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