PURPOSE: To determine whether deep learning-based algorithms applied to breast MR images can aid in the prediction of occult invasive disease following the diagnosis of ductal carcinoma in situ (DCIS) by core needle biopsy.
Super-resolution, which is one of the trend issues of recent times, increases the resolution of the images to higher levels. Increasing the resolution of a vital image in terms of the information it contains such as brain magnetic resonance image (MR...
Hippocampal volumetry is a critical biomarker of aging and dementia, and it is widely used as a predictor of cognitive performance; however, automated hippocampal segmentation methods are limited because the algorithms are (a) not publicly available,...
PURPOSE: Early detection of carotid atherosclerosis on the vessel wall (VW) magnetic resonance imaging (MRI) (VW-MRI) images can prevent the progression of cardiovascular disease. However, the manual inspection process of the VW-MRI images is cumbers...
Several studies have already assessed brain network variations in multiple sclerosis (MS) patients and healthy controls (HCs). The underlying neural system's functioning is apparently too complicated, however. Therefore, the neural time series' analy...
There is significant interest in the development and application of deep neural networks (DNNs) to neuroimaging data. A growing literature suggests that DNNs outperform their classical counterparts in a variety of neuroimaging applications, yet there...
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Oct 10, 2019
The brain disorders may cause loss of some critical functions such as thinking, speech, and movement. So, the early detection of brain diseases may help to get the timely best treatment. One of the conventional methods used to diagnose these disorder...
Utilizing the idea of long-term cumulative return, reinforcement learning (RL) has shown remarkable performance in various fields. We follow the formulation of landmark localization in 3D medical images as an RL problem. Whereas value-based methods h...
The use of Computed Tomography (CT) imaging for patients with stroke symptoms is an essential step for triaging and diagnosis in many hospitals. However, the subtle expression of ischemia in acute CT images has made it hard for automated methods to e...
PURPOSE: To study the influence of gradient echo-based contrasts as input channels to a 3D patch-based neural network trained for synthetic CT (sCT) generation in canine and human populations.
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.