AIMC Topic: Artifacts

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Rethinking Skin Lesion Segmentation in a Convolutional Classifier.

Journal of digital imaging
Melanoma is a fatal form of skin cancer when left undiagnosed. Computer-aided diagnosis systems powered by convolutional neural networks (CNNs) can improve diagnostic accuracy and save lives. CNNs have been successfully used in both skin lesion segme...

Detection of Atrial Fibrillation from RR Intervals and PQRST Morphology using a Neural Network Ensemble.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Early detection and discrimination of cardiac arrhythmia, atrial fibrillation (AF) in particular, is essential for timely intervention to improve patient outcomes. In this work, an algorithm was developed to classify ECG records as normal, AF, other ...

Photoacoustic Source Detection and Reflection Artifact Removal Enabled by Deep Learning.

IEEE transactions on medical imaging
Interventional applications of photoacoustic imaging typically require visualization of point-like targets, such as the small, circular, cross-sectional tips of needles, catheters, or brachytherapy seeds. When these point-like targets are imaged in t...

Convolutional Neural Network Based Metal Artifact Reduction in X-Ray Computed Tomography.

IEEE transactions on medical imaging
In the presence of metal implants, metal artifacts are introduced to x-ray computed tomography CT images. Although a large number of metal artifact reduction (MAR) methods have been proposed in the past decades, MAR is still one of the major problems...

Image reconstruction by domain-transform manifold learning.

Nature
Image reconstruction is essential for imaging applications across the physical and life sciences, including optical and radar systems, magnetic resonance imaging, X-ray computed tomography, positron emission tomography, ultrasound imaging and radio a...

Robust Support Matrix Machine for Single Trial EEG Classification.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Electroencephalogram (EEG) signals are of complex structure and can be naturally represented as matrices. Classification is one of the most important steps for EEG signal processing. Newly developed classifiers can handle these matrix-form data by ad...

ABroAD: A Machine Learning Based Approach to Detect Broadband NIRS Artefacts.

Advances in experimental medicine and biology
Artefacts are a common and unwanted aspect of any measurement process, especially in a clinical environment, with multiple causes such as environmental changes or motion. In near-infrared spectroscopy (NIRS), there are several existing methods that c...

[Clinical analysis of spectrum CT imaging reducing metal artifacts of oral and maxillofacial region].

Shanghai kou qiang yi xue = Shanghai journal of stomatology
PURPOSE: To assess the capability of monochromatic energy images of gemstone spectral imaging(GSI) by using spectral CT in reducing metal artifacts of oral and maxillofacial region.

A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction.

Medical physics
PURPOSE: Due to the potential risk of inducing cancer, radiation exposure by X-ray CT devices should be reduced for routine patient scanning. However, in low-dose X-ray CT, severe artifacts typically occur due to photon starvation, beam hardening, an...

Automated EEG artifact elimination by applying machine learning algorithms to ICA-based features.

Journal of neural engineering
OBJECTIVE: Biological and non-biological artifacts cause severe problems when dealing with electroencephalogram (EEG) recordings. Independent component analysis (ICA) is a widely used method for eliminating various artifacts from recordings. However,...