AIMC Topic: Unsupervised Machine Learning

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Segmenting mechanically heterogeneous domains via unsupervised learning.

Biomechanics and modeling in mechanobiology
From biological organs to soft robotics, highly deformable materials are essential components of natural and engineered systems. These highly deformable materials can have heterogeneous material properties, and can experience heterogeneous deformatio...

Reclassification of ASFV into 7 Biotypes Using Unsupervised Machine Learning.

Viruses
In 2007, an outbreak of African swine fever (ASF), a deadly disease of domestic swine and wild boar caused by the African swine fever virus (ASFV), occurred in Georgia and has since spread globally. Historically, ASFV was classified into 25 different...

Unsupervised learning of mid-level visual representations.

Current opinion in neurobiology
Recently, a confluence between trends in neuroscience and machine learning has brought a renewed focus on unsupervised learning, where sensory processing systems learn to exploit the statistical structure of their inputs in the absence of explicit tr...

Unsupervised learning of stationary and switching dynamical system models from Poisson observations.

Journal of neural engineering
. Investigating neural population dynamics underlying behavior requires learning accurate models of the recorded spiking activity, which can be modeled with a Poisson observation distribution. Switching dynamical system models can offer both explanat...

Interpretable unsupervised learning enables accurate clustering with high-throughput imaging flow cytometry.

Scientific reports
A primary challenge of high-throughput imaging flow cytometry (IFC) is to analyze the vast amount of imaging data, especially in applications where ground truth labels are unavailable or hard to obtain. We present an unsupervised deep embedding algor...

Unsupervised Local Discrimination for Medical Images.

IEEE transactions on pattern analysis and machine intelligence
Contrastive learning, which aims to capture general representation from unlabeled images to initialize the medical analysis models, has been proven effective in alleviating the high demand for expensive annotations. Current methods mainly focus on in...

Workflow for Evaluating Normalization Tools for Omics Data Using Supervised and Unsupervised Machine Learning.

Journal of the American Society for Mass Spectrometry
To achieve high quality omics results, systematic variability in mass spectrometry (MS) data must be adequately addressed. Effective data normalization is essential for minimizing this variability. The abundance of approaches and the data-dependent n...

An unsupervised learning approach to identify immunoglobulin utilization patterns using electronic health records.

Transfusion
BACKGROUND: Managing Canada's immunoglobulin (Ig) product resource allocation is challenging due to increasing demand, high expenditure, and global shortages. Detection of groups with high utilization rates can help with resource planning for Ig prod...