSyn is a generalist latent diffusion model that synthesizes text-guided medical images across multiple specialties and modalities from public data and improves downstream classifiers in low-data settings.
Augmenting Medical Image Classifiers with Synthetic Data from Latent Diffusion Models,
2 Pith papers cite this work, alongside 12 external citations. Polarity classification is still indexing.
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UNVERDICTED 2representative citing papers
Extending Med-DDPM to AD, synthetic MRIs conditioned on anatomical masks produce segmentation models with Dice 0.6532 (synthetic-only) and 0.7244 (hybrid real+synthetic), outperforming real-only training at 0.6513.
citing papers explorer
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A Generalist Model for Diverse Text-Guided Medical Image Synthesis
MediSyn is a generalist latent diffusion model that synthesizes text-guided medical images across multiple specialties and modalities from public data and improves downstream classifiers in low-data settings.
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Structural MRI Synthesis for Alzheimer's Disease via Conditional Diffusion on Anatomical Masks
Extending Med-DDPM to AD, synthetic MRIs conditioned on anatomical masks produce segmentation models with Dice 0.6532 (synthetic-only) and 0.7244 (hybrid real+synthetic), outperforming real-only training at 0.6513.