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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 265–288 of 341 filtered models
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.
Brain MRI foundation model pretrained with masked image modeling on roughly 57,000 multi-contrast head scans for brain tumor diagnosis.
Microscopy foundation model for high-content screening, embedding genome-scale CRISPR knockout and compound perturbations from Cell Painting images.
Lightweight mixture-of-experts medical vision-language model routing visual question answering and image classification to domain-specific experts.
Swin transformer foundation model for fluorescence microscopy image restoration, unifying denoising, super-resolution, and volumetric reconstruction.
Multimodal large language model for 3D medical imaging that handles report generation, visual question answering, and segmentation on CT volumes.
Vision-language model for 3D chest CT that aligns whole volumes with radiology reports for zero-shot abnormality detection and case retrieval.
Separable 4D CNN that strips rotational streak artifacts from respiration-resolved cone-beam CT, processing all ten breathing phases in one pass.
Histopathology vision-language foundation model pretrained on 1.17 million image-caption pairs with contrastive and captioning objectives.
Self-supervised 3D CT foundation model that extracts general-purpose tumor representations for cancer imaging biomarker discovery and prognosis.
Foundation model for medical image registration that aligns CT and MRI across anatomies and modalities without per-pair optimization or retraining.
Self-supervised pretraining framework for 3D medical image encoders that learns anatomy by predicting where a sub-volume sits within a CT scan.
Spot detection for single-molecule RNA FISH and fluorescence microscopy, trained on a differentiable F1 approximation, needing no threshold tuning.
Organelle phenotyping model that scores how perturbations shift subcellular localization and morphology in confocal images of human neurons.
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.
Cryo-electron tomography membrane analysis pipeline pairing generalizable U-Net membrane segmentation with mesh-based particle localization.
Vision-language model for annotation-free pathology localization, marking the finding a text prompt names in X-ray, histology and fundus images.