AIMC Topic: Deep Learning

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Deep learning based HEp-2 image classification: A comprehensive review.

Medical image analysis
Classification of HEp-2 cell patterns plays a significant role in the indirect immunofluorescence test for identifying autoimmune diseases in the human body. Many automatic HEp-2 cell classification methods have been proposed in recent years, amongst...

Identification of glomerular lesions and intrinsic glomerular cell types in kidney diseases via deep learning.

The Journal of pathology
Identification of glomerular lesions and structures is a key point for pathological diagnosis, treatment instructions, and prognosis evaluation in kidney diseases. These time-consuming tasks require a more accurate and reproducible quantitative analy...

Use of deep learning to detect cardiomegaly on thoracic radiographs in dogs.

Veterinary journal (London, England : 1997)
The purpose of this study was to develop a computer-aided detection (CAD) device based on convolutional neural networks (CNNs) to detect cardiomegaly from plain radiographs in dogs. Right lateral chest radiographs (n = 1465) were retrospectively sele...

Deep learning based detection of intracranial aneurysms on digital subtraction angiography: A feasibility study.

The neuroradiology journal
BACKGROUND: Digital subtraction angiography is the gold standard for detecting and characterising aneurysms. Here, we assess the feasibility of commercial-grade deep learning software for the detection of intracranial aneurysms on whole-brain anterop...

Analysis of the role and robustness of artificial intelligence in commodity image recognition under deep learning neural network.

PloS one
In order to explore the application of the image recognition model based on multi-stage convolutional neural network (MS-CNN) in the deep learning neural network in the intelligent recognition of commodity images and the recognition performance of th...

Deep learning based spectral extrapolation for dual-source, dual-energy x-ray computed tomography.

Medical physics
PURPOSE: Data completion is commonly employed in dual-source, dual-energy computed tomography (CT) when physical or hardware constraints limit the field of view (FoV) covered by one of two imaging chains. Practically, dual-energy data completion is a...

Noise and spatial resolution properties of a commercially available deep learning-based CT reconstruction algorithm.

Medical physics
PURPOSE: To characterize the noise and spatial resolution properties of a commercially available deep learning-based computed tomography (CT) reconstruction algorithm.

Machine learning and AI-based approaches for bioactive ligand discovery and GPCR-ligand recognition.

Methods (San Diego, Calif.)
In the last decade, machine learning and artificial intelligence applications have received a significant boost in performance and attention in both academic research and industry. The success behind most of the recent state-of-the-art methods can be...

Fuzzy Inspired Deep Belief Network for the Traffic Flow Prediction in Intelligent Transportation System Using Flow Strength Indicators.

Big data
Intelligent transportation system (ITS) is an advance leading edge technology that aims to deliver innovative services to different modes of transport and traffic management. Traffic flow prediction (TFP) is one of the key macroscopic parameters of t...

Artificial Intelligence and Machine Learning in Arrhythmias and Cardiac Electrophysiology.

Circulation. Arrhythmia and electrophysiology
Artificial intelligence (AI) and machine learning (ML) in medicine are currently areas of intense exploration, showing potential to automate human tasks and even perform tasks beyond human capabilities. Literacy and understanding of AI/ML methods are...