We propose a new deep learning approach for medical imaging that copes with the problem
of a small training set, the main bottleneck of deep learning, and apply it for classification of …

Anemia affects about the 25% of the global population and can provoke severe diseases,
ranging from weakness and dizziness to pregnancy problems, arrhythmias and hearth …

Classification of the morphology of red blood cells (RBCs) plays an extremely important role
in evaluating the quality of long‐term stored blood, as RBC storage lesions such as …

Significance: Label-free quantitative phase imaging is a promising technique for the
automatic detection of abnormal red blood cells (RBCs) in real time. Although deep-learning …

Digital holography can provide quantitative phase images related to the morphology and
content of biological samples. To reconstruct an accurate phase image, several processes …

The optimal functionality of red blood cells is closely associated with the surrounding
environment. This study was undertaken to analyze the changes in membrane profile, mean …

We propose methods to quantitatively calculate the fluctuation rate of red blood cells with
nanometric axial and millisecond temporal sensitivity at the single-cell level by using time …

This study presents a novel approach to automatically perform instant phenotypic
assessment of red blood cell (RBC) storage lesion in phase images obtained by digital …

Digital holography can provide quantitative phase images that are related to the shape and
content of biological samples. In particular, high-resolution phase images contain more …

Cardiomyocytes derived from human pluripotent stem cells are a promising tool for disease
modeling, drug compound testing, and cardiac toxicity screening. Bio‐image segmentation …