All Competitors

A self-improving text-to-image diffusion foundation model that generates synthetic medical images across multiple modalities and organs to augment downstream clinical AI tasks.

A Segment Anything Model (SAM) variant finetuned on diverse medical images, delivering a reusable promptable checkpoint for interactive and automatic medical image segmentation.

A biomedical foundation model for joint segmentation, detection, and recognition across nine imaging modalities using natural language prompts.

A Mixture-of-Experts foundation model for medical multimodal image segmentation that generalizes across imaging modalities and clinical centers.

A generalist radiology foundation model that handles interleaved 2D and 3D medical scans with text for diagnosis, VQA, and report generation.

A multimodal medical vision-language model that performs few-shot generative visual question answering over medical images and text.

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