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 …

Digital holographic microscopy is a thriving imaging modality that attracted considerable
research interest in quantitative biological cell imaging due to its ability to not only create …

We present cytometric classification of live healthy and cancerous cells by using the spatial
morphological and textural information found in the label‐free quantitative phase images of …

We present a deep-learning approach for solving the problem of 2π phase ambiguities in
two-dimensional quantitative phase maps of biological cells, using a multi-layer encoder …

Accepted: 22 September 2019 The convolutional neural network (CNN) and other neural
networks (NNs) provide promising tools for robotized characterization of tumor cells …

The refractive index in the interior of single cells affects the evanescent field depth in
quantitative studies using total internal reflection (TIR) fluorescence, but often that index is …

Giant unilamellar vesicles (GUVs) are cell-sized aqueous compartments enclosed by a
phospholipid bilayer. Due to their cell-mimicking properties, GUVs have become a …

Arthritis is a leading cause of disability in adults, which can be intensely incapacitating. The
location and intensity of the pain is both subjective and challenging to manage …

Cancer cell transformation is often accompanied by a modification of their viscoelastic
properties. When capturing the stress-to-strain response of primary chronic myelogenous …

We used a 1D wavelet transform modulus maxima (WTMM) method to analyze the temporal
fluctuations of breast skin temperature recorded with an infrared (IR) camera from a panel of …