Deep Learning based Cell Classification in Imaging Flow Cytometer

Abstract

Deep learning is an idea technique for image classification. Imaging flow cytometer enables high throughput cell image acquisition and some have integrated with real-time cell sorting. The combination of deep learning and imaging flow cytometer has changed the landscape of high throughput cell analysis research. In this review, we focus on deep learning technologies applied in imaging flow cytometer for cell classification and real-time cell sorting. This article describes some recent research, challenges and future trend in this area.

Advancing precision single-cell analysis of red blood cells through semi-supervised deep learning using database of patients with post-COVID-19 syndrome

“…DL has revolutionized the field of image analysis, offering unprecedented capabilities in identifying and classifying complex patterns within visual data. Its importance in the cell image analysis is particularly noteworthy 13,35 . Traditional image analysis techniques, while useful, often struggle with the high dimensionality and variability inherent in cellular images.…”

Hemodynamic Analysis and Diagnosis Based on Multi-Deep Learning Models

“…The goal of a routine blood examination is to measure the number and concentration of blood cells in a patient's blood in order to determine, among other things, if the patient has anemia or an infection. Therefore, high-precision blood cell detection technology is essential for human health diagnostics [1]. There are five different types of white blood cells, neutrophils, eosinophils, basophils, lymphocytes, and monocytes.…”

On the robustness of machine learning algorithms toward microfluidic distortions for cell classification via on-chip fluorescence microscopy

“…[3][4][5][6] These advances on microfluidics enable analysis of cells based not only on the traditional one-dimensional spectrometric signatures, as in classical flow cytometry, 7,8 but also on their individual spatial characteristics such as shape, texture, size, etc., accessible via fluorescence imaging. [9][10][11] This new approach for cell sorting has also benefited from recent advances in machine and deep learning. [12][13][14][15] Demonstrations of cell sorting in microscopy coupled with microfluidics and machine learning have been provided for several research and commercial imaging flow cytometry applications.…”

“…, accessible via fluorescence imaging. 9–11 This new approach for cell sorting has also benefited from recent advances in machine and deep learning. 12–15 Demonstrations of cell sorting in microscopy coupled with microfluidics and machine learning have been provided for several research and commercial imaging flow cytometry applications.…”