All Competitors

fMRI foundation model of the human brain connectome: 1.2B parameters and brain-environment interaction tokens for behavior and disease prediction.

Brain MRI foundation model pairing DenseNet and Vision Transformer backbones with mixture of experts for disease diagnosis and brain age prediction.

Spatiotemporal foundation model that learns representations directly from 4D functional MRI volumes for disease diagnosis and phenotype prediction.

Medical vision-language model trained with reinforcement learning for generalizable reasoning across eight imaging modalities and five question types.

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

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

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

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

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

Self-supervised foundation model for functional brain network analysis from resting-state fMRI, pretrained across 30 datasets for disorder diagnosis.

Foundation model spanning invasive SEEG/iEEG and non-invasive EEG in one backbone, with zero- and few-shot transfer across neurological disorders.

Radiology foundation model that reads interleaved 2D and 3D scans with text for diagnosis, visual question answering, and report generation.

Vision transformer for electrocardiograms that reads the printed 12-lead ECG as an image, enabling data-efficient diagnosis from few labeled examples.