Since its renaissance, deep learning has been widely used in various medical imaging tasks
and has achieved remarkable success in many medical imaging applications, thereby …

The medical imaging literature has witnessed remarkable progress in high-performing
segmentation models based on convolutional neural networks. Despite the new …

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 …

Traditional neuroimage analysis pipelines involve computationally intensive, time-
consuming optimization steps, and thus, do not scale well to large cohort studies with …

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 …

Fully convolutional deep neural networks carry out excellent potential for fast and accurate
image segmentation. One of the main challenges in training these networks is data …

Deep learning networks have recently been shown to outperform other segmentation
methods on various public, medical-image challenge datasets, particularly on metrics …

Deep learning is a branch of artificial intelligence where networks of simple interconnected
units are used to extract patterns from data in order to solve complex problems. Deep …

Abstract The advent of Deep Learning (DL) is poised to dramatically change the delivery of
healthcare in the near future. Not only has DL profoundly affected the healthcare industry it …

In recent years, deep convolutional neural networks (CNNs) have shown record-shattering
performance in a variety of computer vision problems, such as visual object recognition …