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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 289–312 of 386 filtered models
Open-source, lightweight generalist vision-language foundation model for diverse biomedical imaging and text tasks.
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.
Ultrasound foundation model pretrained on over two million multi-organ images, transferring to segmentation, classification, and image enhancement.
Interactive foundation model for biomedical image segmentation, prompted with scribbles, clicks, and bounding boxes to segment unseen structures.
Chest X-ray conversational assistant that fine-tunes LLaVA-Med on instruction data enriched with predictions from expert radiograph classifiers.
Semi-supervised cryo-ET segmentation framework that adapts DINOv2 vision transformers for 3D organelle annotation using sparse 2D slice labels.
Mitochondrial ultrastructure segmentation in electron microscopy volumes, resolving membranes and cristae with cross-sample domain adaptation.
3D vision-transformer foundation model for multimodal neuroimage segmentation, pretrained self-supervised on brain MRI from 41,400 participants.
Fluorescence microscopy denoising for transient synaptic signals, done frame by frame so one checkpoint transfers across sensors and frame rates.
Microsoft Research multimodal LLM for grounded chest X-ray report generation, localizing each described finding with bounding boxes on the image.
Virtual staining models that translate label-free light microscopy into fluorescent-equivalent predictions of nuclei and plasma membranes.
Neural ab initio reconstruction for cryo-EM and cryo-ET that jointly infers particle poses and a continuous landscape of conformational states.
Keypoint-based foundation model for brain MRI registration, pretrained on over 100,000 3D volumes for rigid, affine, and deformable alignment.
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.
Self-supervised 3D cell segmentation for fluorescence microscopy, pairing WNet3D with Swin-UNetR to segment volumes without annotated training data.
Universal brain lesion segmentation for multi-modal brain MRI, using a Mixture of Modality Experts to span diverse modalities and lesion types.
Mixture-of-Experts foundation model for medical image segmentation that generalizes across imaging modalities and clinical centers.
CLIP-style vision-language model for echocardiography, pretrained on echo videos and cardiologist reports for zero-shot cardiac interpretation.
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.