Recent advances in ultra-high-throughput microscopy have enabled a new generation of
cell classification methodologies using image-based cell phenotypes alone. In contrast to …

Deep learning-based systems can achieve a diagnostic performance comparable to
physicians in a variety of medical use cases including the diagnosis of diabetic retinopathy …

Imaging flow cytometry (IFC) has become a powerful tool for diverse biomedical applications
by virtue of its ability to image single cells in a high-throughput manner. However, there …

Traditional methods for cell cycle stage classification rely heavily on fluorescence
microscopy to monitor nuclear dynamics. These methods inevitably face the typical …

Combining microfluidics technology with machine learning represents an innovative
approach to conduct massive quantitative cell behavior study and implement smart decision …

Abstract Purpose of Review We critically evaluate the future potential of machine learning
(ML), deep learning (DL), and artificial intelligence (AI) in precision medicine. The goal of …

Imaging flow cytometry has been widely adopted in numerous applications such as optical
sensing, environmental monitoring, clinical diagnostics, and precision agriculture. The …

Deep learning is a significant step forward for developing autonomous tasks. One of its
branches, computer vision, allows image recognition with high accuracy thanks to the use of …

Circulating tumour cells (CTCs) found in the blood of cancer patients are a promising
biomarker in precision medicine. However, their use is currently hindered by their low …

Phenotypic image analysis is the task of recognizing variations in cell properties using
microscopic image data. These variations, produced through a complex web of interactions …