AIMC Topic: Machine Learning

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Deep Learning Methods for Underwater Target Feature Extraction and Recognition.

Computational intelligence and neuroscience Mar 27, 2018
The classification and recognition technology of underwater acoustic signal were always an important research content in the field of underwater acoustic signal processing. Currently, wavelet transform, Hilbert-Huang transform, and Mel frequency ceps...
Signal Processing, Computer-Assisted Acoustics Pattern Recognition, Automated Neural Networks, Computer Machine Learning Water
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Statistical and Machine Learning forecasting methods: Concerns and ways forward.

PloS one Mar 27, 2018
Machine Learning (ML) methods have been proposed in the academic literature as alternatives to statistical ones for time series forecasting. Yet, scant evidence is available about their relative performance in terms of accuracy and computational requ...
Neural Networks, Computer Support Vector Machine Normal Distribution Machine Learning Models, Statistical Forecasting Bayes Theorem
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Data Analysis Strategies in Medical Imaging.

Clinical cancer research : an official journal of the American Association for Cancer Research Mar 26, 2018
Radiographic imaging continues to be one of the most effective and clinically useful tools within oncology. Sophistication of artificial intelligence has allowed for detailed quantification of radiographic characteristics of tissues using predefined ...
Precision Medicine Diagnostic Imaging Medical Oncology Artificial Intelligence Humans Machine Learning Data Analysis Algorithms
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Toward Augmented Radiologists: Changes in Radiology Education in the Era of Machine Learning and Artificial Intelligence.

Academic radiology Mar 26, 2018
Radiology practice will be altered by the coming of artificial intelligence, and the process of learning in radiology will be similarly affected. In the short term, radiologists will need to understand the first wave of artificially intelligent tools...
Learning Internship and Residency Humans Fellowships and Scholarships Machine Learning Radiology Curriculum
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Classification of Alzheimer's Disease Based on Eight-Layer Convolutional Neural Network with Leaky Rectified Linear Unit and Max Pooling.

Journal of medical systems Mar 26, 2018
Alzheimer's disease (AD) is a progressive brain disease. The goal of this study is to provide a new computer-vision based technique to detect it in an efficient way. The brain-imaging data of 98 AD patients and 98 healthy controls was collected using...
Male Image Processing, Computer-Assisted Alzheimer Disease Aged Aged, 80 and over Sensitivity and Specificity Neural Networks, Computer Machine Learning Female Humans
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Detecting oropharyngeal carcinoma using multispectral, narrow-band imaging and machine learning.

The Laryngoscope Mar 25, 2018
OBJECTIVE: To determine if multispectral narrow-band imaging (mNBI) can be used for automated, quantitative detection of oropharyngeal carcinoma (OPC).
Humans Machine Learning Prospective Studies Diagnosis, Differential Sensitivity and Specificity Pattern Recognition, Automated Narrow Band Imaging Bayes Theorem Female Male Oropharyngeal Neoplasms Carcinoma Aged Middle Aged Aged, 80 and over
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Deep feature classification of angiomyolipoma without visible fat and renal cell carcinoma in abdominal contrast-enhanced CT images with texture image patches and hand-crafted feature concatenation.

Medical physics Mar 25, 2018
PURPOSE: To develop an automatic deep feature classification (DFC) method for distinguishing benign angiomyolipoma without visible fat (AMLwvf) from malignant clear cell renal cell carcinoma (ccRCC) from abdominal contrast-enhanced computer tomograph...
Tomography, X-Ray Computed Sensitivity and Specificity Image Processing, Computer-Assisted Contrast Media Humans Machine Learning Radiography, Abdominal Angiomyolipoma Diagnosis, Differential Kidney Neoplasms Carcinoma, Renal Cell
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Automated estimation of image quality for coronary computed tomographic angiography using machine learning.

European radiology Mar 23, 2018
OBJECTIVES: Our goal was to evaluate the efficacy of a fully automated method for assessing the image quality (IQ) of coronary computed tomography angiography (CCTA).
Area Under Curve Coronary Artery Disease Computed Tomography Angiography Aged Male Middle Aged Radiographic Image Enhancement Humans Machine Learning Female
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Healthcare-associated ventriculitis and meningitis in a neuro-ICU: Incidence and risk factors selected by machine learning approach.

Journal of critical care Mar 23, 2018
PURPOSE: To define the incidence of healthcare-associated ventriculitis and meningitis (HAVM) in the neuro-ICU and to identify HAVM risk factors using tree-based machine learning (ML) algorithms.
Adolescent Incidence Child, Preschool Machine Learning Male Child Meningitis, Bacterial Middle Aged Prospective Studies Craniotomy Surgical Wound Infection Russia Postoperative Complications Intensive Care Units Cerebral Ventriculitis Cross Infection Young Adult Humans Female Adult Risk Factors
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Editorial on special issue: Machine learning on MCI.

Journal of neuroscience methods Mar 23, 2018
Cognitive Dysfunction Image Processing, Computer-Assisted Magnetic Resonance Imaging Humans Machine Learning Brain
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