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

Deep learning-based image segmentation is by now firmly established as a robust tool in
image segmentation. It has been widely used to separate homogeneous areas as the first …

Convolutional neural networks, are one of the most representative deep learning models.
CNNs were extensively used in many aspects of medical image analysis, allowing for great …

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 …

A common shortfall of supervised deep learning for medical imaging is the lack of labeled
data, which is often expensive and time consuming to collect. This article presents a new …

The artificial neural network (ANN), one of the machine learning (ML) algorithms, inspired by
the human brain system, was developed by connecting layers with artificial neurons …

Lung cancer has one of the highest mortalities of all cancers. According to the National Lung
Screening Trial, patients who underwent low-dose computed tomography (CT) scanning …

Multimodality medical imaging techniques have been increasingly applied in clinical
practice and research studies. Corresponding multimodal image analysis and ensemble …

Deep learning has been widely used in medical image segmentation and other aspects.
However, the performance of existing medical image segmentation models has been limited …

Glaucoma is one of the leading causes of irreversible vision loss. Many approaches have
recently been proposed for automatic glaucoma detection based on fundus images …