AIMC Topic:
Supervised Machine Learning

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An online supervised learning method based on gradient descent for spiking neurons.

Neural networks : the official journal of the International Neural Network Society Apr 27, 2017
The purpose of supervised learning with temporal encoding for spiking neurons is to make the neurons emit a specific spike train encoded by precise firing times of spikes. The gradient-descent-based (GDB) learning methods are widely used and verified...
Algorithms Synapses Learning Nerve Net Models, Neurological Memory Action Potentials Supervised Machine Learning Neurons
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Inter-labeler and intra-labeler variability of condition severity classification models using active and passive learning methods.

Artificial intelligence in medicine Apr 27, 2017
BACKGROUND AND OBJECTIVES: Labeling instances by domain experts for classification is often time consuming and expensive. To reduce such labeling efforts, we had proposed the application of active learning (AL) methods, introduced our CAESAR-ALE fram...
Data Mining Observer Variation Humans Learning Curve Phenotype Electronic Health Records Area Under Curve Reproducibility of Results Supervised Machine Learning Severity of Illness Index Time Factors
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Supervised learning and dimension reduction techniques for quantification of retinal fluid in optical coherence tomography images.

Eye (London, England) Apr 21, 2017
PurposeThe purpose of the present study is to develop fast automated quantification of retinal fluid in optical coherence tomography (OCT) image sets.MethodsWe developed an image analysis pipeline tailored towards OCT images that consists of five ste...
Retinal Neovascularization Macular Degeneration Image Interpretation, Computer-Assisted Exudates and Transudates Humans Diabetic Retinopathy Tomography, Optical Coherence Retina Supervised Machine Learning Macular Edema
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EHR-based phenotyping: Bulk learning and evaluation.

Journal of biomedical informatics Apr 12, 2017
In data-driven phenotyping, a core computational task is to identify medical concepts and their variations from sources of electronic health records (EHR) to stratify phenotypic cohorts. A conventional analytic framework for phenotyping largely uses ...
Humans Phenotype Data Curation Electronic Health Records Models, Statistical Supervised Machine Learning
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Sensitive detection of rare disease-associated cell subsets via representation learning.

Nature communications Apr 6, 2017
Rare cell populations play a pivotal role in the initiation and progression of diseases such as cancer. However, the identification of such subpopulations remains a difficult task. This work describes CellCnn, a representation learning approach to de...
Cytomegalovirus Infections Supervised Machine Learning Survival Analysis Monocytes Signal Transduction Humans Acquired Immunodeficiency Syndrome Neural Networks, Computer Neoplasm, Residual Cytokines Leukemia Prognosis Immunologic Memory Single-Cell Analysis Killer Cells, Natural T-Lymphocyte Subsets Rare Diseases
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Optimization of Gene Set Annotations Using Robust Trace-Norm Multitask Learning.

IEEE/ACM transactions on computational biology and bioinformatics Apr 3, 2017
Gene set enrichment (GSE) is a useful tool for analyzing and interpreting large molecular datasets generated by modern biomedical science. The accuracy and reproducibility of GSE analysis are heavily affected by the quality and integrity of gene sets...
Humans Computer Simulation Molecular Sequence Annotation Computational Biology Databases, Genetic Supervised Machine Learning Neoplasms Gene Expression Profiling
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Sequential Dictionary Learning From Correlated Data: Application to fMRI Data Analysis.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society Mar 22, 2017
Sequential dictionary learning via the K-SVD algorithm has been revealed as a successful alternative to conventional data driven methods, such as independent component analysis for functional magnetic resonance imaging (fMRI) data analysis. fMRI data...
Signal Processing, Computer-Assisted Supervised Machine Learning Brain Humans Image Processing, Computer-Assisted Algorithms Magnetic Resonance Imaging
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Collaborative Active Visual Recognition from Crowds: A Distributed Ensemble Approach.

IEEE transactions on pattern analysis and machine intelligence Mar 15, 2017
Active learning is an effective way of engaging users to interactively train models for visual recognition more efficiently. The vast majority of previous works focused on active learning with a single human oracle. The problem of active learning wit...
Pattern Recognition, Automated Crowding Humans Algorithms Image Processing, Computer-Assisted Female Supervised Machine Learning Male
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Machine learning applications in cell image analysis.

Immunology and cell biology Mar 15, 2017
Machine learning (ML) refers to a set of automatic pattern recognition methods that have been successfully applied across various problem domains, including biomedical image analysis. This review focuses on ML applications for image analysis in light...
Supervised Machine Learning Microscopy Cell Tracking Animals Image Processing, Computer-Assisted Humans Unsupervised Machine Learning Machine Learning
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Supervised Machine Learning Algorithms Can Classify Open-Text Feedback of Doctor Performance With Human-Level Accuracy.

Journal of medical Internet research Mar 15, 2017
BACKGROUND: Machine learning techniques may be an effective and efficient way to classify open-text reports on doctor's activity for the purposes of quality assurance, safety, and continuing professional development.
Humans Surveys and Questionnaires Algorithms Clinical Competence Physicians Supervised Machine Learning Feedback
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