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Department of Biomedical Informatics and Data Science , Yale School of Medicine , New Haven , US, Affiliation:
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Fusing volumetric and surface brain MRI data in a self-supervised autoencoder uncovers substantially more heritable genetic loci than unimodal approaches.
Unlocking significant gains in binding affinity prediction by systematically curating assay metadata, revealing that protocol-level experimental context is a critical, underutilized signal.
AnaDiffusion allows for precise and controllable editing of 3D brain MRIs, achieving the lowest FID scores while preserving anatomical integrity.
Transforming autoregressive models into diffusion models can be achieved with up to 7,000x fewer training tokens, revolutionizing the efficiency of model training.
