All Competitors
Every biological foundation model, evaluated and ranked by the bio.rodeo team
Applications
Architectures
Learning Paradigms
Biological Subjects
Showing 1–9 of 9 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.
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.
CellFM
Sun Yat-sen University
An 800M-parameter single-cell foundation model pre-trained on 100 million human cells via a RetNet architecture for cell annotation, perturbation prediction, and gene analysis.
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.
CellPLM
OmicsML
Single-cell transformer that treats cells as tokens and tissues as sentences, encoding cell-cell relationships with 100x faster inference than prior pre-trained models.
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.
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.