AIMC Topic: Unsupervised Machine Learning

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Unsupervised brain MRI tumour segmentation via two-stage image synthesis.

Medical image analysis Apr 5, 2025
Deep learning shows promise in automated brain tumour segmentation, but it depends on costly expert annotations. Recent advances in unsupervised learning offer an alternative by using synthetic data for training. However, the discrepancy between real...
Unsupervised Machine Learning Image Interpretation, Computer-Assisted Humans Algorithms Brain Neoplasms Deep Learning Magnetic Resonance Imaging
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Unsupervised Domain Adaptation for Cross-Modality Cerebrovascular Segmentation.

IEEE journal of biomedical and health informatics Apr 4, 2025
Cerebrovascular segmentation from time-of-flight magnetic resonance angiography (TOF-MRA) and computed tomography angiography (CTA) is essential in providing supportive information for diagnosing and treatment planning of multiple intracranial vascul...
Cerebral Angiography Unsupervised Machine Learning Magnetic Resonance Angiography Computed Tomography Angiography Humans Algorithms Databases, Factual Brain Deep Learning Image Processing, Computer-Assisted
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SFM-Net: Semantic Feature-Based Multi-Stage Network for Unsupervised Image Registration.

IEEE journal of biomedical and health informatics Apr 4, 2025
It is difficult for general registration methods to establish the fine correspondence between images with complex anatomical structures. To overcome the above problem, this work presents SFM-Net, an unsupervised multi-stage semantic feature-based net...
Humans Neural Networks, Computer Image Processing, Computer-Assisted Magnetic Resonance Imaging Liver Algorithms Brain Deep Learning Unsupervised Machine Learning Semantics
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CorrMorph: Unsupervised Deformable Brain MRI Registration Based on Correlation Mining.

IEEE journal of biomedical and health informatics Apr 4, 2025
Deformable image registration, as a fundamental prerequisite for many medical image analysis tasks, has received considerable attention. However, existing methods suffer from two key issues: 1) single-stream methods that stack moving and fixed images...
Image Processing, Computer-Assisted Magnetic Resonance Imaging Unsupervised Machine Learning Algorithms Brain Adult Humans
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Exploring Contrastive Pre-Training for Domain Connections in Medical Image Segmentation.

IEEE transactions on medical imaging Apr 3, 2025
Unsupervised domain adaptation (UDA) in medical image segmentation aims to improve the generalization of deep models by alleviating domain gaps caused by inconsistency across equipment, imaging protocols, and patient conditions. However, existing UDA...
Image Processing, Computer-Assisted Magnetic Resonance Imaging Unsupervised Machine Learning Algorithms Brain Humans Deep Learning
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Definition of the microbial rare biosphere through unsupervised machine learning.

Communications biology Apr 2, 2025
The microbial rare biosphere, composed of low-abundance microorganisms in a community, lacks a standardized delineation method for its definition. Currently, most studies rely on arbitrary thresholds to define the microbial rare biosphere (e.g., 0.1%...
Machine Learning Unsupervised Machine Learning Microbiota
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Improved unsupervised 3D lung lesion detection and localization by fusing global and local features: Validation in 3D low-dose computed tomography.

Medical image analysis Apr 1, 2025
Unsupervised anomaly detection (UAD) is crucial in low-dose computed tomography (LDCT). Recent AI technologies, leveraging global features, have enabled effective UAD with minimal training data of normal patients. However, this approach, devoid of ut...
Lung Neoplasms Unsupervised Machine Learning Radiographic Image Interpretation, Computer-Assisted Sensitivity and Specificity Lung Imaging, Three-Dimensional Radiation Dosage Humans Algorithms Tomography, X-Ray Computed
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Unsupervised Deep Learning of Electronic Health Records to Characterize Heterogeneity Across Alzheimer Disease and Related Dementias: Cross-Sectional Study.

JMIR aging Mar 31, 2025
BACKGROUND: Alzheimer disease and related dementias (ADRD) exhibit prominent heterogeneity. Identifying clinically meaningful ADRD subtypes is essential for tailoring treatments to specific patient phenotypes.
Male Humans Dementia Aged Unsupervised Machine Learning Electronic Health Records Middle Aged Aged, 80 and over Alzheimer Disease Female Deep Learning Cross-Sectional Studies
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Adaptive bigraph-based multi-view unsupervised dimensionality reduction.

Neural networks : the official journal of the International Neural Network Society Mar 27, 2025
As a crucial machine learning technology, graph-based multi-view unsupervised dimensionality reduction aims to learn compact low-dimensional representations for unlabeled multi-view data using graph structures. However, it faces several challenges, i...
Machine Learning Algorithms Unsupervised Machine Learning Dimensionality Reduction Humans Neural Networks, Computer
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Machine Learning Identification of Patient Phenoclusters in Aortic Regurgitation.

JACC. Cardiovascular imaging Mar 26, 2025
BACKGROUND: Current treatment paradigms assume aortic regurgitation (AR) patients to be a homogenous population, but varied courses of disease progression and outcomes are observed clinically.
United States Aortic Valve Unsupervised Machine Learning Predictive Value of Tests Sex Factors Phenotype Aortic Valve Insufficiency Magnetic Resonance Imaging, Cine Disease Progression Ventricular Function, Left Prognosis Severity of Illness Index Retrospective Studies Middle Aged Aged Time Factors Humans Female Risk Assessment Adult Reproducibility of Results Machine Learning Male Risk Factors Decision Support Techniques
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