All Competitors

Hypergraph foundation model for brain disease diagnosis from resting-state fMRI, self-supervised on high-order connectivity among brain regions.

Multimodal LLM unifying 12-lead ECG time series, ECG images, and text for grounded, clinician-aligned electrocardiogram interpretation.

Brain-to-text decoder that reconstructs typed sentences from non-invasive MEG and EEG brain recordings using a CNN, transformer, and language model.

ECG foundation model that treats heartbeats as words and rhythm strips as sentences, using heartbeat-level tokenization for diagnostic classification.

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

Multimodal ECG language model pairing a specialized signal encoder with a biomedical LLM for cardiovascular disease detection and question answering.

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

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.

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

Motion foundation model for wearable accelerometry, trained with relative contrastive learning on 1B segments from 87,376 participants.

ECG foundation model that learns discrete rhythm tokens from noisy real-world recordings for arrhythmia classification and anomaly detection.

Audio-language foundation model for bioacoustics that answers natural-language questions about animal sounds, with zero-shot species classification.

Language model over whole-night sleep stage sequences that corrects automated sleep staging and supplies features for sleep disorder diagnosis.

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

Multimodal large language model that interprets 12-lead electrocardiogram images, answering open-ended clinical questions and generating ECG reports.

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

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

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

ECG foundation model pretrained on 12-lead waveforms paired with clinical reports, enabling label-efficient and zero-shot cardiac diagnosis.

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

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

Multimodal contrastive model aligning clinical EEG with free-text reports, enabling zero-shot EEG classification from natural-language prompts.