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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 49–72 of 341 filtered models
Ab initio heterogeneous cryo-EM reconstruction seeds its encoder with foundation-model priors, sorting 100 structures from one simulated mixture.
Native 3D vision transformer self-supervised on unlabeled fluorescence microscopy volumes, segmenting subcellular structures without voxel labels.
Microscopy image restoration foundation model unifying 8 tasks across 5 modalities and 2D/3D data, with zero-shot inference on unseen systems.
Flow-matching generative model that synthesizes fluorescence images of human fibroblasts conditioned on surface micro-topographies.
Generalist neuroimaging vision foundation model pretrained on 5.24M clinical MRI and CT volumes for radiologic diagnosis and report generation.
Self-supervised Siamese network for cryo-electron tomography, enabling zero-shot denoising, segmentation, and macromolecule detection in tomograms.
Synthetic 3D MRI generation spanning brain, prostate, breast and abdomen, with the T1, T2 or FLAIR contrast selected at sampling time.
Vision Transformer foundation model for spatial metabolomics, pretrained on ~4,000 curated METASPACE mass spectrometry imaging datasets.
Self-supervised foundation model for human cortical cytoarchitecture, encoding histological brain sections into anatomically meaningful features.
Vision-language encoders for chest CT that align 3D volumes with radiology reports using contrastive, report-generation, and masked-image objectives.
Medical vision-language model that takes visual prompts on an image and returns answers grounded in pixel-level segmentation masks.
Single-molecule localisation microscopy encoder that embeds nanoscale point clouds into a 128-dimension latent space to compare protein architecture.
Diffusion model for multichannel fluorescent cell microscopy, generating morphologically plausible images aligned to OpenPhenom phenotypic embeddings.
Brain MRI foundation model pairing DenseNet and Vision Transformer backbones with mixture of experts for disease diagnosis and brain age prediction.
Micronuclei instance segmentation for fluorescence microscopy, using an anchor-tuned Mask R-CNN to detect micronuclei and link them to parent nuclei.
Neuro-oncology foundation model for brain tumor MRI, using distributionally robust pretraining for molecular subtyping and survival prediction.
Centrosome segmentation framework chaining YOLOv11 detection, U-Net refinement, and StarDist cell boundaries across immunofluorescence and IHC tissue.
Self-supervised foundation model for 3D brain MRI, learning transferable anatomical representations from unlabeled scans for disease classification.
Cryo-EM and cryo-ET map enhancement model that sharpens density maps with a Mamba-based dual-branch UNet and local resolution-guided learning.
Transcriptome-guided diffusion model generating Cell Painting images for unseen perturbations, improving MOA retrieval accuracy by 16.9% over IMPA.
Image-to-image translation from label-free phase-contrast microscopy to H&E-like images, so pretrained histopathology models run on live cells.
Modality-agnostic foundation model for human brain imaging that runs five core neuroimaging tasks across uncalibrated CT and MRI without retraining.
Histopathology classifier separating atypical from normal mitotic figures, LoRA-adapting a DINOv3 vision transformer with 1.3M trainable parameters.
Generalist MRI vision-language foundation model that handles reconstruction, segmentation, abnormality detection, and report generation in one model.