Deepcell Grant Application Resources Document
Deepcell Software Suite Demo
Deepcell Platform Overview
SLAS 2023 Solutions Spotlight Presentation
Deepcell Platform Brochure
A novel platform using deep learning models to perform multi-dimensional morphology analysis for biological discoveries
Self-supervised deep learning models capture multi-dimensional features for cell morphology analysis from brightfield images
Multi-omics analysis integrating deep learning morphology profiling and scRNA-Seq reveals lung tumor heterogeneity and enriches subpopulations
A novel platform using deep learning models to perform multidimensional morphology analysis for biological discoveries
Multi-dimensional morphology analysis enables identification and label-free enrichment of heterogeneous tumor cell populations
Classification and enrichment of carcinoma cells in real-time based on high-dimensional morphology enables increased sensitivity of molecular and cytology analysis
A platform for high-resolution morphology analysis reveals tumor heterogeneity and enables label-free enrichment of target subpopulations
Deep learning morphology profiling identifies and enriches carcinoma cells from effusion samples in real-time for cytological and molecular analysis
Deep learning enables high-dimensional morphological detection and characterization of carcinoma cells from patient effusion samples
Deep learning morphology analysis identifies and enriches carcinoma cells from body fluids in real-time for cytological and molecular analysis
Morphological characterization and sorting of viable and label-free malignant cells from NSCLC tissue using deep learning
Deep learning enables real-time classification and label-free enrichment of cells in flow
Enrichment of Single Cells Using Deep Learning Based Classification and Sorting
Deep learning enables label-free profiling of the tumor microenvironment and enrichment of rare cancer cells
Quantitative phenotyping and enrichment of live single cells via deep learning
Deep learning enables the identification and isolation of single cells of interest using high resolution images of non-labeled cells in flow
