All Competitors

3D vision-transformer foundation model for multimodal neuroimage segmentation, pretrained self-supervised on brain MRI from 41,400 participants.

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

Whole-slide histopathology foundation model pretrained on 1.3 billion image tiles from 171,189 clinical slides spanning 31 tissue types.

Vision-language foundation model pre-trained on screening mammogram-report pairs to improve data efficiency and robustness in breast cancer detection.

Ophthalmic imaging foundation model pretrained on 2.78M images across 11 modalities for diagnosis, prognosis, and visual question answering.

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

Compact 22M-parameter ViT-S pathology foundation model pre-trained on 195M tiles, spanning subcellular segmentation to slide-level prediction.

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.

Brain MRI foundation model pretrained with masked image modeling on roughly 57,000 multi-contrast head scans for brain tumor diagnosis.

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.

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

Self-supervised pathology foundation model with a 300M-parameter vision transformer tile encoder, trained on a multi-stain whole-slide image corpus.

Histopathology foundation model that encodes 224x224 H&E patches into compact 384-dimensional embeddings for tumor and biomarker classifiers.

Health acoustics foundation model that turns short clips of coughs and breaths into embeddings for building acoustic biomarker models with less data.

Vision foundation model for the tree of life, trained on TreeOfLife-10M for zero-shot species classification of plants, animals, and fungi.

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.

Multi-modal ophthalmic foundation model for generalist eye AI, spanning fundus imaging and OCT for disease screening, segmentation, and biomarkers.