AIMC Topic: Cluster Analysis

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Complexity-based unsupervised machine learning for patient-specific VMAT quality assurance.

Medical physics
BACKGROUND: Patient-specific quality assurance (PSQA) is essential to guarantee the requested accuracy and safety of high-precision radiotherapy treatments. With the widespread adoption of modulated-intensity techniques, there is a growing need for i...

BrainAGE latent representation clustering is associated with longitudinal disease progression in early-onset Alzheimer's disease.

Journal of neuroradiology = Journal de neuroradiologie
INTRODUCTION: Early-onset Alzheimer's disease (EOAD) population is a clinically, genetically and pathologically heterogeneous condition. Identifying biomarkers related to disease progression is crucial for advancing clinical trials and improving ther...

Kernelized weighted local information based picture fuzzy clustering with multivariate coefficient of variation and modified total Bregman divergence measure for brain MRI image segmentation.

Computers in biology and medicine
This paper proposes a novel clustering method for noisy image segmentation using a kernelized weighted local information approach under the Picture Fuzzy Set (PFS) framework. Existing kernel-based fuzzy clustering methods struggle with noisy environm...

Unveiling sources of organophosphate esters in marine environments utilizing multi-factor multi-modal high-dimensional clustering algorithm.

Water research
In marine environments, the sources of organophosphate esters (OPEs), particularly emerging OPEs (eOPEs) remain primarily unclear and present significant challenges for accurate source tracing. Here, we developed an unsupervised machine learning fram...

Semantic discrete decoder based on adaptive pixel clustering for monocular depth estimation.

Neural networks : the official journal of the International Neural Network Society
Monocular depth estimation (MDE) has long been a popular and challenging task. Currently, mainstream methods mainly include regression methods based on geometric constraints and ordinal regression methods based on discretized depth intervals. However...

Multi-view graph clustering with Dually Enhanced Tensor Rank Minimization and Diverse Separation of Inconsistent Information.

Neural networks : the official journal of the International Neural Network Society
Multi-view graph clustering is a powerful machine-learning technique for data analysis. However, most of the previous methods still suffer from several limitations. First, most methods overlook the potential inconsistent information in multiple views...

Learning to solve combinatorial optimization problems with heterophily.

Neural networks : the official journal of the International Neural Network Society
Graph Neural Networks (GNNs) are widely used to address combinatorial optimization problems. However, many popular GNNs struggle to generalize to heterophilic scenarios where adjacent nodes tend to be with different labels or dissimilar features, suc...

Interpretable inverse iteration mean shift networks for clustering tasks.

Neural networks : the official journal of the International Neural Network Society
Neural networks have become the standard approach for tasks such as computer vision, machine translation and pattern recognition. While they exhibit significant feature representation capabilities, they often lack interpretability. This suggests that...

KMTLabeler: An Interactive Knowledge-Assisted Labeling Tool for Medical Text Classification.

IEEE transactions on visualization and computer graphics
The process of labeling medical text plays a crucial role in medical research. Nonetheless, creating accurately labeled medical texts of high quality is often a time-consuming task that requires specialized domain knowledge. Traditional methods for g...

Urinary Metabolic Biomarkers of Attentional Control in Children With Attention-Deficit/Hyperactivity Disorder: A Dimensional Approach Through H NMR-Based Metabolomics.

NMR in biomedicine
Enhancing the understanding of attention-deficit/hyperactivity disorder (ADHD) by linking biological processes with behavioral manifestations is a primary objective of the Research Domain Criteria (RDoC) framework, which aims to transcend traditional...