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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 241–264 of 341 filtered models
Brain-dynamics foundation model for resting-state fMRI, adapting the Joint-Embedding Predictive Architecture with brain gradient positioning.
Cryo-EM and cryo-ET heterogeneity analysis that separates subunit rigid-body motion from compositional change into distinct latent spaces.
Text-guided MRI synthesis model that generates brain MR sequences and resolutions on demand from routine scans using imaging-metadata prompts.
Chest X-ray foundation model that pairs masked image modeling with image-report contrastive alignment for zero-shot diagnosis and phrase grounding.
Brain MRI foundation model family pretrained with anatomically informed contrastive learning for diagnosis and clinical score prediction.
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.