Deep learning algorithms have recently been developed that utilize patient anatomy and
raw imaging information to predict radiation dose, as a means to increase treatment …

Head and neck (HN) imaging is concerned with the evaluation of disorders affecting the
complex structures and spaces of the neck, paranasal sinuses, skull base, and the orbits …

Purpose To suggest an attention-aware, cycle-consistent generative adversarial network (A-
CycleGAN) enhanced with variational autoencoding (VAE) as a superior alternative to …

The purpose of the work is to develop a deep unsupervised learning strategy for cone-beam
CT (CBCT) to CT deformable image registration (DIR). This technique uses a deep …

Purpose This study suggests a lifelong learning‐based convolutional neural network (LL‐
CNN) algorithm as a superior alternative to single‐task learning approaches for automatic …

A deeply supervised attention-enabled boosted convolutional neural network (DAB-CNN) is
presented as a superior alternative to current state-of-the-art convolutional neural networks …

This study was to explore the effect of computed tomography (CT) images processed by
image edge detection technology based on the improved Canny algorithm in the diagnosis …

The goal of this study is to demonstrate the feasibility of a novel non-coplanar-arc
optimization algorithm (CyberArc). This method aims to reduce the delivery time of …

Purpose This study aims to reduce the delivery time of CyberKnife m6 treatments by
allowing for noncoplanar continuous arc delivery. To achieve this, a novel noncoplanar …

To improve the intelligence and automation for adapting to the development of prosumer
substations, this paper proposes an isolation switch opening and closing position …