A weakly supervised deep learning approach for label-free imaging flow-cytometry-based blood diagnostics.

ABSTRACT: The application of machine learning approaches to imaging flow cytometry (IFC) data has the potential to transform the diagnosis of hematological diseases. However, the need for manually labeled single-cell images for machine learning model training has severely limited its clinical application. To address this, we present iCellCnn, a weakly supervised deep learning approach for label-free IFC-based blood diagnostics. We demonstrate the capability of iCellCnn to achieve diagnosis of Sézary syndrome (SS) from patient samples on the basis of bright-field IFC images of T cells obtained after fluorescence-activated cell sorting of human peripheral blood mononuclear cell specimens. With a sample size of four healthy donors and five SS patients, iCellCnn achieved a 100% classification accuracy.

SUBMITTER: Otesteanu CF 

PROVIDER: S-EPMC9017143 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature