Deep Learning Enables Individual Xenograft Cell Classification in Histological Images by Analysis of Contextual Features.

ABSTRACT: Patient-Derived Xenografts (PDXs) are the preclinical models which best recapitulate inter- and intra-patient complexity of human breast malignancies, and are also emerging as useful tools to study the normal breast epithelium. However, data analysis generated with such models is often confounded by the presence of host cells and can give rise to data misinterpretation. For instance, it is important to discriminate between xenografted and host cells in histological sections prior to performing immunostainings. We developed Single Cell Classifier (SCC), a data-driven deep learning-based computational tool that provides an innovative approach for automated cell species discrimination based on a multi-step process entailing nuclei segmentation and single cell classification. We show that huma

SUBMITTER: Juppet Q 

PROVIDER: S-EPMC8236058 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature