AIMC Topic: Humans

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A Systematic Review of Health Dialog Systems.

Methods of information in medicine
BACKGROUND:  Health dialog systems have seen increased adoption by patients, hospitals, and universities due to the confluence of advancements in machine learning and the ubiquity of high-performance hardware that supports real-time speech recognitio...

Standard SPECT myocardial perfusion estimation from half-time acquisitions using deep convolutional residual neural networks.

Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology
INTRODUCTION: The purpose of this work was to assess the feasibility of acquisition time reduction in MPI-SPECT imaging using deep leering techniques through two main approaches, namely reduction of the acquisition time per projection and reduction o...

Exploration of critical care data by using unsupervised machine learning.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Identification of subgroups may be useful to understand the clinical characteristics of ICU patients. The purposes of this study were to apply an unsupervised machine learning method to ICU patient data to discover subgroups...

Estimation of age in unidentified patients via chest radiography using convolutional neural network regression.

Emergency radiology
PURPOSE: Patient age has important clinical utility for refining a differential diagnosis in radiology. Here, we evaluate the potential for convolutional neural network models to predict patient age based on anterior-posterior chest radiographs for i...

Analysis of gene expression profiles of lung cancer subtypes with machine learning algorithms.

Biochimica et biophysica acta. Molecular basis of disease
Lung cancer is one of the most common cancer types worldwide and causes more than one million deaths annually. Lung adenocarcinoma (AC) and lung squamous cell cancer (SCC) are two major lung cancer subtypes and have different characteristics in sever...

Deep neural network inspired by iterative shrinkage-thresholding algorithm with data consistency (NISTAD) for fast Undersampled MRI reconstruction.

Magnetic resonance imaging
With the aim of developing a fast algorithm for high-quality MRI reconstruction from undersampled k-space data, we propose a novel deep neural Network, which is inspired by Iterative Shrinkage Thresholding Algorithm with Data consistency (NISTAD). NI...

Automated detection of COVID-19 cases using deep neural networks with X-ray images.

Computers in biology and medicine
The novel coronavirus 2019 (COVID-2019), which first appeared in Wuhan city of China in December 2019, spread rapidly around the world and became a pandemic. It has caused a devastating effect on both daily lives, public health, and the global econom...

Cardiac VFM visualization and analysis based on YOLO deep learning model and modified 2D continuity equation.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
In order to realize the visual analysis of cardiac fluid motion, according to the characteristics of cardiac flow field ultrasound image, a method for the cardiac Vector Flow Mapping (VFM) analysis and evaluation based on the You-Only-Look-Once (YOLO...

Nonequilibrium Statistical Mechanics of Continuous Attractors.

Neural computation
Continuous attractors have been used to understand recent neuroscience experiments where persistent activity patterns encode internal representations of external attributes like head direction or spatial location. However, the conditions under which ...