AIMC Topic: Magnetic Resonance Imaging

Clear Filters Showing 6441 to 6450 of 6780 articles

A Novel Deep Learning Based Computer-Aided Diagnosis System Improves the Accuracy and Efficiency of Radiologists in Reading Biparametric Magnetic Resonance Images of the Prostate: Results of a Multireader, Multicase Study.

Investigative radiology
OBJECTIVE: The aim of this study was to evaluate the effect of a deep learning based computer-aided diagnosis (DL-CAD) system on radiologists' interpretation accuracy and efficiency in reading biparametric prostate magnetic resonance imaging scans.

Advances of Artificial Intelligence Application in Medical Imaging of Ovarian Cancers.

Chinese medical sciences journal = Chung-kuo i hsueh k'o hsueh tsa chih
Ovarian cancer is one of the three most common gynecological cancers in the world, and is regarded as a priority in terms of women's cancer. In the past few years, many researchers have attempted to develop and apply artificial intelligence (AI) tech...

Diagnosis of focal liver lesions with deep learning-based multi-channel analysis of hepatocyte-specific contrast-enhanced magnetic resonance imaging.

World journal of gastroenterology
BACKGROUND: The nature of input data is an essential factor when training neural networks. Research concerning magnetic resonance imaging (MRI)-based diagnosis of liver tumors using deep learning has been rapidly advancing. Still, evidence to support...

Improved FCM algorithm for fisheye image cluster analysis for tree height calculation.

Mathematical biosciences and engineering : MBE
The height of standing trees is an important index in forestry research. This index is not only hard to measure directly but also the environmental factors increase the measurement difficulty. Therefore, the measurement of the height of standing tree...

Diverse Deep Neural Networks All Predict Human Inferior Temporal Cortex Well, After Training and Fitting.

Journal of cognitive neuroscience
Deep neural networks (DNNs) trained on object recognition provide the best current models of high-level visual cortex. What remains unclear is how strongly experimental choices, such as network architecture, training, and fitting to brain data, contr...

Semantic image segmentation of brain MRI with deep learning.

Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences
OBJECTIVES: Previous studies on brain MRI image segmentation, such as threshold method, boundary detection method, and region method did not achieve good performance in complex scenes. Based on the deep learning segmentation technology, this study co...

Radiomics and machine learning applications in rectal cancer: Current update and future perspectives.

World journal of gastroenterology
The high incidence of rectal cancer in both sexes makes it one of the most common tumors, with significant morbidity and mortality rates. To define the best treatment option and optimize patient outcome, several rectal cancer biological variables mus...

A 3D multiscale view convolutional neural network with attention for mental disease diagnosis on MRI images.

Mathematical biosciences and engineering : MBE
Computer Assisted Diagnosis (CAD) based on brain Magnetic Resonance Imaging (MRI) is a popular research field for the computer science and medical engineering. Traditional machine learning and deep learning methods were employed in the classification...

Development and Evaluation of Deep Learning-based Automated Segmentation of Pituitary Adenoma in Clinical Task.

The Journal of clinical endocrinology and metabolism
CONTEXT: The resection plan of pituitary adenoma (PA) needs preoperative observation of the sellar region. Radiomics prediction requires high-quality segmentations. Manual delineation is time-consuming and subject to rater variability.

A Bayesian optimization approach for rapidly mapping residual network function in stroke.

Brain : a journal of neurology
Post-stroke cognitive and linguistic impairments are debilitating conditions, with limited therapeutic options. Domain-general brain networks play an important role in stroke recovery and characterizing their residual function with functional MRI has...