Edge Computing (EC) is a new architecture that extends Cloud Computing (CC) services
closer to data sources. EC combined with Deep Learning (DL) is a promising technology …

Deep learning has been widely used for medical image segmentation and a large number of
papers has been presented recording the success of deep learning in the field. A …

The medical imaging literature has witnessed remarkable progress in high-performing
segmentation models based on convolutional neural networks. Despite the new …

Semi-supervised learning has attracted much attention in medical image segmentation due
to challenges in acquiring pixel-wise image annotations, which is a crucial step for building …

Training deep convolutional neural networks usually requires a large amount of labeled
data. However, it is expensive and time-consuming to annotate data for medical image …

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 …

Abstract Machine learning (ML) algorithms have made a tremendous impact in the field of
medical imaging. While medical imaging datasets have been growing in size, a challenge …

Training deep neural networks usually requires a large amount of labeled data to obtain
good performance. However, in medical image analysis, obtaining high-quality labels for the …

Segmentation is a key step for various medical image analysis tasks. Recently, deep neural
networks could provide promising solutions for automatic image segmentation. The network …

The scarcity of labeled data often limits the application of supervised deep learning
techniques for medical image segmentation. This has motivated the development of semi …