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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 265–288 of 386 filtered models
Diffusion model translating in both directions between protein sequences and fluorescence microscopy images to predict subcellular localization.
Computed tomography embedding model that compresses a whole DICOM CT volume into a 1,408-number vector for data-efficient downstream classifiers.
3D CT vision-language model that drafts radiology reports, answers questions about volumes, and screens for disease from a masked-autoencoder encoder.
Vision-language chat model for 3D chest CT volumes, answering free-form questions and drafting radiology report findings from a frozen 3D encoder.
Self-supervised contrastive model embedding cell and organelle dynamics from time-lapse microscopy for cell-state analysis without manual labels.
Echocardiography vision foundation model self-distilled on 20 million ultrasound images from 11 clinical centres, with swappable task decoders.
CT segmentation foundation model that uses task prompts to segment 83 anatomical structures and lesions across whole-body scans in a single network.
Masked-autoencoder foundation model for chest radiographs, self-supervised on 1.04 million unlabelled images for disease screening and localization.
Generative foundation model for cryo-EM density maps using flow matching, enabling zero-shot denoising, map sharpening, and missing wedge restoration.
Medical imaging embedding model spanning X-ray, CT, MRI, dermoscopy, OCT, fundus, ultrasound, histopathology and mammography in one encoder.
Cardiac MR vision foundation model self-supervised on 36 million images, fine-tuned for segmentation, view classification and pathology detection.
Domain-aware multi-task pretrained 3D Swin Transformer for T1-weighted brain MRI, transferring to Alzheimer's, Parkinson's and brain age tasks.
Medical vision-language generalist that answers questions, classifies disease, localizes findings and drafts radiology reports from one checkpoint.
Brain-dynamics foundation model for resting-state fMRI, adapting the Joint-Embedding Predictive Architecture with brain gradient positioning.
Multimodal neuroimaging model fusing resting-state fMRI and EEG, pretrained without labels across spatial, temporal, and spectral domains.
Unsupervised VAE-GAN hybrid profiling single-cell morphology in a 10-dimensional disentangled latent space transferable across imaging modalities.
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.
Cell segmentation and cell-type classification for multiplexed tissue images, predicting masks, boxes and class labels from one transformer backbone.
A 3D UNETR++ transformer that enhances experimental cryo-EM density maps, improving map-model agreement and automated de novo structure modeling.
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.