AIMC Topic: Image Interpretation, Computer-Assisted

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Automated System for Referral of Cotton-Wool Spots.

Current diabetes reviews
BACKGROUND: Cotton-wool spots also referred as soft exudates are the early signs of complications in the eye fundus of the patients suffering from diabetic retinopathy. Early detection of exudates helps in the diagnosis of the disease and provides be...

Linear-regression convolutional neural network for fully automated coronary lumen segmentation in intravascular optical coherence tomography.

Journal of biomedical optics
Intravascular optical coherence tomography (OCT) is an optical imaging modality commonly used in the assessment of coronary artery diseases during percutaneous coronary intervention. Manual segmentation to assess luminal stenosis from OCT pullback sc...

When Machines Think: Radiology's Next Frontier.

Radiology
Artificial intelligence (AI), machine learning, and deep learning are terms now seen frequently, all of which refer to computer algorithms that change as they are exposed to more data. Many of these algorithms are surprisingly good at recognizing obj...

Machine Learning Approaches in Cardiovascular Imaging.

Circulation. Cardiovascular imaging
Cardiovascular imaging technologies continue to increase in their capacity to capture and store large quantities of data. Modern computational methods, developed in the field of machine learning, offer new approaches to leveraging the growing volume ...

[Computational neuroanatomy and microstructure imaging using magnetic resonance imaging].

Der Nervenarzt
BACKGROUND: Current computational neuroanatomy focuses on morphological measurements of the brain using standard magnetic resonance imaging (MRI) techniques. In comparison quantitative MRI (qMRI) typically provides a better tissue contrast and also g...

Optimizing a machine learning based glioma grading system using multi-parametric MRI histogram and texture features.

Oncotarget
Current machine learning techniques provide the opportunity to develop noninvasive and automated glioma grading tools, by utilizing quantitative parameters derived from multi-modal magnetic resonance imaging (MRI) data. However, the efficacies of dif...

A cross-sectional evaluation of meditation experience on electroencephalography data by artificial neural network and support vector machine classifiers.

Medicine
To quantitate the meditation experience is a subjective and complex issue because it is confounded by many factors such as emotional state, method of meditation, and personal physical condition. In this study, we propose a strategy with a cross-secti...