AIMC Topic: Unsupervised Machine Learning

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Unsupervised learning reveals interpretable latent representations for translucency perception.

PLoS computational biology
Humans constantly assess the appearance of materials to plan actions, such as stepping on icy roads without slipping. Visual inference of materials is important but challenging because a given material can appear dramatically different in various sce...

An Unsupervised Learning-Based Regional Deformable Model for Automated Multi-Organ Contour Propagation.

Journal of digital imaging
The aim of this study is to evaluate a regional deformable model based on a deep unsupervised learning model for automatic contour propagation in breast cone-beam computed tomography-guided adaptive radiation therapy. A deep unsupervised learning mod...

Detecting genomic deletions from high-throughput sequence data with unsupervised learning.

BMC bioinformatics
BACKGROUND: Structural variation (SV), which ranges from 50 bp to [Formula: see text] 3 Mb in size, is an important type of genetic variations. Deletion is a type of SV in which a part of a chromosome or a sequence of DNA is lost during DNA replicati...

Applying Unsupervised Machine Learning Models to Identify Serve Performance Related Indicators in Women's Volleyball.

Research quarterly for exercise and sport
In volleyball, the effect of different factors on serve performance has usually been analyzed with traditional statistical techniques such as logistic regression or discriminant analysis. In this study, two of the main models used in unsupervised ma...

Machine Learning Assisted Clustering of Nanoparticle Structures.

Journal of chemical information and modeling
We propose a scheme for the automatic separation (i.e., clustering) of data sets composed of several nanoparticle (NP) structures by means of Machine Learning techniques. These data sets originate from atomistic simulations, such as global optimizati...

Profiling Physical Fitness of Physical Education Majors Using Unsupervised Machine Learning.

International journal of environmental research and public health
The academic curriculum has shown to promote sedentary behavior in college students. This study aimed to profile the physical fitness of physical education majors using unsupervised machine learning and to identify the differences between sexes, acad...

Cell deformability heterogeneity recognition by unsupervised machine learning from in-flow motion parameters.

Lab on a chip
Cell deformability is a well-established marker of cell states for diagnostic purposes. However, the measurement of a wide range of different deformability levels is still challenging, especially in cancer, where a large heterogeneity of rheological/...

Unsupervised Learning Composite Network to Reduce Training Cost of Deep Learning Model for Colorectal Cancer Diagnosis.

IEEE journal of translational engineering in health and medicine
Deep learning facilitates complex medical data analysis and is increasingly being explored in colorectal cancer diagnostics. However, the training cost of the deep learning model limits its real-world medical utility. In this study, we present a comp...

Unsupervised machine learning methods and emerging applications in healthcare.

Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA
Unsupervised machine learning methods are important analytical tools that can facilitate the analysis and interpretation of high-dimensional data. Unsupervised machine learning methods identify latent patterns and hidden structures in high-dimensiona...

Quantifying the Severity of Metopic Craniosynostosis Using Unsupervised Machine Learning.

Plastic and reconstructive surgery
BACKGROUND: Quantifying the severity of head shape deformity and establishing a threshold for operative intervention remains challenging in patients with metopic craniosynostosis (MCS). This study combines three-dimensional skull shape analysis with ...