AIMC Topic: Unsupervised Machine Learning

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Unsupervised stochastic learning and reduced order modeling for global sensitivity analysis in cardiac electrophysiology models.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Numerical simulations in electrocardiology are often affected by various uncertainties inherited from the lack of precise knowledge regarding input values including those related to the cardiac cell model, domain geometry, a...

CPSS: Fusing consistency regularization and pseudo-labeling techniques for semi-supervised deep cardiovascular disease detection using all unlabeled electrocardiograms.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Deep learning usually achieves good performance in the supervised way, which requires a large amount of labeled data. However, manual labeling of electrocardiograms (ECGs) is laborious that requires much medical knowledge. S...

Unsupervised and semi-supervised domain adaptation networks considering both global knowledge and prototype-based local class information for Motor Imagery Classification.

Neural networks : the official journal of the International Neural Network Society
The non-stationarity of EEG signals results in variability across sessions, impeding model building and data sharing. In this paper, we propose a domain adaptation method called GPL, which simultaneously considers global knowledge and prototype-based...

Construction of prediction models for novel subtypes in patients with arteriosclerosis obliterans undergoing endovascular therapy: an unsupervised machine learning study.

Journal of cardiothoracic surgery
BACKGROUND: Arteriosclerosis obliterans (ASO) is a chronic arterial disease that can lead to critical limb ischemia. Endovascular therapy is increasingly used for limb salvage in ASO patients, but the outcomes vary. The development of prediction mode...

Development of a Novel Classification Approach for Cow Behavior Analysis Using Tracking Data and Unsupervised Machine Learning Techniques.

Sensors (Basel, Switzerland)
Global Positioning Systems (GPSs) can collect tracking data to remotely monitor livestock well-being and pasture use. Supervised machine learning requires behavioral observations of monitored animals to identify changes in behavior, which is labor-in...

Deep learning based decoding of single local field potential events.

NeuroImage
How is information processed in the cerebral cortex? In most cases, recorded brain activity is averaged over many (stimulus) repetitions, which erases the fine-structure of the neural signal. However, the brain is obviously a single-trial processor. ...

Insights into traditional Large Deformation Diffeomorphic Metric Mapping and unsupervised deep-learning for diffeomorphic registration and their evaluation.

Computers in biology and medicine
This paper explores the connections between traditional Large Deformation Diffeomorphic Metric Mapping methods and unsupervised deep-learning approaches for non-rigid registration, particularly emphasizing diffeomorphic registration. The study provid...

Improvement of gram staining effect by ethanol pretreatment and quantization of staining image by unsupervised machine learning.

Archives of microbiology
In this study, we propose an Ethanol Pretreatment Gram staining method that significantly enhances the color contrast of the stain, thereby improving the accuracy of judgement, and demonstrated the effectiveness of the modification by eliminating una...

Clothing-invariant contrastive learning for unsupervised person re-identification.

Neural networks : the official journal of the International Neural Network Society
Clothing change person re-identification (CC-ReID) aims to match images of the same person wearing different clothes across diverse scenes. Leveraging biological features or clothing labels, existing CC-ReID methods have demonstrated promising perfor...