Massachusetts General Hospital

A modality-agnostic, multi-task foundation model for human brain imaging that runs five core tasks across uncalibrated CT and MRI without retraining.

A spatiotemporal foundation model that learns generalizable representations directly from 4D functional MRI volumes for diverse brain-imaging tasks.

Multimodal multitask foundation model that fuses 3D low-dose chest CT with clinical data to perform 17 lung-cancer-screening tasks via free-text question answering.

Open-source, lightweight generalist vision-language foundation model for diverse biomedical imaging and text tasks.

Interactive foundation model for biomedical image segmentation, prompted with scribbles, clicks, and bounding boxes to segment unseen structures.

A multimodal vision-language copilot for human pathology that analyzes histology images and answers diverse pathology queries in natural language.

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 self-supervised 3D CT foundation model that extracts general-purpose tumor representations for cancer imaging biomarker discovery across diverse downstream tasks.

An in-context learning model that segments unseen medical imaging tasks from a few labeled examples, with no retraining or fine-tuning.