All Competitors

Unified science foundation model treating molecules, proteins, RNA, DNA, and materials as one sequence language, in 1B, 8B, and 46.7B sizes.

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

Functional MRI foundation model that learns brain dynamics as a stochastic optimal control problem, self-supervised on 41,072 UK Biobank subjects.

Multimodal medical imaging foundation model for zero-shot clinical diagnosis and report generation from chest X-ray and CT in English and Chinese.

4B-parameter generative genome foundation model trained on assembled environmental metagenomes for microbial representation and de novo DNA design.

Self-supervised 3D vision foundation model for non-contrast head CT, pretrained on 361,663 scans to detect a broad range of intracranial disease.

Photoplethysmography foundation model pretrained on raw wearable signals from a field study, transferring across lab and field health tasks.

Text-to-image diffusion model that generates synthetic medical images across imaging modalities and organs to augment scarce clinical training data.

Genomics foundation model for genome-scale SNP analysis, handling imputation, phasing, ancestry, and relatedness from one 0.8B-parameter checkpoint.

Segment Anything Model finetuned on diverse medical images, giving a reusable promptable checkpoint for interactive and automatic image segmentation.

Multimodal vision-text foundation model for brain CT and MRI, pretrained on roughly 10 million image-report pairs to act as a clinical copilot.

Vision-language foundation model for precision oncology, pretrained on 50M pathology images and 1B text tokens via unified masked modeling.

Multimodal 80B-parameter protein-language model that answers natural language questions about protein function from sequence and structure.

Text-guided protein design framework aligning language with sequences for text-conditioned generation, zero-shot editing, and property prediction.

Cryo-ET segmentation framework adapting SAM2 to vesicles and membrane-bound compartments in tomograms and 2D micrographs, zero-shot or fine-tuned.

Knowledge-enhanced ECG foundation model aligning a ResNet encoder with LLM-generated disease descriptions for zero- and few-shot interpretation.

Wearable accelerometry foundation model distilled from a PPG encoder, predicting cardiovascular and health biomarkers from motion signals alone.

Multimodal foundation model for wearable physiological sensing across PPG, ECG, EEG, GSR, and IMU signals, using channel-aware attention.

Multimodal foundation model integrating protein sequence, structure, and natural language to model and generate protein phenotypes across scales.

EEG foundation model for brain-computer interface decoding, factorizing self-attention into parallel spatial and temporal branches.

Vision transformers trained on Human Protein Atlas fluorescence microscopy for subcellular protein localization and cell morphology representation.

Generative model that emulates protein equilibrium ensembles, sampling cryptic pockets and unfolded states far faster than molecular dynamics.

Tissue-aware foundation model that restores brain MRI quality across motion correction, super-resolution, denoising, and harmonization.

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