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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 121–144 of 341 filtered models
Unified electron microscopy image analysis toolkit built on EM-DINO, a vision foundation model pretrained on 5 million diverse EM images.
Promptable medical image segmentation trained only on procedurally generated synthetic images, then applied zero-shot to CT, MRI, and ultrasound.
Complex-valued diffusion model generating synthetic MRI k-space phase from magnitude images, raising k-space skull-stripping Dice from 41.1% to 80.1%.
3D MRI organ segmentation foundation model built on Swin-UNETR and trained on the UKBOB whole-body dataset covering 72 organs and skeletal structures.
RNA 3D structure reconstruction from cryo-EM density maps, using a 3D U-Net that predicts 18 atom types and assigns sequence by global alignment.
Promptable 3D medical image and video segmentation foundation model fine-tuned from SAM 2.1, cutting lesion annotation time by up to 92%.
General medical vision-language model trained with reinforcement learning to reason step by step over medical images for diagnosis and visual QA.
Hypergraph foundation model for brain disease diagnosis from resting-state fMRI, self-supervised on high-order connectivity among brain regions.
Histology nuclei segmentation that adapts SAM to train on several datasets at once, aligning auxiliary domains without diluting the primary one.
Multimodal medical imaging foundation model built for chromosome karyotype analysis, with 92.75% sensitivity for structural abnormality detection.
Whole-heart segmentation foundation model for CT and MRI, pretrained self-supervised on unlabeled cardiac scans with an xLSTM-UNet backbone.
3D segmentation foundation model for female genito-pelvic anatomy, reading T2-weighted MRI and radiotherapy planning CT with one shared encoder.
Medical vision-language model trained with reinforcement learning for generalizable reasoning across eight imaging modalities and five question types.
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.
Cryo-ET tilt-series classifier that flags and removes tilts corrupted by drift, contamination, ice reflections, lamella edges, or thick lamellae.
Cell segmentation for image-based spatial transcriptomics that fuses RNA point clouds with any number of membrane and nuclear staining channels.
Learned compression autoencoders for histopathology whole-slide images, tuned so reconstructions preserve the features downstream models rely on.
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.