AIMC Topic: Diagnostic Imaging

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Health Evaluation and Fault Diagnosis of Medical Imaging Equipment Based on Neural Network Algorithm.

Computational intelligence and neuroscience
In recent years, high-precision medical equipment, especially large-scale medical imaging equipment, is usually composed of circuit, water, light, and other structures. Its structure is cumbersome and complex, so it is difficult to detect and diagnos...

Notable Papers and New Directions in Sensors, Signals, and Imaging Informatics.

Yearbook of medical informatics
OBJECTIVE: To identify and highlight research papers representing noteworthy developments in signals, sensors, and imaging informatics in 2020.

Foundational Considerations for Artificial Intelligence Using Ophthalmic Images.

Ophthalmology
IMPORTANCE: The development of artificial intelligence (AI) and other machine diagnostic systems, also known as software as a medical device, and its recent introduction into clinical practice requires a deeply rooted foundation in bioethics for cons...

Assessing the Impact of Deep Neural Network-Based Image Denoising on Binary Signal Detection Tasks.

IEEE transactions on medical imaging
A variety of deep neural network (DNN)-based image denoising methods have been proposed for use with medical images. Traditional measures of image quality (IQ) have been employed to optimize and evaluate these methods. However, the objective evaluati...

Electromechanical Wave Imaging With Machine Learning for Automated Isochrone Generation.

IEEE transactions on medical imaging
Standard Electromechanical Wave Imaging isochrone generation relies on manual selection of zero-crossing (ZC) locations on incremental strain curves for a number of pixels in the segmented myocardium for each echocardiographic view and patient. When ...

Deep Learning-Based High-Frequency Ultrasound Skin Image Classification with Multicriteria Model Evaluation.

Sensors (Basel, Switzerland)
This study presents the first application of convolutional neural networks to high-frequency ultrasound skin image classification. This type of imaging opens up new opportunities in dermatology, showing inflammatory diseases such as atopic dermatitis...

A proof of concept study for machine learning application to stenosis detection.

Medical & biological engineering & computing
This proof of concept (PoC) assesses the ability of machine learning (ML) classifiers to predict the presence of a stenosis in a three vessel arterial system consisting of the abdominal aorta bifurcating into the two common iliacs. A virtual patient ...

Artificial intelligence-based hybrid deep learning models for image classification: The first narrative review.

Computers in biology and medicine
BACKGROUND: Artificial intelligence (AI) has served humanity in many applications since its inception. Currently, it dominates the imaging field-in particular, image classification. The task of image classification became much easier with machine lea...

Artificial intelligence: The opinions of radiographers and radiation therapists in Ireland.

Radiography (London, England : 1995)
INTRODUCTION: Implementation of Artificial Intelligence (AI) into medical imaging is much debated. Diagnostic Radiographers (DRs) and Radiation Therapists (RTTs) are at the forefront of this technological leap, thus an understanding of their views, i...