Deep learning algorithms, in particular convolutional networks, have rapidly become a
methodology of choice for analyzing medical images. This paper reviews the major deep …

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 …

Deep learning (DL) based semantic segmentation methods have been providing state-of-the-
art performance in the last few years. More specifically, these techniques have been …

Deep learning (DL)-based semantic segmentation methods have been providing state-of-
the-art performance in the past few years. More specifically, these techniques have been …

Automated breast cancer multi-classification from histopathological images plays a key role
in computer-aided breast cancer diagnosis or prognosis. Breast cancer multi-classification is …

Deep learning has rapidly advanced in various fields within the past few years and has
recently gained particular attention in the radiology community. This article provides an …

Purpose To determine the feasibility of using a deep learning approach to detect cartilage
lesions (including cartilage softening, fibrillation, fissuring, focal defects, diffuse thinning due …

Deep learning algorithms produces state-of-the-art results for different machine learning and
computer vision tasks. To perform well on a given task, these algorithms require large …

Precise segmentation and anatomical identification of the vertebrae provides the basis for
automatic analysis of the spine, such as detection of vertebral compression fractures or other …

The identification of medical images is an essential task in computer-aided diagnosis,
medical image retrieval and mining. Medical image data mainly include electronic health …