All Competitors

A self-supervised foundation model for structural brain MRI combining DenseNet and Vision Transformer with Mixture of Experts for multi-task brain disease diagnosis and brain age prediction.

A SimCLR self-supervised foundation model for 3D brain MRI, pretrained on 18,759 patients across 11 neurological-disease datasets for diverse diagnostic tasks.

A 3D brain MRI segmentation foundation model trained on 66,000+ brain image-label pairs across 14 MRI sub-modalities, paired with a hypergraph dynamic adapter for brain disease analysis.

A self-supervised vision foundation model for structural brain MRI that produces general-purpose features adaptable to diverse downstream clinical and neuroscience tasks.

A masked-autoencoder foundation model that pre-trains a 3D Residual Encoder U-Net on ~39k brain MRIs to improve volumetric medical image segmentation.

Anatomical foundation models for brain MRI, pretrained with weakly supervised contrastive learning over cortical anatomy for diagnosis and clinical score prediction.

A foundational keypoint model for robust, flexible brain MRI registration, pretrained on over 100,000 3D volumes and supporting rigid, affine, and deformable alignment.

A universal foundation model for brain lesion segmentation on multi-modal brain MRI, using a Mixture of Modality Experts to handle diverse modalities and lesion types.

Self-supervised vision transformer autoencoder pretrained on ~57,000 multi-contrast brain MRIs via masked image modeling for downstream brain tumor diagnosis.

Hierarchical knowledge-enhanced vision-language pre-training model for universal brain MRI diagnosis across 10+ diseases from multi-modal scans and reports.

A vision-language model that bootstraps pre-training from frozen image encoders and LLMs for 3D medical image diagnosis and visual question answering, demonstrated on brain MRI.