The label-free single cell analysis by machine and Deep Learning, in combination with
digital holography in transmission microscope configuration, is becoming a powerful …

Quantitative phase imaging with off-axis digital holography in a microscopic configuration
provides insight into the cells' intracellular content and morphology. This imaging is …

Nowadays, label-free imaging flow cytometry at the single-cell level is considered the
stepforward lab-on-a-chip technology to address challenges in clinical diagnostics, biology …

Digital holographic microscopy (DHM) as a label-free quantitative imaging tool has been
widely used to investigate the morphology of living cells dynamically. In the off-axis DHM …

Background Image-based cell analytic methodologies offer a relatively simple and
economical way to analyze and understand cell heterogeneities and developments. Owing …

We demonstrate a successful deep learning strategy for cell identification and disease
diagnosis using spatio-temporal cell information recorded by a digital holographic …

This paper presents our method, which simultaneously combines automatic imaging,
identification, and counting with the acquisition of morphological information for at least …

Deep artificial neural network learning is an emerging tool in image analysis. We
demonstrate its potential in the field of digital holographic microscopy by addressing the …

In the last few years, classification of cells by machine learning has become frequently used
in biology. However, most of the approaches are based on morphometric (MO) features …

Digital holographic microscopy enables the measurement of the quantitative light field
information and the visualization of transparent specimens. It can be implemented for …