AIMC Topic: Machine Learning

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Application of Deep Learning in Neuroradiology: Brain Haemorrhage Classification Using Transfer Learning.

Computational intelligence and neuroscience Jun 3, 2019
In this paper, we address the problem of identifying brain haemorrhage which is considered as a tedious task for radiologists, especially in the early stages of the haemorrhage. The problem is solved using a deep learning approach where a convolution...
Hemorrhage Intracranial Hemorrhages Machine Learning Deep Learning Support Vector Machine Radiologists Humans Neural Networks, Computer
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Predicting cochlear dead regions in patients with hearing loss through a machine learning-based approach: A preliminary study.

PloS one Jun 3, 2019
We propose a machine learning (ML)-based model for predicting cochlear dead regions (DRs) in patients with hearing loss of various etiologies. Five hundred and fifty-five ears from 380 patients (3,770 test samples) diagnosed with sensorineural hearin...
Aged Aged, 80 and over Young Adult Middle Aged Logistic Models Adult Male Machine Learning Auditory Threshold Humans Noise Algorithms Cochlea Female Multivariate Analysis Hearing Loss
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A Machine Learning Approach for the Discovery of Ligand-Specific Functional Mechanisms of GPCRs.

Molecules (Basel, Switzerland) Jun 2, 2019
G protein-coupled receptors (GPCRs) play a key role in many cellular signaling mechanisms, and must select among multiple coupling possibilities in a ligand-specific manner in order to carry out a myriad of functions in diverse cellular contexts. Muc...
Algorithms Molecular Docking Simulation Drug Design Drug Discovery Humans Ligands Machine Learning Receptor, Serotonin, 5-HT2A Protein Binding Quantitative Structure-Activity Relationship Receptors, G-Protein-Coupled Binding Sites Molecular Dynamics Simulation
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Arrangements of Resting State Electroencephalography as the Input to Convolutional Neural Network for Biometric Identification.

Computational intelligence and neuroscience Jun 2, 2019
Biometric is an important field that enables identification of an individual to access their sensitive information and asset. In recent years, electroencephalography- (EEG-) based biometrics have been popularly explored by researchers because EEG is ...
Algorithms Brain-Computer Interfaces Humans Electroencephalography Biometric Identification Signal Processing, Computer-Assisted Neural Networks, Computer Machine Learning
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Convolutional Neural Networks for Recognition of Lymphoblast Cell Images.

Computational intelligence and neuroscience Jun 2, 2019
This paper presents the recognition for WHO classification of acute lymphoblastic leukaemia (ALL) subtypes. The two ALL subtypes considered are T-lymphoblastic leukaemia (pre-T) and B-lymphoblastic leukaemia (pre-B). They exhibit various characterist...
Recognition, Psychology Humans Osteochondrodysplasias Algorithms Fetal Growth Retardation Adrenal Insufficiency Support Vector Machine Neural Networks, Computer Urogenital Abnormalities Lymphocytes Machine Learning Nerve Net
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Decoding electroencephalographic signals for direction in brain-computer interface using echo state network and Gaussian readouts.

Computers in biology and medicine Jun 1, 2019
BACKGROUND: Noninvasive brain-computer interfaces (BCI) for movement control via an electroencephalogram (EEG) have been extensively investigated. However, most previous studies decoded user intention for movement directions based on sensorimotor rhy...
Brain-Computer Interfaces Humans Machine Learning Brain Models, Neurological Electroencephalography Adult Male
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Classification of Skin Disease using Ensemble Data Mining Techniques.

Asian Pacific journal of cancer prevention : APJCP Jun 1, 2019
Objective: Skin diseases are a major global health problem associated with high number of people. With the rapid development of technologies and the application of various data mining techniques in recent years, the progress of dermatological predict...
Prognosis Skin Diseases Humans Data Mining Machine Learning Algorithms
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Development and Validation of Deep Learning Models for Screening Multiple Abnormal Findings in Retinal Fundus Images.

Ophthalmology May 31, 2019
PURPOSE: To develop and evaluate deep learning models that screen multiple abnormal findings in retinal fundus images.
Fundus Oculi Image Interpretation, Computer-Assisted Datasets as Topic Retinal Diseases Deep Learning ROC Curve Area Under Curve Cross-Sectional Studies Male Middle Aged Machine Learning Algorithms Female Adult Sensitivity and Specificity Humans Aged Aged, 80 and over Neural Networks, Computer
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Automated Machine Learning Diagnostic Support System as a Computational Biomarker for Detecting Drug-Induced Liver Injury Patterns in Whole Slide Liver Pathology Images.

Assay and drug development technologies May 31, 2019
in vivor p R.
Humans Machine Learning Biomarkers Liver Automation Chemical and Drug Induced Liver Injury Toxicogenetics Animals Computational Biology Rats
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Ontology mapping for semantically enabled applications.

Drug discovery today May 31, 2019
In this review, we provide a summary of recent progress in ontology mapping (OM) at a crucial time when biomedical research is under a deluge of an increasing amount and variety of data. This is particularly important for realising the full potential...
Drug Discovery Semantics Biological Ontologies Machine Learning Algorithms Humans
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