AIMC Topic: Neural Networks, Computer

Clear Filters Showing 2631 to 2640 of 31376 articles

Automatic skull reconstruction by deep learnable symmetry enforcement.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Every year, thousands of people suffer from skull damage and require personalized implants to fill the cranial cavity. Unfortunately, the waiting time for reconstruction surgery can extend to several weeks or even months, es...

Automated segmentation by SCA-UNet can be directly used for radiomics diagnosis of thymic epithelial tumors.

European journal of radiology
BACKGROUND: Automatic segmentation of thymic lesions in preoperative computed tomography (CT) images is crucial for accurate diagnosis but remains time-consuming. Although UNet is widely used in medical imaging, its performance is limited by the inhe...

Physics-informed neural networks for enhanced reference evapotranspiration estimation in Morocco: Balancing semi-physical models and deep learning.

Chemosphere
Reference evapotranspiration (ETo) is essential for agricultural water management, crop productivity, and irrigation systems. The Penman-Monteith (PM) equation is the standard method for estimating ETo, but its data-intensive nature makes it impracti...

A multi-task self-supervised approach for mass detection in automated breast ultrasound using double attention recurrent residual U-Net.

Computers in biology and medicine
Breast cancer is the most common and lethal cancer among women worldwide. Early detection using medical imaging technologies can significantly improve treatment outcomes. Automated breast ultrasound, known as ABUS, offers more advantages compared to ...

Leveraging Radiomics and Hybrid Quantum-Classical Convolutional Networks for Non-Invasive Detection of Microsatellite Instability in Colorectal Cancer.

Molecular imaging and biology
PURPOSE: The goal of this study is to create a novel framework for identifying MSI status in colorectal cancer using advanced radiomics and deep learning strategies, aiming to enhance clinical decision-making and improve patient outcomes in oncology.

Transition State Searching Accelerated by Neural Network Potential.

Journal of chemical information and modeling
Understanding transition states is pivotal in the design of efficient chemical processes and catalysts. However, identifying transition states is challenging due to the resource-intensive and iterative nature of current computational methods. This st...

Advancing exposure science through artificial intelligence: Neural ordinary differential equations for predicting blood concentrations of volatile organic compounds.

Ecotoxicology and environmental safety
Volatile organic compounds (VOCs) are a significant concern for human health and environmental safety, requiring accurate models to predict their concentrations in body fluids for effective risk assessments. This study evaluates the application of ne...

Development of a Machine-Learning Algorithm to Identify Cauda Equina Compression on Magnetic Resonance Imaging Scans.

World neurosurgery
OBJECTIVE: Cauda equina syndrome (CES) poses significant neurological risks if untreated. Diagnosis relies on clinical and radiological features. As the symptoms are often nonspecific and common, the diagnosis is usually made after a magnetic resonan...

Robust Myocardial Perfusion MRI Quantification With DeepFermi.

IEEE transactions on bio-medical engineering
Stress perfusion cardiac magnetic resonance is an important technique for examining and assessing the blood supply of the myocardium. Currently, the majority of clinical perfusion scans are evaluated based on visual assessment by experienced clinicia...