AIMC Topic: Unsupervised Machine Learning

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Active Dynamic Weighting for multi-domain adaptation.

Neural networks : the official journal of the International Neural Network Society May 20, 2024
Multi-source unsupervised domain adaptation aims to transfer knowledge from multiple labeled source domains to an unlabeled target domain. Existing methods either seek a mixture of distributions across various domains or combine multiple single-sourc...
Algorithms Unsupervised Machine Learning Humans Neural Networks, Computer
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Unsupervised domain adaptive segmentation algorithm based on two-level category alignment.

Neural networks : the official journal of the International Neural Network Society May 20, 2024
To enhance the model's generalization ability in unsupervised domain adaptive segmentation tasks, most approaches have primarily focused on pixel-level local features, but neglected the clue in category information. This limitation results in the seg...
Unsupervised Machine Learning Semantics Neural Networks, Computer Algorithms Image Processing, Computer-Assisted Humans
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Synthetic CT generation based on CBCT using improved vision transformer CycleGAN.

Scientific reports May 20, 2024
Cone-beam computed tomography (CBCT) is a crucial component of adaptive radiation therapy; however, it frequently encounters challenges such as artifacts and noise, significantly constraining its clinical utility. While CycleGAN is a widely employed ...
Algorithms Tomography, X-Ray Computed Cone-Beam Computed Tomography Image Processing, Computer-Assisted Humans Unsupervised Machine Learning Neural Networks, Computer
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Structure enhanced prototypical alignment for unsupervised cross-domain node classification.

Neural networks : the official journal of the International Neural Network Society May 18, 2024
Graph Neural Networks (GNNs) have demonstrated remarkable success in graph node classification task. However, their performance heavily relies on the availability of high-quality labeled data, which can be time-consuming and labor-intensive to acquir...
Humans Semantics Neural Networks, Computer Algorithms Unsupervised Machine Learning
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Missing Wedge Completion via Unsupervised Learning with Coordinate Networks.

International journal of molecular sciences May 17, 2024
Cryogenic electron tomography (cryoET) is a powerful tool in structural biology, enabling detailed 3D imaging of biological specimens at a resolution of nanometers. Despite its potential, cryoET faces challenges such as the missing wedge problem, whi...
Neural Networks, Computer Algorithms Deep Learning Imaging, Three-Dimensional Electron Microscope Tomography Image Processing, Computer-Assisted Unsupervised Machine Learning Cryoelectron Microscopy
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Unsupervised machine learning for clustering forward head posture, protraction and retraction movement patterns based on craniocervical angle data in individuals with nonspecific neck pain.

BMC musculoskeletal disorders May 13, 2024
OBJECTIVES: The traditional understanding of craniocervical alignment emphasizes specific anatomical landmarks. However, recent research has challenged the reliance on forward head posture as the primary diagnostic criterion for neck pain. An advance...
Movement Head Movements Neck Pain Adult Cluster Analysis Male Humans Posture Middle Aged Unsupervised Machine Learning Pain Measurement Female Cervical Vertebrae Head Young Adult
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A retrospective prognostic evaluation using unsupervised learning in the treatment of COVID-19 patients with hypertension treated with ACEI/ARB drugs.

PeerJ May 13, 2024
INTRODUCTION: This study aimed to evaluate the prognosis of patients with COVID-19 and hypertension who were treated with angiotensin-converting enzyme inhibitor (ACEI)/angiotensin receptor B (ARB) drugs and to identify key features affecting patient...
Angiotensin Receptor Antagonists Angiotensin-Converting Enzyme Inhibitors Female Cluster Analysis Male SARS-CoV-2 Humans Algorithms Unsupervised Machine Learning Middle Aged Aged COVID-19 Prognosis Hypertension COVID-19 Drug Treatment Retrospective Studies
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DFUSNN: zero-shot dual-domain fusion unsupervised neural network for parallel MRI reconstruction.

Physics in medicine and biology May 10, 2024
. Recently, deep learning models have been used to reconstruct parallel magnetic resonance (MR) images from undersampled k-space data. However, most existing approaches depend on large databases of fully sampled MR data for training, which can be cha...
Image Processing, Computer-Assisted Magnetic Resonance Imaging Unsupervised Machine Learning Humans Neural Networks, Computer
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Using machine learning to discover traumatic brain injury patient phenotypes: national concussion surveillance system Pilot.

Brain injury May 9, 2024
OBJECTIVE: The objective is to determine whether unsupervised machine learning identifies traumatic brain injury (TBI) phenotypes with unique clinical profiles.
Humans Machine Learning Phenotype Adult Male Middle Aged Female Aged Young Adult Self Report Brain Concussion Centers for Disease Control and Prevention, U.S. Brain Injuries, Traumatic Cluster Analysis Unsupervised Machine Learning Pilot Projects Adolescent United States
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Stain-Free Approach to Determine and Monitor Cell Heath Using Supervised and Unsupervised Image-Based Deep Learning.

Journal of pharmaceutical sciences May 6, 2024
Cell-based medicinal products (CBMPs) are a growing class of therapeutics that promise new treatments for complex and rare diseases. Given the inherent complexity of the whole human cells comprising CBMPs, there is a need for robust and fast analytic...
Flow Cytometry Supervised Machine Learning Quality Control Image Processing, Computer-Assisted Cell Survival Humans Unsupervised Machine Learning Algorithms Deep Learning Jurkat Cells
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