Recent advances in deep learning have led to a promising performance in many medical
image analysis tasks. As the most commonly performed radiological exam, chest …

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 …

This paper aims to propose a high-speed and accurate fully-automated method to detect
COVID-19 from the patient's chest CT scan images. We introduce a new dataset that …

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 …

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 …

As the most common examination tool in medical practice, chest radiography has important
clinical value in the diagnosis of disease. Thus, the automatic detection of chest disease …

Despite the tremendous success, deep neural networks are exposed to serious IP
infringement risks. Given a target deep model, if the attacker knows its full information, it can …

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 …

Breast cancer is the second leading cause of cancer death among women worldwide.
Nevertheless, it is also one of the most treatable malignances if detected early. Screening for …