AIMC Topic: Algorithms

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Enhancing cervical cancer detection and robust classification through a fusion of deep learning models.

Scientific reports
Cervical cancer, the second most prevalent cancer affecting women, arises from abnormal cell growth in the cervix, a crucial anatomical structure within the uterus. The significance of early detection cannot be overstated, prompting the use of variou...

Machine Learning Quantification of Pulmonary Regurgitation Fraction from Echocardiography.

Pediatric cardiology
Assessment of pulmonary regurgitation (PR) guides treatment for patients with congenital heart disease. Quantitative assessment of PR fraction (PRF) by echocardiography is limited. Cardiac MRI (cMRI) is the reference-standard for PRF quantification. ...

Preoperative Differentiation of HER2-Zero and HER2-Low from HER2-Positive Invasive Ductal Breast Cancers Using BI-RADS MRI Features and Machine Learning Modeling.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Accurate determination of human epidermal growth factor receptor 2 (HER2) is important for choosing optimal HER2 targeting treatment strategies. HER2-low is currently considered HER2-negative, but patients may be eligible to receive new a...

Boundary sample-based class-weighted semi-supervised learning for malignant tumor classification of medical imaging.

Medical & biological engineering & computing
Medical image classification plays a pivotal role within the field of medicine. Existing models predominantly rely on supervised learning methods, which necessitate large volumes of labeled data for effective training. However, acquiring and annotati...

Improving span-based Aspect Sentiment Triplet Extraction with part-of-speech filtering and contrastive learning.

Neural networks : the official journal of the International Neural Network Society
Aspect Sentiment Triple Extraction (ASTE), a subtask of fine-grained sentiment analysis, aims to extract aspect terms, opinion terms, and their corresponding sentiment polarities from sentences. Previous methods often enumerated all possible spans of...

SPICER: Self-supervised learning for MRI with automatic coil sensitivity estimation and reconstruction.

Magnetic resonance in medicine
PURPOSE: To introduce a novel deep model-based architecture (DMBA), SPICER, that uses pairs of noisy and undersampled k-space measurements of the same object to jointly train a model for MRI reconstruction and automatic coil sensitivity estimation.

Labelling with dynamics: A data-efficient learning paradigm for medical image segmentation.

Medical image analysis
The success of deep learning on image classification and recognition tasks has led to new applications in diverse contexts, including the field of medical imaging. However, two properties of deep neural networks (DNNs) may limit their future use in m...

Automatic segmentation of dura for quantitative analysis of lumbar stenosis: A deep learning study with 518 CT myelograms.

Journal of applied clinical medical physics
BACKGROUND: The diagnosis of lumbar spinal stenosis (LSS) can be challenging because radicular pain is not often present in the culprit-level localization. Accurate segmentation and quantitative analysis of the lumbar dura on radiographic images are ...

Precision healthcare: A deep dive into machine learning algorithms and feature selection strategies for accurate heart disease prediction.

Computers in biology and medicine
This paper presents a comprehensive exploration of machine learning algorithms (MLAs) and feature selection techniques for accurate heart disease prediction (HDP) in modern healthcare. By focusing on diverse datasets encompassing various challenges, ...

Prediction of 24-Hour Urinary Sodium Excretion Using Machine-Learning Algorithms.

Journal of the American Heart Association
BACKGROUND: Accurate quantification of sodium intake based on self-reported dietary assessments has been a persistent challenge. We aimed to apply machine-learning (ML) algorithms to predict 24-hour urinary sodium excretion from self-reported questio...