All Competitors

Open foundation model for photoplethysmography (PPG), learning morphology-aware waveform representations for cardiovascular and wearable health tasks.

Molecular foundation model that late-fuses graph, image, and SMILES encoders into one embedding for molecular property and drug target prediction.

Wearable sensor foundation model pretrained on heart rate, accelerometer, skin temperature and other channels for activity recognition and imputation.

CT segmentation foundation model that uses task prompts to segment 83 anatomical structures and lesions across whole-body scans in a single network.

Mamba-based mature RNA foundation model, contrastively trained on splice isoforms and 400+ mammalian species orthologs for mRNA property prediction.

Generative foundation model for cryo-EM density maps using flow matching, enabling zero-shot denoising, map sharpening, and missing wedge restoration.

Masked-autoencoder foundation model pretrained on digital-stethoscope heart sounds and single-lead ECG for cardiovascular disease detection.

Biomolecular structure prediction foundation model covering proteins, small molecules, DNA, RNA, and glycans in a single diffusion framework.

Knowledge-informed cross-species foundation model pre-trained on 101 million human and mouse single-cell transcriptomes to decipher gene regulation.

Convolutional ECG foundation model trained on expert annotations spanning 150 diagnostic categories, with 12-lead and single-lead wearable variants.

Joint-embedding predictive foundation model pretrained on over a million unlabeled ECGs, learning transferable 12-lead representations for diagnosis.

Transformer-based generative language model for de novo RNA design, pretrained on 16 million non-coding RNA sequences from RNAcentral.

Brain-dynamics foundation model for resting-state fMRI, adapting the Joint-Embedding Predictive Architecture with brain gradient positioning.

Text-guided MRI synthesis model that generates brain MR sequences and resolutions on demand from routine scans using imaging-metadata prompts.

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

Chest X-ray foundation model that pairs masked image modeling with image-report contrastive alignment for zero-shot diagnosis and phrase grounding.

EEG-to-language foundation model that pairs a Q-Conformer encoder with a frozen LLM to decode coherent sentences from non-invasive brain recordings.

End-to-end framework predicting protein structure and mutational fitness from a single sequence, with five-fold faster inference than ESMFold.

Multi-task EEG foundation model that treats brain signals as a foreign language, pairing a text-aligned neural tokenizer with a GPT-2 backbone.

Foundation model for photoplethysmography (PPG) that learns quality-robust waveform representations for heart rate, blood pressure, and AF detection.

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

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

Brain MRI foundation model family pretrained with anatomically informed contrastive learning for diagnosis and clinical score prediction.

Medical vision-language model trained on the MedTrinity-25M dataset, answering questions and generating text about radiology and histology images.