All Competitors

EEG foundation model that learns transferable brain-signal representations with a vector-quantized tokenizer and masked transformer pretraining.

Multi-modal foundation model for sleep analysis, learning joint representations across brain, cardiac, and respiratory polysomnography signals.

ECG foundation model that learns 12-lead waveform representations by contrastively aligning each recording with machine-generated cardiological text.

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

Whole-slide histopathology foundation model pretrained on 1.3 billion image tiles from 171,189 clinical slides spanning 31 tissue types.

Vision-language foundation model pre-trained on screening mammogram-report pairs to improve data efficiency and robustness in breast cancer detection.

Ophthalmic imaging foundation model pretrained on 2.78M images across 11 modalities for diagnosis, prognosis, and visual question answering.

Universal brain lesion segmentation for multi-modal brain MRI, using a Mixture of Modality Experts to span diverse modalities and lesion types.

Efficient protein language model library from Prescient Design enabling high-quality sequence representations and fitness prediction in 24 GPU hours.

Mixture-of-Experts foundation model for medical image segmentation that generalizes across imaging modalities and clinical centers.

Trainable, open-source reimplementation of AlphaFold2 for protein structure prediction that matches its accuracy and runs 3-5x faster.

Deep learning framework predicting equilibrium distributions of molecular systems, enabling efficient ensemble generation and conformation sampling.

Compact 22M-parameter ViT-S pathology foundation model pre-trained on 195M tiles, spanning subcellular segmentation to slide-level prediction.

CLIP-style vision-language model for echocardiography, pretrained on echo videos and cardiologist reports for zero-shot cardiac interpretation.

Single-cell foundation model that treats cells as tokens and tissues as sentences, encoding cell-cell relationships across 85M parameters.

Family of medical multimodal models built on Gemini, adding uncertainty-guided web search, custom modality encoders, and long-context EHR reasoning.

Generalist medical vision-language foundation model with 40B parameters, spanning radiology, pathology, dermatology, retinography, and endoscopy.

AI-designed CRISPR-Cas9 gene editor generated by protein language models trained on 1.2 million CRISPR operons and shown to edit the human genome.

Transformer foundation model pretrained on 110M single-cell and spatial transcriptomics profiles, transferring spatial context to dissociated cells.

Brain MRI foundation model pretrained with masked image modeling on roughly 57,000 multi-contrast head scans for brain tumor diagnosis.

Swin transformer foundation model for fluorescence microscopy image restoration, unifying denoising, super-resolution, and volumetric reconstruction.

Genomic language model trained on metagenomic scaffolds that learns protein co-regulation and function by modeling gene context and operon structure.

Transformer language model for 5' UTR sequences that predicts mRNA translation efficiency, ribosome loading, and protein expression levels.

Transformer encoder self-supervised on 100,000 hours of dual-accelerometer data, used as a frozen feature extractor for human activity recognition.