All Competitors
Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 1–20 of 20 filtered models
Cellpose-SAM
HHMI Janelia Research Campus
Generalist cell segmentation model combining SAM's ViT-L backbone with Cellpose flow fields. First model to surpass average human annotators on the Cellpose benchmark.
OmniEM
Peking University
Unified electron microscopy image analysis toolkit built on EM-DINO, a vision foundation model pretrained on 5 million diverse EM images.
Cellpose 3
HHMI Janelia Research Campus
Generalist cell segmentation framework with a super-generalist cyto3 model and one-click image restoration networks optimized for downstream segmentation quality.
SubCell
Chan Zuckerberg Initiative / Human Protein Atlas / Lundberg Lab
Self-supervised Vision Transformer models trained on proteome-wide fluorescence microscopy images from the Human Protein Atlas for subcellular protein localization.
BiomedParse
Microsoft Research
A biomedical foundation model for joint segmentation, detection, and recognition across nine imaging modalities using natural language prompts.
OpenPhenom-S/16
Recursion Pharmaceuticals
Channel-agnostic Vision Transformer trained on 3M+ Cell Painting images via masked autoencoder, producing 384-dimensional morphological embeddings for zero-shot phenotypic analysis.
Cryo-IEF
Westlake University
Foundation model pre-trained on 65M cryo-EM particle images via contrastive learning, enabling zero-shot classification, pose clustering, and quality assessment.
CryoFM
ByteDance Seed
Generative foundation model for cryo-EM density maps using flow matching, enabling zero-shot denoising, map sharpening, and missing wedge restoration.
CryoSAM
Xu Lab
Training-free cryo-ET tomogram segmentation that adapts SAM and DINOv2 for 3D volumetric data, enabling full tomogram segmentation from a single user prompt.
CryoViT
Stanford University
Semi-supervised cryo-ET segmentation framework that adapts DINOv2 vision transformers for 3D organelle annotation using sparse 2D slice labels.
Cytoland
Chan Zuckerberg Biohub / Mehta Lab
A suite of virtual staining models that translate label-free microscopy images into fluorescent-equivalent staining of nuclei and plasma membranes.
CellSeg3D
Mathis Lab
Self-supervised 3D cell segmentation for fluorescence microscopy using WNet3D and Swin-UNetR, achieving supervised-level performance without annotated training data.
UniFMIR
Fudan University
Foundation model for fluorescence microscopy image restoration, unifying super-resolution, denoising, isotropic reconstruction, projection, and volumetric reconstruction in one Swin transformer.
CONCH
Mahmood Lab / Brigham and Women's Hospital
Vision-language foundation model for computational pathology, pretrained on 1.17M histopathology image-caption pairs with contrastive and captioning objectives.
CellSAM
Van Valen Lab
Universal cell segmentation model adapting Meta's SAM for biology. Segments mammalian cells, yeast, and bacteria across diverse imaging modalities with human-level accuracy.
PLIP
Stanford University
CLIP-based vision-language foundation model for pathology, fine-tuned on 208,414 image-text pairs. Enables zero-shot tissue classification and image retrieval.
CellViT
Institute for AI in Medicine
Vision Transformer for cell instance segmentation and classification in H&E digital pathology, extended by CellViT++ with foundation model backbones and few-shot adaptation.
BiomedCLIP
Microsoft Research
Multimodal biomedical foundation model trained on 15M PubMed Central figure-caption pairs via contrastive learning, achieving state-of-the-art zero-shot performance across imaging modalities.
Cellpose 2.0
HHMI Janelia Research Campus
Human-in-the-loop cell segmentation framework enabling custom model training from as few as 100-200 corrected annotations.
Cellpose
HHMI Janelia Research Campus
Generalist deep learning algorithm for cell and nucleus instance segmentation using simulated diffusion flows, without per-dataset retraining.