Mayo Clinic

Biomolecular sequence-structure co-design that plans over frozen folding and inverse-folding models with Monte Carlo tree search, training nothing.

Open-weights pathology foundation models pairing a 1.1B-parameter ViT-g/8 tile encoder with distilled 86M and 22M variants for H&E histology.

Vision-language encoders for chest CT that align 3D volumes with radiology reports using contrastive, report-generation, and masked-image objectives.

Whole-slide pathology embedding framework that ranks tiles, keeps only the 25 most informative, and encodes a slide in 2.27 seconds.

Open-source, lightweight generalist vision-language foundation model for diverse biomedical imaging and text tasks.

Self-supervised 3D pretrained models for CT and MRI that learn anatomical representations from unlabeled volumes and transfer to segmentation tasks.