AIMC Topic: Magnetic Resonance Imaging

Clear Filters Showing 4601 to 4610 of 6780 articles

Deep Learning to Distinguish Benign from Malignant Renal Lesions Based on Routine MR Imaging.

Clinical cancer research : an official journal of the American Association for Cancer Research
PURPOSE: With increasing incidence of renal mass, it is important to make a pretreatment differentiation between benign renal mass and malignant tumor. We aimed to develop a deep learning model that distinguishes benign renal tumors from renal cell c...

Convolutional neural network-automated hepatobiliary phase adequacy evaluation may optimize examination time.

European journal of radiology
PURPOSE: To develop and evaluate the performance of a fully-automated convolutional neural network (CNN)-based algorithm to evaluate hepatobiliary phase (HBP) adequacy of gadoxetate disodium (EOB)-enhanced MRI. Secondarily, we explored the potential ...

ADHD classification by dual subspace learning using resting-state functional connectivity.

Artificial intelligence in medicine
As one of the most common neurobehavioral diseases in school-age children, Attention Deficit Hyperactivity Disorder (ADHD) has been increasingly studied in recent years. But it is still a challenge problem to accurately identify ADHD patients from he...

Semi-supervised learning for automatic segmentation of the knee from MRI with convolutional neural networks.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Segmentation is a crucial step in multiple biomechanics and orthopedics applications. The time-intensiveness and expertise requirements of medical image segmentation present a significant bottleneck for corresponding workflo...

Learning a cortical parcellation of the brain robust to the MRI segmentation with convolutional neural networks.

Medical image analysis
The parcellation of the human cortex into meaningful anatomical units is a common step of various neuroimaging studies. There have been multiple successful efforts to process magnetic resonance (MR) brain images automatically and identify specific an...

Improving cardiac MRI convolutional neural network segmentation on small training datasets and dataset shift: A continuous kernel cut approach.

Medical image analysis
Cardiac magnetic resonance imaging (MRI) provides a wealth of imaging biomarkers for cardiovascular disease care and segmentation of cardiac structures is required as a first step in enumerating these biomarkers. Deep convolutional neural networks (C...

Object parsing in the left lateral occipitotemporal cortex: Whole shape, part shape, and graspability.

Neuropsychologia
Small and manipulable objects (tools) preferentially evoke a network of brain regions relative to other objects, including the lateral occipitotemporal cortex (LOTC), which is assumed to process tool shape information. Given the correlation between v...

A two-step automated quality assessment for liver MR images based on convolutional neural network.

European journal of radiology
PURPOSE: To propose an automatic approach based on a convolutional neural network (CNN) to evaluate the quality of T2-weighted liver magnetic resonance (MR) images as nondiagnostic (ND) or diagnostic (D).

Artificial intelligence in multiparametric prostate cancer imaging with focus on deep-learning methods.

Computer methods and programs in biomedicine
Prostate cancer represents today the most typical example of a pathology whose diagnosis requires multiparametric imaging, a strategy where multiple imaging techniques are combined to reach an acceptable diagnostic performance. However, the reviewing...

Gait characteristics and clinical relevance of hereditary spinocerebellar ataxia on deep learning.

Artificial intelligence in medicine
BACKGROUND: Deep learning has always been at the forefront of scientific research. It has also been applied to medical research. Hereditary spinocerebellar ataxia (SCA) is characterized by gait abnormalities and is usually evaluated semi-quantitative...