AIMC Topic: Deep Learning

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Optimized deep neural network architecture for robust detection of epileptic seizures using EEG signals.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVE: Automatic detection of epileptic seizures based on deep learning methods received much attention last year. However, the potential of deep neural networks in seizure detection has not been fully exploited in terms of the optimal design of ...

Blood Sugar Level Indication Through Chewing and Swallowing from Acoustic MEMS Sensor and Deep Learning Algorithm for Diabetic Management.

Journal of medical systems
Diabetes, a metabolic disorder due to high blood glycemic index in the human body. The glycemic index varies in the human of improper diet and eating pattern such as junk foods, variation in the quantity of food, swallowing of food without chewing an...

A Survey of the Techniques for The Identification and Classification of Human Actions from Visual Data.

Sensors (Basel, Switzerland)
Recognition of human actions form videos has been an active area of research because it has applications in various domains. The results of work in this field are used in video surveillance, automatic video labeling and human-computer interaction, am...

An Interpretable and Expandable Deep Learning Diagnostic System for Multiple Ocular Diseases: Qualitative Study.

Journal of medical Internet research
BACKGROUND: Although artificial intelligence performs promisingly in medicine, few automatic disease diagnosis platforms can clearly explain why a specific medical decision is made.

Enhanced Point-of-Care Ultrasound Applications by Integrating Automated Feature-Learning Systems Using Deep Learning.

Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine
Recent applications of artificial intelligence (AI) and deep learning (DL) in health care include enhanced diagnostic imaging modalities to support clinical decisions and improve patients' outcomes. Focused on using automated DL-based systems to impr...

Simultaneous NODDI and GFA parameter map generation from subsampled q-space imaging using deep learning.

Magnetic resonance in medicine
PURPOSE: To develop a robust multidimensional deep-learning based method to simultaneously generate accurate neurite orientation dispersion and density imaging (NODDI) and generalized fractional anisotropy (GFA) parameter maps from undersampled q-spa...

ADA-Tucker: Compressing deep neural networks via adaptive dimension adjustment tucker decomposition.

Neural networks : the official journal of the International Neural Network Society
Despite recent success of deep learning models in numerous applications, their widespread use on mobile devices is seriously impeded by storage and computational requirements. In this paper, we propose a novel network compression method called Adapti...

How Cognitive Machines Can Augment Medical Imaging.

AJR. American journal of roentgenology
OBJECTIVE: Artificial intelligence (AI) neural networks rapidly convert disparate facts and data into highly predictive analytic models. Machine learning maps image-patient phenotype correlations opaque to standard statistics. Deep learning performs ...