AIMC Topic: Pattern Recognition, Automated

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A comparison of statistical and machine-learning techniques in evaluating the association between dietary patterns and 10-year cardiometabolic risk (2002-2012): the ATTICA study.

The British journal of nutrition May 23, 2018
Statistical methods are usually applied in examining diet-disease associations, whereas factor analysis is commonly used for dietary pattern recognition. Recently, machine learning (ML) has been also proposed as an alternative technique in health cla...
Data Interpretation, Statistical Sex Factors Linear Models Pattern Recognition, Automated Follow-Up Studies Models, Statistical Blood Pressure Hypercholesterolemia Cardiovascular Diseases Middle Aged Diet Risk Assessment Adolescent Incidence Hypertension Diabetes Mellitus Aged, 80 and over Young Adult Adult Male Reproducibility of Results Risk Factors Aged Humans Machine Learning Prospective Studies Female
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Adaptive detrending to accelerate convolutional gated recurrent unit training for contextual video recognition.

Neural networks : the official journal of the International Neural Network Society May 22, 2018
Video image recognition has been extensively studied with rapid progress recently. However, most methods focus on short-term rather than long-term (contextual) video recognition. Convolutional recurrent neural networks (ConvRNNs) provide robust spati...
Neural Networks, Computer Machine Learning Video Recording Pattern Recognition, Automated
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Fine-grained leukocyte classification with deep residual learning for microscopic images.

Computer methods and programs in biomedicine May 22, 2018
BACKGROUND AND OBJECTIVE: Leukocyte classification and cytometry have wide applications in medical domain, previous researches usually exploit machine learning techniques to classify leukocytes automatically. However, constrained by the past developm...
Humans Neural Networks, Computer Machine Learning Support Vector Machine Models, Statistical Leukocytes Microscopy Pattern Recognition, Automated
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A computational framework for the detection of subcortical brain dysmaturation in neonatal MRI using 3D Convolutional Neural Networks.

NeuroImage May 21, 2018
Deep neural networks are increasingly being used in both supervised learning for classification tasks and unsupervised learning to derive complex patterns from the input data. However, the successful implementation of deep neural networks using neuro...
Pattern Recognition, Automated Neuroimaging Heart Defects, Congenital Cerebellar Diseases Deep Learning Humans Image Processing, Computer-Assisted Neural Networks, Computer Magnetic Resonance Imaging Infant, Newborn Cerebellum
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Analysis of hepatitis B virus infection in blood sera using Raman spectroscopy and machine learning.

Photodiagnosis and photodynamic therapy May 19, 2018
This study presents the analysis of hepatitis B virus (HBV) infection in human blood serum using Raman spectroscopy combined with pattern recognition technique. In total, 119 confirmed samples of HBV infected sera, collected from Pakistan Atomic Ener...
Diagnostic Errors Hepatitis B Image Processing, Computer-Assisted Spectrum Analysis, Raman Humans Algorithms Sensitivity and Specificity Support Vector Machine Pattern Recognition, Automated Serum
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Phrase mining of textual data to analyze extracellular matrix protein patterns across cardiovascular disease.

American journal of physiology. Heart and circulatory physiology May 18, 2018
Extracellular matrix (ECM) proteins have been shown to play important roles regulating multiple biological processes in an array of organ systems, including the cardiovascular system. Using a novel bioinformatics text-mining tool, we studied six cate...
Cardiovascular Diseases Big Data Protein Interaction Maps Principal Component Analysis Data Mining Extracellular Matrix Pattern Recognition, Automated Extracellular Matrix Proteins Humans Machine Learning Databases, Factual Biomarkers
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Phenotype Extraction Based on Word Embedding to Sentence Embedding Cascaded Approach.

IEEE transactions on nanobioscience May 18, 2018
As a significant determinant in the development of named entity recognition, phenotypic descriptions are normally presented differently in biomedical literature with the use of complicated semantics. In this paper, a novel approach has been proposed ...
Phenotype Databases, Factual Computational Biology Natural Language Processing Pattern Recognition, Automated Arabidopsis
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Automated and real-time segmentation of suspicious breast masses using convolutional neural network.

PloS one May 16, 2018
In this work, a computer-aided tool for detection was developed to segment breast masses from clinical ultrasound (US) scans. The underlying Multi U-net algorithm is based on convolutional neural networks. Under the Mayo Clinic Institutional Review B...
Carcinoma, Lobular Carcinoma, Intraductal, Noninfiltrating Pattern Recognition, Automated Carcinoma, Ductal, Breast Young Adult Image Processing, Computer-Assisted Humans Prospective Studies Adult Neural Networks, Computer Female Mammography Breast Neoplasms Algorithms Aged Ultrasonography, Mammary Middle Aged Aged, 80 and over Breast
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Exploiting semantic patterns over biomedical knowledge graphs for predicting treatment and causative relations.

Journal of biomedical informatics May 12, 2018
BACKGROUND: Identifying new potential treatment options for medical conditions that cause human disease burden is a central task of biomedical research. Since all candidate drugs cannot be tested with animal and clinical trials, in vitro approaches a...
Biomedical Research Regression Analysis Decision Trees Drug Repositioning Humans Machine Learning Algorithms Databases, Factual Pattern Recognition, Automated Unified Medical Language System Vocabulary, Controlled Knowledge Natural Language Processing Probability Medical Informatics Semantics
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A New Correntropy-Based Conjugate Gradient Backpropagation Algorithm for Improving Training in Neural Networks.

IEEE transactions on neural networks and learning systems May 10, 2018
Mean square error (MSE) is the most prominent criterion in training neural networks and has been employed in numerous learning problems. In this paper, we suggest a group of novel robust information theoretic backpropagation (BP) methods, as correntr...
Information Storage and Retrieval Pattern Recognition, Automated Neural Networks, Computer Systems Theory Artificial Intelligence Algorithms Humans Models, Neurological
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