AIMC Topic: Computer Systems
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Deep Learning for Low-Dose CT Denoising Using Perceptual Loss and Edge Detection Layer.
Journal of digital imaging
Apr 1, 2020
Low-dose CT denoising is a challenging task that has been studied by many researchers. Some studies have used deep neural networks to improve the quality of low-dose CT images and achieved fruitful results. In this paper, we propose a deep neural net...
Real-time Detection of Aortic Valve in Echocardiography using Convolutional Neural Networks.
Current medical imaging
Jan 1, 2020
BACKGROUND: Valvular heart disease is a serious disease leading to mortality and increasing medical care cost. The aortic valve is the most common valve affected by this disease. Doctors rely on echocardiogram for diagnosing and evaluating valvular h...
Deep into Patient care: An automated deep learning approach for reshaping patient care in clinical setting.
Computer methods and programs in biomedicine
Jan 1, 2019
A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time Series.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Apr 1, 2018
Sleep stage classification constitutes an important preliminary exam in the diagnosis of sleep disorders. It is traditionally performed by a sleep expert who assigns to each 30 s of the signal of a sleep stage, based on the visual inspection of signa...
Real-time cerebellar neuroprosthetic system based on a spiking neural network model of motor learning.
Journal of neural engineering
Feb 1, 2018
OBJECTIVE: Damage to the brain, as a result of various medical conditions, impacts the everyday life of patients and there is still no complete cure to neurological disorders. Neuroprostheses that can functionally replace the damaged neural circuit h...
Selection of Semantic Relevant Healthcare Services Subsets.
Studies in health technology and informatics
Jan 1, 2018
We describe an approach to select semantically coherent specialty subsets based on the historical use of terminology by different service areas. Our approach uses rule-based and machine learning techniques to obtain a reduced set of 29 specialties.
DeepPhe: A Natural Language Processing System for Extracting Cancer Phenotypes from Clinical Records.
Cancer research
Nov 1, 2017
Precise phenotype information is needed to understand the effects of genetic and epigenetic changes on tumor behavior and responsiveness. Extraction and representation of cancer phenotypes is currently mostly performed manually, making it difficult t...
CD-KES: An Ontology Based Knowledge Education System for Patients with Chronic Diseases and Its Constructing Approach.
Studies in health technology and informatics
Jan 1, 2017
Patients' participation plays a crucial role in the management of chronic diseases. Educating patients about their diseases allows patients to self-regulate their daily health conditions more reasonably and effectively. This study focuses on an infor...
Development of a Service-Oriented Sharable Clinical Decision Support System Based on Ontology for Chronic Disease.
Studies in health technology and informatics
Jan 1, 2017
Clinical decision support systems (CDSSs) have been proved as an efficient way to improve health care quality. However, the inflexibility in integrating multiple clinical practice guidelines (multi-CPGs), the mass input workload of patient data, and ...
Architecture and Initial Development of a Digital Library Platform for Computable Knowledge Objects for Health.
Studies in health technology and informatics
Jan 1, 2017
Throughout the world, biomedical knowledge is routinely generated and shared through primary and secondary scientific publications. However, there is too much latency between publication of knowledge and its routine use in practice. To address this l...
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