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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 241–264 of 386 filtered models
Vision transformers trained on Human Protein Atlas fluorescence microscopy for subcellular protein localization and cell morphology representation.
Tissue-aware foundation model that restores brain MRI quality across motion correction, super-resolution, denoising, and harmonization.
Self-supervised vision foundation model for structural brain MRI, providing a reusable encoder for brain age, survival, and image classification.
Spatiotemporal vision transformer that turns a resting-state fMRI scan into 4D brain network maps, supervised by windowed ICA components.
3D blood vessel segmentation across CT, MRI, light-sheet microscopy and OCTA volumes, generalizing zero-shot to imaging domains absent from training.
Radiology CT framework pairing lesion-driven contrastive slice embeddings with attention pooling to predict clinical endpoints without fine-tuning.
Renal pathology segmentation model resolving 14 glomerular tissue, cell, and lesion classes in human and mouse whole-slide images.
Ultrasound foundation model pretrained by federated learning across 16 institutions, transferring to disease diagnosis and lesion segmentation.
Multi-scale latent diffusion model for histopathology that synthesizes tissue patches at any magnification and composes them into 4096-pixel images.
Promptable ultrasound image segmentation foundation model, a SAM adaptation trained on US-43d, the largest public ultrasound segmentation corpus.
Conditional denoising diffusion model that synthesizes chest radiographs from patient demographics and echocardiographic ventricular measurements.
Biomedical imaging foundation model that segments, detects, and recognizes structures across nine modalities from natural language prompts.
De novo atomic model building from cryo-EM density maps, adapting AlphaFold2 with local attention and a 3D rotary position embedding.
Cell Painting microscopy foundation model, a channel-agnostic masked autoencoder producing morphological embeddings for zero-shot phenotypic analysis.
Cell Painting image generation conditioned on a control well image and a compound's structure, covering cell lines and chemicals never trained on.
Cryo-EM foundation model pre-trained on 65 million particle images, enabling zero-shot classification, pose clustering, and quality assessment.
Cell microscopy foundation model with a 1.9-billion-parameter masked autoencoder producing embeddings that stay consistent across screening batches.
Imaging-genetics foundation model pairing SNP genotypes with brain-MRI phenotypes by contrastive learning to surface many-to-many associations.
Protein subcellular localization from sequence, returning both a text label and a synthetic fluorescence image of the protein inside a given nucleus.
Cell phenotyping model for spatial proteomics using a language-informed vision transformer to classify cell types zero-shot across marker panels.
Masked-autoencoder foundation model that pre-trains a 3D Residual Encoder U-Net on roughly 39,000 brain MRIs for volumetric image segmentation.
Self-supervised vision transformer pretrained on chest X-rays to produce a domain-specific foundation model for classification and lung segmentation.
Spot detection and quantification in 5D fluorescence microscopy. Pretrained 2D and 3D U-Nets segment foci, then Gaussian fitting measures each one.
Multimodal large language model that interprets 12-lead electrocardiogram images, answering open-ended clinical questions and generating ECG reports.