Brigham and Women's Hospital

Transformer foundation model pretrained on a biomedical knowledge graph for zero-shot drug repurposing, target, and adverse-effect prediction.

Attention-based multiple instance learning heads for whole-slide pathology, pretrained on a 108-way pan-cancer slide classification task.

Spatial proteomics foundation model, marker-aware and panel-agnostic, pretrained on 47 million multiplexed tissue-imaging patches from 175 markers.

Distribution-level representation learning that embeds whole cell populations, perturbation responses, and sequence sets, not individual data points.

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

3D vision foundation model for computed tomography, contrastively pretrained on 148,000 scans for segmentation, triage, retrieval, and concept search.

Self-supervised vision foundation model for structural brain MRI, providing a reusable encoder for brain age, survival, and image classification.

Histopathology patch encoder turning 512x512 tiles into 768-dimensional features, trained with a CoCa objective on 1.26 million captioned images.

Slide-level pathology foundation model turning whole-slide images into reusable embeddings for classification, retrieval, and report generation.

DNA methylation foundation model over 49,156 array CpG sites. Imputes missing values, embeds samples, and predicts epigenetic age and disease risk.

Joint embedding space for common SNPs and free-text clinical concepts, aligned by contrastive learning over GWAS, biobank and knowledge-graph pairs.

Weakly supervised histopathology foundation model pretrained on 60,530 whole-slide images for cancer detection, prognosis, and molecular prediction.

Slide-level pathology foundation model that learns whole-slide embeddings by aligning multiple stains of the same tissue during pretraining.

Multi-omics transformer generating transcriptomic, methylation and proteomic signatures for a given tissue, disease, age group, sex and compound.

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

Protein language model that predicts which of twelve subcellular compartments and biomolecular condensates a human protein partitions into.

Histopathology vision-language foundation model pretrained on 1.17 million image-caption pairs with contrastive and captioning objectives.

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

Gut microbiome language model that reads a 16S sample as a sentence of taxa, producing context-sensitive embeddings that transfer across cohorts.