United Imaging Intelligence

EEG foundation model pairing masked contextual modelling with cross-view invariance learning over 11,000 hours of routine clinical recordings.

Medical vision-language model for MR, CT and radiography, pretrained by curriculum learning on 12 million image-text pairs for zero-shot diagnosis.

Reference-guided anatomical segmentation for medical images, pairing vision-language spatial reasoning with a co-trained SAM 2 mask decoder.

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

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