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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 169–192 of 386 filtered models
Medical vision-language model trained with reinforcement learning for generalizable reasoning across eight imaging modalities and five question types.
3D segmentation foundation model for female genito-pelvic anatomy, reading T2-weighted MRI and radiotherapy planning CT with one shared encoder.
Whole-body CT segmentation covering 235 fine-grained anatomies: 193 organs, 33 lymph node stations, and 9 lesion types from one unified network.
Cryo-EM density-map-to-atomic-structure modeling that fuses protein language model embeddings with density voxels, then refines with AlphaFold3.
Pathology image restoration recovering all-in-focus histology from single defocused focal planes, guided by semantic, defocus, and edge prompts.
Learned compression autoencoders for histopathology whole-slide images, tuned so reconstructions preserve the features downstream models rely on.
Cell segmentation for image-based spatial transcriptomics that fuses RNA point clouds with any number of membrane and nuclear staining channels.
Cryo-ET tilt-series classifier that flags and removes tilts corrupted by drift, contamination, ice reflections, lamella edges, or thick lamellae.
Vision-language model for open-vocabulary mouse behavior analysis, describing multi-view video and pose kinematics in natural language.
Retinal encoding models that predict ganglion cell responses to visual stimuli, with pretrained checkpoints across four species and two modalities.
Clinical imaging encoder multitask-pretrained across X-ray, mammography, dermoscopy, fundus, ultrasound, CT, and histopathology for few-shot transfer.
PET/CT foundation model pretrained by cross-modal masked autoencoding on whole-body scans, for tumor lesion segmentation and lymphoma staging.
Promptable 3D segmentation for particle picking in cryo-electron tomography, conditioned on a reference subtomogram to detect any target complex.
Chest X-ray encoder that detects CT-level abnormalities by aligning radiographs with 3D CT volumes and radiology reports in a shared embedding space.
Patch-based 3D diffusion model that generates teravoxel-scale virtual mouse brain volumes conditioned on spatially resolved gene expression.
Universal ultrasound segmentation foundation model adapting the Segment Anything Model to eight anatomical regions in a single promptable network.
Cell Painting image encoder that turns whole-slide multi-channel microscopy into morphological profiles in one pass, with no cell segmentation step.
2B-parameter medical vision-language model that uses reinforcement learning to show interpretable reasoning for radiology visual question answering.
Whole-brain axon and soma segmentation foundation model and registration pipeline for developmental connectomics, with no per-stage retraining.
Semantic layout-guided 3D diffusion model that synthesizes thoracic CT volumes from a lung and nodule mask to expand lung cancer screening data.
Promptable 3D segmentation foundation model for whole-body PET, delineating organs and lesions from one or a few clicked points.
Chest X-ray vision-language model that drafts the findings section of a radiology report, at 7B parameters small enough to run on a single GPU.