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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 145–168 of 341 filtered models
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.
Vision-language foundation model for fetal ultrasound, pretrained on 210,035 image-text pairs for plane classification, biometry, and segmentation.
Medical vision-language model that unifies image comprehension and generation in one autoregressive transformer via heterogeneous LoRA adapters.
Cervical cytology screening system pretrained on 127,471 whole-slide images from 48 centers, with test-time adaptation for new clinical sites.
865M-parameter multimodal foundation model that fuses 3D low-dose chest CT with clinical data to answer 17 lung cancer screening questions.
Nuclei detection and classification in histopathology whole slide images, replacing segmentation masks with direct transformer-based set prediction.
Pan-tumour CT foundation model pretrained on 30,000 synthetic 3D scans carrying lesion masks and structured reports across ten organ systems.
End-to-end transformer reading fluorescence microscopy video to return single-molecule trajectories with Hurst exponents and diffusion coefficients.
Multimodal medical imaging foundation model for zero-shot clinical diagnosis and report generation from chest X-ray and CT in English and Chinese.
Medical image segmentation model that replaces MedSAM's manual box prompts with a diffusion prompt encoder and labels each mask by class.
Self-supervised 3D vision foundation model for non-contrast head CT, pretrained on 361,663 scans to detect a broad range of intracranial disease.