AIMC Topic: Unsupervised Machine Learning

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SegAE: Unsupervised white matter lesion segmentation from brain MRIs using a CNN autoencoder.

NeuroImage. Clinical Nov 9, 2019
White matter hyperintensities (WMHs) of presumed vascular origin are frequently observed in magnetic resonance images (MRIs) of the elderly. Detection and quantification of WMHs is important to help doctors make diagnoses and evaluate prognosis of th...
Deep Learning Humans Image Processing, Computer-Assisted Neural Networks, Computer Magnetic Resonance Imaging Unsupervised Machine Learning Aged Female Image Enhancement Aged, 80 and over White Matter Male
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Comparing lesion segmentation methods in multiple sclerosis: Input from one manually delineated subject is sufficient for accurate lesion segmentation.

NeuroImage. Clinical Nov 5, 2019
PURPOSE: Accurate lesion segmentation is important for measurements of lesion load and atrophy in subjects with multiple sclerosis (MS). International MS lesion challenges show a preference of convolutional neural networks (CNN) strategies, such as n...
Female Multiple Sclerosis, Relapsing-Remitting Adult Male Humans Magnetic Resonance Imaging Middle Aged Neural Networks, Computer Unsupervised Machine Learning Deep Learning Brain Supervised Machine Learning Image Processing, Computer-Assisted
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Unsupervised 3D End-to-End Medical Image Registration With Volume Tweening Network.

IEEE journal of biomedical and health informatics Nov 1, 2019
3D medical image registration is of great clinical importance. However, supervised learning methods require a large amount of accurately annotated corresponding control points (or morphing), which are very difficult to obtain. Unsupervised learning m...
Humans Tomography, X-Ray Computed Neural Networks, Computer Liver Imaging, Three-Dimensional Algorithms Unsupervised Machine Learning Databases, Factual
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Online identification of functional regions in deep brain stimulation based on an unsupervised random forest with feature selection.

Journal of neural engineering Oct 23, 2019
OBJECTIVE: The identification of functional regions, in particular the subthalamic nucleus, through microelectrode recording (MER) is the key step in deep brain stimulation (DBS). To eliminate variability in a neurosurgeon's judgment, this study pres...
Stereotaxic Techniques Subthalamic Nucleus Unsupervised Machine Learning Deep Brain Stimulation Microelectrodes Humans Action Potentials Parkinson Disease
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Unsupervised machine learning for exploratory data analysis in imaging mass spectrometry.

Mass spectrometry reviews Oct 11, 2019
Imaging mass spectrometry (IMS) is a rapidly advancing molecular imaging modality that can map the spatial distribution of molecules with high chemical specificity. IMS does not require prior tagging of molecular targets and is able to measure a larg...
Data Analysis Unsupervised Machine Learning Humans Molecular Imaging Animals Mass Spectrometry
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Looking to the future: Learning from experience, averting catastrophe.

Neural networks : the official journal of the International Neural Network Society Oct 10, 2019
As humans go through life sifting vast quantities of complex information, we extract knowledge from settings that are more ambiguous than our early homes and classrooms. Learning from experience in an individual's unique context generally improves ex...
Supervised Machine Learning History, 21st Century History, 20th Century Neural Networks, Computer Unsupervised Machine Learning
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Visual Correspondences for Unsupervised Domain Adaptation on Electron Microscopy Images.

IEEE transactions on medical imaging Oct 9, 2019
We present an Unsupervised Domain Adaptation strategy to compensate for domain shifts on Electron Microscopy volumes. Our method aggregates visual correspondences-motifs that are visually similar across different acquisitions-to infer changes on the ...
Algorithms Brain Animals Image Processing, Computer-Assisted Mice Unsupervised Machine Learning Mitochondria Microscopy, Electron
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ICU staffing feature phenotypes and their relationship with patients' outcomes: an unsupervised machine learning analysis.

Intensive care medicine Oct 8, 2019
PURPOSE: To study whether ICU staffing features are associated with improved hospital mortality, ICU length of stay (LOS) and duration of mechanical ventilation (MV) using cluster analysis directed by machine learning.
Unsupervised Machine Learning Humans Hospital Mortality Length of Stay Nurses Intensive Care Units Time Factors Personnel Staffing and Scheduling Brazil Odds Ratio Logistic Models Physicians Physical Therapists Organ Dysfunction Scores Cluster Analysis Retrospective Studies Hospital Bed Capacity
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Unsupervised Machine Learning for Analysis of Phase Separation in Ternary Lipid Mixture.

Journal of chemical theory and computation Oct 7, 2019
Phase separation in mixed lipid systems has been extensively studied both experimentally and theoretically because of its biological importance. A detailed description of such complex systems undoubtedly requires novel mathematical frameworks that ar...
Phosphatidylcholines 1,2-Dipalmitoylphosphatidylcholine Lipid Bilayers Unsupervised Machine Learning Cholesterol Lipids Molecular Dynamics Simulation
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Machine learning in cardiovascular magnetic resonance: basic concepts and applications.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance Oct 7, 2019
Machine learning (ML) is making a dramatic impact on cardiovascular magnetic resonance (CMR) in many ways. This review seeks to highlight the major areas in CMR where ML, and deep learning in particular, can assist clinicians and engineers in improvi...
Cardiovascular Diseases Diagnosis, Computer-Assisted Deep Learning Humans Machine Learning Reproducibility of Results Supervised Machine Learning Image Interpretation, Computer-Assisted Coronary Circulation Unsupervised Machine Learning Predictive Value of Tests Myocardium Myocardial Perfusion Imaging Magnetic Resonance Imaging, Cine
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