AIMC Topic: Unsupervised Machine Learning

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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...
Computed Tomography Angiography Cerebral Angiography Image Processing, Computer-Assisted Unsupervised Machine Learning Magnetic Resonance Angiography Humans Algorithms Databases, Factual Brain Deep Learning
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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 Deep Learning Image Processing, Computer-Assisted Magnetic Resonance Imaging Liver Neural Networks, Computer Algorithms Brain 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 Humans Algorithms Brain Adult
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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...
Unsupervised Machine Learning Deep Learning Image Processing, Computer-Assisted Magnetic Resonance Imaging Humans Algorithms Brain
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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%...
Unsupervised Machine Learning Microbiota Machine Learning
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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...
Tomography, X-Ray Computed Unsupervised Machine Learning Sensitivity and Specificity Radiographic Image Interpretation, Computer-Assisted Humans Radiation Dosage Algorithms Lung Imaging, Three-Dimensional Lung Neoplasms
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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 Aged Dementia Humans Middle Aged Aged, 80 and over Unsupervised Machine Learning Electronic Health Records Deep Learning Cross-Sectional Studies Alzheimer Disease Female
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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.
Phenotype Prognosis Retrospective Studies Aortic Valve Humans Severity of Illness Index Sex Factors Machine Learning Unsupervised Machine Learning Middle Aged Time Factors Aortic Valve Insufficiency Predictive Value of Tests Risk Assessment Decision Support Techniques United States Aged Magnetic Resonance Imaging, Cine Disease Progression Female Male Adult Reproducibility of Results Ventricular Function, Left Risk Factors
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TCDE-Net: An unsupervised dual-encoder network for 3D brain medical image registration.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society Mar 23, 2025
Medical image registration is a critical task in aligning medical images from different time points, modalities, or individuals, essential for accurate diagnosis and treatment planning. Despite significant progress in deep learning-based registration...
Brain Imaging, Three-Dimensional Magnetic Resonance Imaging Unsupervised Machine Learning Humans Neural Networks, Computer Algorithms
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Uncovering hidden subtypes in dementia: An unsupervised machine learning approach to dementia diagnosis and personalization of care.

Journal of biomedical informatics Mar 19, 2025
OBJECTIVE: Dementia represents a growing public health challenge, affecting an increasing number of individuals. It encompasses a broad spectrum of cognitive impairments, ranging from mild to severe stages, each of which demands varying levels of car...
Male Dementia Cluster Analysis Unsupervised Machine Learning Humans Female Machine Learning Aged Precision Medicine Aged, 80 and over
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