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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 1–24 of 386 filtered models
Self-supervised 4D live-cell microscopy model embedding mitochondrial shape and motion. Frozen features predict membrane potential at R² = 0.91.
Self-supervised ViT-Small/14 domain-adapted from DINOv2 to chest radiographs, linear-probed for stroke and heart failure detection.
Label-free virtual staining framework that turns gigapixel spectral whole-slide images into H&E histology without tiling seams or color drift.
Intraoperative X-ray to CT registration that reads the preoperative volume as a second input, removing the per-patient training step entirely.
Conditional residual-diffusion model for multi-parametric brain MRI that synthesizes a missing contrast and returns pixel-level uncertainty maps.
Crop phenotyping vision-language model that answers questions about arbitrarily shaped image regions, routing each task to its own expert branch.
Nuclear segmentation and subtype classification in immunohistochemistry slides, steering frozen SAM and UNI encoders with frequency-domain prompts.
Chest radiograph foundation model with a single latent state behind both diagnostic readout and report-conditioned image generation.
Cortical surface registration network that learns which folds predict functional organization, using multi-modal supervision during training only.
Differentiable surrogate for near-infrared light transport in the human head, predicting time-resolved fluence 466x faster than Monte Carlo.
Conditional flow matching model that reconstructs 4D left-ventricular geometry and motion from a 12-lead ECG and basic demographic variables.
Pediatric brain MRI segmentation model trained on synthetic scans, so a single 3D U-Net labels clinical acquisitions from neonates to young adults.
Instruction-driven latent diffusion that segments polyps and skin lesions by painting a color-coded overlay on the image, not predicting a mask.
Mammography vision-language model that samples sparse high-res patches instead of downscaling, for zero-shot BI-RADS, density and cancer readout.
Image-to-transcriptome diffusion model generating scGPT-space single-cell RNA embeddings from Cell Painting fluorescence microscopy features.
Dense self-supervised pretraining that learns pixel- and voxel-level features for segmenting cardiac MRI and abdominal CT from very few labels.
Skeletal CT reconstruction from two orthogonal X-ray projections, using conditional latent diffusion that classifies scoliosis as the volume forms.
Cardiac MR video foundation model whose frozen encoder estimates ejection fraction, myocardial strain and cardiomyopathy from cine, LGE and mapping.
Low-dose CT reconstruction by flow matching along the residual path to the paired standard-dose image, reaching diffusion-level quality in one step.
Flow-matching generative prior over high-quality PET images, used as a manifold constraint in an ADMM solver for low-dose PET reconstruction.
Volumetric cell segmentation for 3D fluorescence microscopy, pretrained on 5.1 TB of unlabeled volumes and generalizing across species and imaging.
3D brain MRI synthesis from separate hemisphere and cerebellum-brainstem diffusion priors, assembled and refined into one volume with editable parts.
Cryo-EM pose estimation conditioned on a reference volume handed in at inference, assigning particle orientations zero-shot on unseen structures.
Accelerated MRI reconstruction that plugs a pretrained consistency-model prior into regularization by denoising, using four network evaluations.