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 emerged as a leading machine learning tool in object detection and has
attracted attention with its achievements in progressing medical image analysis …

Historically, medical imaging has been a qualitative or semi-quantitative modality. It is
difficult to quantify what can be seen in an image, and to turn it into valuable predictive …

Computer-aided diagnosis (CAD) has become one of the major research subjects in
medical imaging and diagnostic radiology. In this article, the motivation and philosophy for …

With its origins in the mid-to late-1900s, today, artificial intelligence (AI) is used in a wide
range of medical fields for varying purposes. This review first covers the early work …

The traditional chest radiograph is still ubiquitous in clinical practice, and will likely remain
so for quite some time. Yet, its interpretation is notoriously difficult. This explains the …

We have developed a double-matching method and an artificial visual neural network
technique for lung nodule detection. This neural network technique is generally applicable …

Diagnosis of neurological diseases is a growing concern and one of the most difficult
challenges for modern medicine. According to the World Health Organisation's recent report …

Computer-aided diagnosis (CAD) has become one of the major research subjects in
medical imaging and diagnostic radiology. The basic concept of CAD is to provide a …

We have developed several training methods in conjunction with a convolution neural
network for general medical image pattern recognition. An unconventional method of using …