All Competitors

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.

EEG foundation model for Alzheimer's disease detection, pretrained by contrastive learning across 13 clinical EEG datasets and 2,238 subjects.

Nanopore basecaller built on a Squeezeformer encoder, turning raw ion-current signal into DNA at 93.97% average read identity across 11 datasets.

Compact EEG foundation model whose alternating attention separates within-channel time from across-channel space, cutting attention memory sixfold.

Conditional diffusion model with cross-attention that synthesizes subject-specific 3D intrinsic connectivity networks from resting-state fMRI.

RNA modification classification from nanopore direct-RNA current, resolving m6A, inosine, pseudouridine, Gm, and m1A at single-base resolution.

Spike inference from calcium imaging traces, driven by a multistate GCaMP kinetic model that generates the synthetic data its decoders are trained on.

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.

Glucose forecasting from continuous glucose monitor streams over a two-hour horizon. Cuts one-hour rMSE 48.51% on OhioT1DM without training on it.

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

EEG foundation model that pretrains a distance-weighted electrode graph ahead of its convolutional encoder to capture inter-channel relationships.

Spatiotemporal vision transformer that turns a resting-state fMRI scan into 4D brain network maps, supervised by windowed ICA components.

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.

Demultiplexer for direct RNA nanopore sequencing that basecalls the DNA barcode inside the RT adapter, reaching 99% precision on up to 96 barcodes.

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.

Direct RNA nanopore basecaller pairing a densely connected 1D CNN and CTC decoder with a Random Forest classifier that demultiplexes 24 barcodes.