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Diffusion models can outperform autoregressive counterparts in medical report drafting while offering a unique any-order infill capability that enhances usability for clinicians.
Transition-aware sampling can dramatically enhance the relevance of chest X-ray reports by leveraging longitudinal patient data, yielding superior results in clinical settings.
Achieving a dual-purpose tokenizer that excels in both clinical task performance and controllable 3D brain MRI generation could revolutionize how we approach medical imaging.
OpenMedQ outperforms even the largest models in medical vision-language tasks, achieving state-of-the-art results with a fraction of the parameters.
Set-distance rewards can boost chest X-ray report generation performance by over 6% while slashing token generation by more than half.
