All Competitors

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

Chest X-ray foundation model that pairs masked image modeling with image-report contrastive alignment for zero-shot diagnosis and phrase grounding.

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

Medical vision-language model trained on the MedTrinity-25M dataset, answering questions and generating text about radiology and histology images.

Chest X-ray embedding model built on ELIXR, producing image and image-text embeddings for data-efficient and zero-shot radiograph classification.

SAM2-based foundation model that segments 2D and 3D medical images by treating volumes and image sets as video object tracking.

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

Open medical multimodal LLMs (7B and 34B) for visual question answering over radiology, pathology, and endoscopy images, trained on PubMedVision.

Microsoft Research multimodal LLM for grounded chest X-ray report generation, localizing each described finding with bounding boxes on the image.

Family of medical multimodal models built on Gemini, adding uncertainty-guided web search, custom modality encoders, and long-context EHR reasoning.

Generalist medical vision-language foundation model with 40B parameters, spanning radiology, pathology, dermatology, retinography, and endoscopy.

Lightweight mixture-of-experts medical vision-language model routing visual question answering and image classification to domain-specific experts.

Multimodal large language model for 3D medical imaging that handles report generation, visual question answering, and segmentation on CT volumes.

Self-supervised pretraining framework for 3D medical image encoders that learns anatomy by predicting where a sub-volume sits within a CT scan.

Instruction-tuned vision-language foundation model for chest X-ray interpretation, with 8 billion parameters spanning eight clinical task types.

Promptable foundation model for universal medical image segmentation, fine-tuned from SAM on 1.57M image-mask pairs across 10 imaging modalities.

Vision-language pretraining for 3D CT volumes, aligning scans with their radiology reports for zero-shot classification, retrieval, and segmentation.

Radiology-specific multimodal LLM that generates the findings section of a chest X-ray report from a frontal image, pairing RAD-DINO with Vicuna-7B.

Promptable 3D foundation model for volumetric CT segmentation, covering over 200 anatomical categories through point, box, and free-text prompts.

Chinese medical vision-language model pairing a Vision Transformer with an LLM to caption medical images and answer clinical questions in Chinese.

Fully 3D promptable segmentation foundation model for volumetric CT and MR, encoding whole volumes so anatomy can be segmented from one prompt point.

Chest X-ray vision-language model that generates free-text radiology reports, pairing a CXR-specific image encoder with a 7B LLaMA-2 language model.

Large-scale chest X-ray vision-language pretraining model that learns image-report alignment for zero-shot and few-shot radiograph classification.

Abdominal CT segmentation model driven by CLIP text embeddings, covering 25 organs and 6 tumor types with zero-shot extension to new categories.