All Competitors

Patient-level foundation model that pools every cell in an scRNA-seq sample into one disease representation, trained on 24.3 million cells.

Patient-level single-cell foundation model that condenses a donor's scRNA-seq profile into one 288-dimensional embedding for disease cohort search.

Imaging-genetics foundation model pairing SNP genotypes with brain-MRI phenotypes by contrastive learning to surface many-to-many associations.

Self-supervised vision transformer pretrained on chest X-rays to produce a domain-specific foundation model for classification and lung segmentation.

Domain-aware multi-task pretrained 3D Swin Transformer for T1-weighted brain MRI, transferring to Alzheimer's, Parkinson's and brain age tasks.

Multimodal ECG-language model aligning 12-lead waveforms with clinical report text for conversational cardiac diagnosis and report generation.

Open transformer foundation model for 12-lead electrocardiograms, pretrained on 1.5 million unlabeled ECGs with a wav2vec 2.0 self-supervised recipe.

Cell-free RNA language model for multi-cancer detection, classifying plasma samples straight from raw sequencing reads without gene annotation.