Deep learning algorithms have recently been applied for image detection and classication,
lately with good results in the medicine such as medical image analysis. This paper aims to …

Background: The purpose of this study was to conduct a systematic review for understanding
the availability and limitations of artificial intelligence (AI) approaches that could …

Due to the lack of a standardized 3D cephalometric analysis methodology, 2D
cephalograms synthesized from 3D cone-beam computed tomography (CBCT) volumes are …

Convolutional neural networks (CNN) are widely used in the fields of object detection and
image segmentation thanks to their high performance. The choice of architecture and …

Abstract Multiple Instance Learning (MIL) is a weakly supervised paradigm that has been
successfully applied to many different scientific areas and is particularly well suited to …

Aim To develop a unified deep-learning-based method for automated intracerebral
haemorrhage (ICH) segmentation on computed tomography (CT) images with different layer …

The automatic Brain CT reports generation can improve the efficiency and accuracy of
diagnosing cranial diseases. However, current methods are limited by 1) coarse-grained …

Intracranial hemorrhage is a disease with a greater mortality rate. The only way to provide a
definitive diagnosis of intra cranial hemorrhage is through neuroimaging. Deep learning …

OBJECTIVE. Machine learning has potential to play a key role across a variety of medical
imaging applications. This review seeks to elucidate the ways in which machine learning …

This paper proposes a randomized optimization framework for constrained signal
reconstruction, where the word" constrained" implies that data-fidelity is imposed as a hard …