deepcell-types
DeepCell Types is a novel approach to cell phenotyping for spatial proteomics that addresses the challenge of generalization across diverse datasets with varying marker panels.
See the deepcell-types paper for details!
Model Details
deepcell-types is a language-informed vision model comprising three main components:
- A visual encoder,
- A language encoder, and
- A channel-wise transformer.
See the deepcell-types paper for further details.
Model Description
- Developed by: Van Valen Lab
- Funded by: Funding details
- Model type: Language-informed vision model
- License: Modified-apache2.0-noncommercial
Model Sources [optional]
- Repository: https://github.com/vanvalenlab/deepcell-types
- Paper [optional]: https://www.biorxiv.org/content/10.1101/2024.11.02.621624v3.full
- Demo [optional]: https://vanvalenlab.github.io/deepcell-types/site/tutorial.html
Uses
Cell phenotyping for spatial proteomic images.
Direct Use
The weights provided in this repository are suitable for cell phenotype prediction across a variety of tissues and spatial proteomic imaging methods.
Downstream Use
deepcell-types is a generalist model designed to perform cell-type prediction across all cell types and spatial proteomic imaging modalities. However, it is possible to fine-tune deepcell-types for specific applications. See the methods section of the paper for details.
Out-of-Scope Use
The model herein is provided subject to license terms prohibiting use for commercial applications.
Inquiries regarding commercial licensing should be directed to the Caltech Office of Technology Transfer.
How to Get Started with the Model
See the deepcell-types tutorial.