Artificial intelligence is a hot topic in medical imaging. The development of deep learning
methods and in particular the use of convolutional neural networks (CNNs), have led to …

Deep learning is a genre of machine learning that allows computational models to learn
representations of data with multiple levels of abstraction using numerous processing layers …

Relevance and penetration of machine learning in clinical practice is a recent phenomenon
with multiple applications being currently under development. Deep learning—and …

Half a century ago, the term “computer-aided diagnosis”(CAD) was introduced in the
scientific literature. Pulmonary imaging, with chest radiography and computed tomography …

In this review article, the current and future impact of artificial intelligence (AI) technologies
on diagnostic imaging is discussed, with a focus on cardio-thoracic applications. The …

Deep learning (DL) has shown rapid advancement and considerable promise when applied
to the automatic detection of diseases using CXRs. This is important given the widespread …

Background Deep learning (DL) based solutions have been proposed for interpretation of
several imaging modalities including radiography, CT, and MR. For chest radiographs, DL …

Although there is no solid agreement for artificial intelligence (AI), it refers to a computer
system with intelligence similar to that of humans. Deep learning appeared in 2006, and …

Radiological imaging diagnosis plays important roles in clinical patient management. Deep
learning with convolutional neural networks (CNNs) is recently gaining wide attention for its …

Purpose To assess the ability of convolutional neural networks (CNNs) to enable high-
performance automated binary classification of chest radiographs. Materials and Methods In …