Massachusetts General Hospital

Pan-cancer clinico-genomic model for treatment response and survival prediction, transferring zero-shot to unseen hospitals and cancer types.

Medical imaging foundation model unifying pathology and radiology, serving classification and segmentation on 2D, 3D and gigapixel inputs.

Intracortical speech brain-to-text decoder jointly pretrained across six BCI users, with over 50% lower relative word error than per-user models.

Prime editing efficiency prediction from pegRNA sequence, with every biochemical step of the editing mechanism modeled as its own learned rate.

Gut microbiome foundation model pretrained on human shotgun metagenomes, learning species-level taxonomic representations for disease prediction.

Modality-agnostic foundation model for human brain imaging that runs five core neuroimaging tasks across uncalibrated CT and MRI without retraining.

Generalist MRI vision-language foundation model that handles reconstruction, segmentation, abnormality detection, and report generation in one model.

Spatiotemporal foundation model that learns representations directly from 4D functional MRI volumes for disease diagnosis and phenotype prediction.

Surgical video foundation model pretrained by entropy-maximizing compression on 0.78M unlabeled frames from 2,122 minimally invasive procedures.

2D/3D X-ray to volume registration for surgical navigation, aligning live fluoroscopy to preoperative CT or MRI after a five-minute fine-tune.

PET/CT foundation model pretrained by cross-modal masked autoencoding on whole-body scans, for tumor lesion segmentation and lymphoma staging.

Genomic language model continue-pretrained on 13 million UK Biobank variants, giving variant-aware DNA embeddings for gene function and expression.

865M-parameter multimodal foundation model that fuses 3D low-dose chest CT with clinical data to answer 17 lung cancer screening questions.

Slide-level pathology foundation model that encodes a whole-slide image of any size into one embedding, supervised by paired sequencing data.

CRISPR-Cas PAM specificity prediction directly from Cas protein sequence, plus computational evolution of Cas9 variants toward a chosen PAM.

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

Interactive foundation model for biomedical image segmentation, prompted with scribbles, clicks, and bounding boxes to segment unseen structures.

Multimodal vision-language copilot for pathology that answers open-ended questions about histology images and reasons about differential diagnoses.

Keypoint-based foundation model for brain MRI registration, pretrained on over 100,000 3D volumes for rigid, affine, and deformable alignment.

Bulk tumor transcriptome model ensembling hundreds of variational autoencoders into interpretable cancer-specific latent spaces for 18 cancers.

Self-supervised 3D CT foundation model that extracts general-purpose tumor representations for cancer imaging biomarker discovery and prognosis.

Medical image segmentation model that solves unseen segmentation tasks in context from a few labeled examples, with no retraining or fine-tuning.