AI Medical Compendium Topic

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Supervised Machine Learning

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Semi-supervised learning framework with shape encoding for neonatal ventricular segmentation from 3D ultrasound.

Medical physics
BACKGROUND: Three-dimensional (3D) ultrasound (US) imaging has shown promise in non-invasive monitoring of changes in the lateral brain ventricles of neonates suffering from intraventricular hemorrhaging. Due to the poorly defined anatomical boundari...

Analysis of Cerebral CT Based on Supervised Machine Learning as a Predictor of Outcome After Out-of-Hospital Cardiac Arrest.

Neurology
BACKGROUND AND OBJECTIVES: In light of limited intensive care capacities and a lack of accurate prognostic tools to advise caregivers and family members responsibly, this study aims to determine whether automated cerebral CT (CCT) analysis allows pro...

Self-supervised learning for classifying paranasal anomalies in the maxillary sinus.

International journal of computer assisted radiology and surgery
PURPOSE: Paranasal anomalies, frequently identified in routine radiological screenings, exhibit diverse morphological characteristics. Due to the diversity of anomalies, supervised learning methods require large labelled dataset exhibiting diverse an...

Heterogeneous graph convolutional network for multi-view semi-supervised classification.

Neural networks : the official journal of the International Neural Network Society
This paper proposes a novel approach to semantic representation learning from multi-view datasets, distinct from most existing methodologies which typically handle single-view data individually, maintaining a shared semantic link across the multi-vie...

SCAC: A Semi-Supervised Learning Approach for Cervical Abnormal Cell Detection.

IEEE journal of biomedical and health informatics
Cervical abnormal cell detection plays a crucial role in the early screening of cervical cancer. In recent years, some deep learning-based methods have been proposed. However, these methods rely heavily on large amounts of annotated images, which are...

Scan-Specific Self-Supervised Bayesian Deep Non-Linear Inversion for Undersampled MRI Reconstruction.

IEEE transactions on medical imaging
Magnetic resonance imaging is subject to slow acquisition times due to the inherent limitations in data sampling. Recently, supervised deep learning has emerged as a promising technique for reconstructing sub-sampled MRI. However, supervised deep lea...

DREAMER: a computational framework to evaluate readiness of datasets for machine learning.

BMC medical informatics and decision making
BACKGROUND: Machine learning (ML) has emerged as the predominant computational paradigm for analyzing large-scale datasets across diverse domains. The assessment of dataset quality stands as a pivotal precursor to the successful deployment of ML mode...

Cognitive driven gait freezing phase detection and classification for neuro-rehabilitated patients using machine learning algorithms.

Journal of neuroscience methods
BACKGROUND: The significance of diagnosing illnesses associated with brain cognitive and gait freezing phase patterns has led to a recent surge in interest in the study of gait for mental disorders. A more precise and effective way to characterize an...

Automatized self-supervised learning for skin lesion screening.

Scientific reports
Melanoma, the deadliest form of skin cancer, has seen a steady increase in incidence rates worldwide, posing a significant challenge to dermatologists. Early detection is crucial for improving patient survival rates. However, performing total body sc...