Due to the proliferation of biomedical imaging modalities, such as Photoacoustic
Tomography, Computed Tomography (CT), Optical Microscopy and Tomography, etc …

What has happened in machine learning lately, and what does it mean for the future of
medical image analysis? Machine learning has witnessed a tremendous amount of attention …

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 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 …

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

In this review the application of deep learning for medical diagnosis is addressed. A
thorough analysis of various scientific articles in the domain of deep neural networks …

Recently, deep learning has unlocked unprecedented success in various domains,
especially using images, text, and speech. However, deep learning is only beneficial if the …

In the last few years, there has been an exponential increase in the usage of the
autonomous vehicles across the globe. It is due to an exponential increase in the popularity …

Medical image segmentation is an important tool for current clinical applications. It is the
backbone of numerous clinical diagnosis methods, oncological treatments and computer …

Purpose Delineation of the clinical target volume (CTV) and organs at risk (OAR s) is very
important for radiotherapy but is time‐consuming and prone to inter‐observer variation …