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Unsupervised Machine Learning

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Unsupervised feature selection algorithm for multiclass cancer classification of gene expression RNA-Seq data.

Genomics
This paper presents a Grouping Genetic Algorithm (GGA) to solve a maximally diverse grouping problem. It has been applied for the classification of an unbalanced database of 801 samples of gene expression RNA-Seq data in 5 types of cancer. The sample...

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

NeuroImage. Clinical
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...

Comparing lesion segmentation methods in multiple sclerosis: Input from one manually delineated subject is sufficient for accurate lesion segmentation.

NeuroImage. Clinical
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...

Unsupervised 3D End-to-End Medical Image Registration With Volume Tweening Network.

IEEE journal of biomedical and health informatics
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...

Online identification of functional regions in deep brain stimulation based on an unsupervised random forest with feature selection.

Journal of neural engineering
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...

Unsupervised machine learning for exploratory data analysis in imaging mass spectrometry.

Mass spectrometry reviews
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...

Looking to the future: Learning from experience, averting catastrophe.

Neural networks : the official journal of the International Neural Network Society
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...

Visual Correspondences for Unsupervised Domain Adaptation on Electron Microscopy Images.

IEEE transactions on medical imaging
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 ...

Unsupervised Machine Learning for Analysis of Phase Separation in Ternary Lipid Mixture.

Journal of chemical theory and computation
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...