The current development in deep learning is witnessing an exponential transition into
automation applications. This automation transition can provide a promising framework for …

Substantial progress has been made towards implementing automated radiology reporting
models based on deep learning (DL). This is due to the introduction of large medical …

New technologies are transforming medicine, and this revolution starts with data. Health
data, clinical images, genome sequences, data on prescribed therapies and results …

Two-dimensional arrays of bi-component structures made of cobalt and permalloy elliptical
dots with thickness of 25 nm, length 1 mm and width of 225 nm, have been prepared by a …

The extensive growth of data in the health domain has increased the utility of Deep Learning
in health. Deep learning is a highly advanced successor of artificial neural networks, having …

Traditional bulk sequencing methods are limited to measuring the average signal in a group
of cells, potentially masking heterogeneity, and rare populations. The single-cell resolution …

Depression is considered by WHO as the main contributor to global disability and it poses
dangerous threats to approximately all aspects of human life, in particular public and private …

The advancements in genomics and biomedical technologies have generated vast amounts
of biological and physiological data, which present opportunities for understanding human …

Purpose: This concise review aims to explore the potential for the clinical implementation of
artificial intelligence (AI) strategies for detecting glaucoma and monitoring glaucoma …

Biomolecular simulations are intrinsically high dimensional and generate noisy data sets of
ever-increasing size. Extracting important features from the data is crucial for understanding …