Artificial Intelligence (AI) has emerged as a useful aid in numerous clinical applications for
diagnosis and treatment decisions. Deep neural networks have shown the same or better …

Supervised learning aims to build a function or model that seeks as many mappings as
possible between the training data and outputs, where each training data will predict as a …

Encoder-decoder networks are state-of-the-art approaches to biomedical image
segmentation, but have two problems: ie, the widely used pooling operations may discard …

The U-Net architecture, built upon the fully convolutional network, has proven to be effective
in biomedical image segmentation. However, U-Net applies skip connections to merge …

Abstract In recent years Deep Learning has brought about a breakthrough in Medical Image
Segmentation. In this regard, U-Net has been the most popular architecture in the medical …

An outbreak of a novel coronavirus disease (ie, COVID-19) has been recorded in Wuhan,
China since late December 2019, which subsequently became pandemic around the world …

Background Automated segmentation of anatomical structures is a crucial step in image
analysis. For lung segmentation in computed tomography, a variety of approaches exists …

Deep learning has revolutionized image processing and achieved the-state-of-art
performance in many medical image segmentation tasks. Many deep learning-based …

With the advent of advancements in deep learning approaches, such as deep convolution
neural network, residual neural network, adversarial network; U-Net architectures are most …

Purpose Accurate and timely organs‐at‐risk (OARs) segmentation is key to efficient and
high‐quality radiation therapy planning. The purpose of this work is to develop a deep …