The goals of this review paper on deep learning (DL) in medical imaging and radiation
therapy are to (a) summarize what has been achieved to date;(b) identify common and …

Multimodal medical image fusion consists in combining two or more images of the same or
different modalities aiming to improve the image content, and preserve information. The …

Deep learning is the state-of-the-art machine learning approach. The success of deep
learning in many pattern recognition applications has brought excitement and high …

The accelerating complexity and variety of medical imaging devices and methods have
outpaced the ability to evaluate and optimize their design and clinical use. This is a …

To realize the full potential of deep learning for medical imaging, large annotated datasets
are required for training. Such datasets are difficult to acquire due to privacy issues, lack of …

Background and objective: Chronic kidney disease is a worldwide health issue which
includes not only kidney failure but also complications of reduced kidney functionality. Cyst …

CT screening has been proven to be effective for diagnosing lung cancer at its early
manifestation in the form of pulmonary nodules, thus decreasing the mortality. However, the …

Medical images are a rich source of invaluable necessary information used by clinicians.
Recent technologies have introduced many advancements for exploiting the most of this …

Deep two-dimensional (2-D) convolutional neural networks (CNNs) have been remarkably
successful in producing record-breaking results in a variety of computer vision tasks. It is …

Abstract Background and Objective: In medical imaging, the scarcity of labeled lesion data
has hindered the application of many deep learning algorithms. To overcome this problem …