AIMC Topic: Unsupervised Machine Learning

Clear Filters Showing 81 to 90 of 828 articles

Unsupervised learning from EEG data for epilepsy: A systematic literature review.

Artificial intelligence in medicine
BACKGROUND AND OBJECTIVES: Epilepsy is a neurological disorder characterized by recurrent epileptic seizures, whose neurophysiological signature is altered electroencephalographic (EEG) activity. The use of artificial intelligence (AI) methods on EEG...

High throughput analysis of rare nanoparticles with deep-enhanced sensitivity via unsupervised denoising.

Nature communications
The large-scale multiparametric analysis of individual nanoparticles is increasingly vital in the diverse fields of biology, medicine, and materials science. However, the current methods struggle with the tradeoff between measurement scalability and ...

IConDiffNet: an unsupervised inverse-consistent diffeomorphic network for medical image registration.

Physics in medicine and biology
Deformable image registration (DIR) is critical in many medical imaging applications. Diffeomorphic transformations, which are smooth invertible mappings with smooth inverses preserve topological properties and are an anatomically plausible means of ...

Unsupervised neural network-based image stitching method for bladder endoscopy.

PloS one
Bladder endoscopy enables the observation of intravesical lesion characteristics, making it an essential tool in urology. Image stitching techniques are commonly employed to expand the field of view of bladder endoscopy. Traditional image stitching m...

Unsupervised cross-modality domain adaptation via source-domain labels guided contrastive learning for medical image segmentation.

Medical & biological engineering & computing
Unsupervised domain adaptation (UDA) offers a promising approach to enhance discriminant performance on target domains by utilizing domain adaptation techniques. These techniques enable models to leverage knowledge from the source domain to adjust to...

An efficient approach on risk factor prediction related to cardiovascular disease around Kumbakonam, Tamil Nadu, India, using unsupervised machine learning techniques.

Scientific reports
Nowadays, human beings suffer from varieties of diseases due to the environmental circumstances and their residing habits. Cardiovascular diseases (CVD) are the leading cause of mortality among all diseases. CVDs are heart-related diseases. In early ...

PrediRep: Modeling hierarchical predictive coding with an unsupervised deep learning network.

Neural networks : the official journal of the International Neural Network Society
Hierarchical predictive coding (hPC) provides a compelling framework for understanding how the cortex predicts future sensory inputs by minimizing prediction errors through an internal generative model of the external world. Existing deep learning mo...

Unsupervised machine learning clustering approach for hospitalized COVID-19 pneumonia patients.

BMC pulmonary medicine
BACKGROUND: Identification of distinct clinical phenotypes of diseases can guide personalized treatment. This study aimed to classify hospitalized COVID-19 pneumonia subgroups using an unsupervised machine learning approach.

Machine learning-based unsupervised phenotypic clustering analysis of patients with IgA nephropathy: Distinct therapeutic responses of different groups.

Chinese medical journal
BACKGROUND: Immunoglobulin A nephropathy (IgAN) has a heterogeneous clinical presentation. Comparison of different IgAN subgroups may facilitate the application of more targeted therapies. This study was aimed to distinct disease phenotypes in IgAN a...

An investigation of microbial groundwater contamination seasonality and extreme weather event interruptions using "big data", time-series analyses, and unsupervised machine learning.

Environmental pollution (Barking, Essex : 1987)
Temporal studies of groundwater potability have historically focused on E. coli detection rates, with non-E. coli coliforms (NEC) and microbial concentrations remaining understudied by comparison. Additionally, "big data" (i.e., large, diverse datase...