Article

Automatic Classification of Circulating Blood Cell Clusters based on Multi-channel Flow Cytometry Imaging

2025-10-21

Abstract excerpt

Circulating blood cell clusters (CCCs) containing red blood cells (RBCs), white blood cells (WBCs), and platelets are significant biomarkers linked to conditions like thrombosis, infection, and inflammation. Flow cytometry, paired with fluorescence staining, is commonly used to analyze these cell clusters, revealing cell morphology and protein profiles. While computational approaches based on machine learning have...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 45e7829e-09aa-53bc-862c-f99f02a8f708
DOI: 10.1101/2025.10.20.683505

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Automatic Classification of Circulating Blood Cell Clusters based on Multi-channel Flow Cytometry ImagingDOI 10.1101/2025.10.20.683505Read

Deep Optical Blood Analysis: COVID-19 Detection as a Case Study in Next Generation Blood ScreeningSemantic proximity Read

Quantitative image analysis pipeline for detecting circulating hybrid cells in immunofluorescence images with human-level accuracySemantic proximity Read

Label-free tumor cells classification using deep learning and high-content imagingSemantic proximity Read

Deep-learning-assisted biophysical imaging cytometry at massive throughput delineates cell population heterogeneity.Semantic proximity Read

Representation learning enables robust single cell phenotyping in whole slide liquid biopsy imaging.Semantic proximity Read

Integrated cytometry with machine learning applied to high-content imaging of human kidney tissue for <i>in-situ</i> cell classification and neighborhood analysisSemantic proximity Read

Imaging Flow Cytometry: Development, Present Applications and Future ChallengesSemantic proximity Read

An efficient fluorescence in situ hybridization (FISH)-based circulating genetically abnormal cells (CACs) identification method based on Multi-scale MobileNet-YOLO-V4Semantic proximity Read

DeepHeme: A generalizable, bone marrow classifier with hematopathologist-level performanceSemantic proximity Read

Cyto-Feature Engineering: A Pipeline for Flow Cytometry Analysis to Uncover Immune Populations and Associations with Disease.Semantic proximity Read

Decoupling Detection and Classification to Improve Morphological Phenotype Analysis of Sickle Red Blood Cells in Full-Scope MicroscopySemantic proximity Read

Contrastive Representation Learning for Single Cell Phenotyping in Whole Slide Imaging of Enrichment-free Liquid BiopsySemantic proximity Read