AIMC Topic: Neural Networks, Computer

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Error correcting 2D-3D cascaded network for myocardial infarct scar segmentation on late gadolinium enhancement cardiac magnetic resonance images.

Medical image analysis
Late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) imaging is considered the in vivo reference standard for assessing infarct size (IS) and microvascular obstruction (MVO) in ST-elevation myocardial infarction (STEMI) patients. Howeve...

Machine learning approaches for classifying major depressive disorder using biological and neuropsychological markers: A meta-analysis.

Neuroscience and biobehavioral reviews
Traditional diagnostic methods for major depressive disorder (MDD), which rely on subjective assessments, may compromise diagnostic accuracy. In contrast, machine learning models have the potential to classify and diagnose MDD more effectively, reduc...

Rethinking boundary detection in deep learning-based medical image segmentation.

Medical image analysis
Medical image segmentation is a pivotal task within the realms of medical image analysis and computer vision. While current methods have shown promise in accurately segmenting major regions of interest, the precise segmentation of boundary areas rema...

Multi-machine learning methods for rapid and synergistic inversion of groundwater contamination source, hydrogeologic parameter and boundary condition.

Journal of contaminant hydrology
The application of machine learning methods to the groundwater pollution inversion problem has become a hot research topic in recent years. However, applying machine learning methods to achieve synergistic and rapid identification of pollution source...

A diffusion-stimulated CT-US registration model with self-supervised learning and synthetic-to-real domain adaptation.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
In abdominal interventional procedures, achieving precise registration of 2D ultrasound (US) frames with 3D computed tomography (CT) scans presents a significant challenge. Traditional tracking methods often rely on high-precision sensors, which can ...

Estimation of the average molecular weight of microbial polyesters from FTIR spectra using artificial intelligence.

Analytical sciences : the international journal of the Japan Society for Analytical Chemistry
In this paper, we present a method for calculating the average molecular weight of microbial polyesters using Fourier transform infrared spectroscopy (FTIR) data as input. FTIR spectra provide the necessary quantitative information, as the impact of ...

Neural Networks for On-Chip Model Predictive Control: A Method to Build Optimized Training Datasets and its Application to Type-1 Diabetes.

IEEE transactions on cybernetics
Training neural networks (NNs) to behave as model predictive control (MPC) algorithms is an effective way to implement them in constrained embedded devices. By collecting large amounts of input-output data, where inputs represent system states and ou...

Circapproved: Digital Pattern Recognition via Artificial Neural Network for the Identification of Normal Penis Parameters for Circumcision Eligibility Using Mobile App.

Journal of pediatric surgery
BACKGROUND: Circumcision is a prevalent surgical procedure performed for medical, cultural, and religious reasons, requiring a thorough assessment of penile anatomy to determine eligibility, especially for identifying contraindications such as congen...

Predictive modeling based on machine learning for mapping risk areas of human sporotrichosis in southeastern Brazil.

Research in veterinary science
Sporotrichosis, a zoonotic mycosis with a growing public health impact, requires innovative methods to map risk areas. This study applied machine learning techniques, Artificial Neural Networks (ANN), and Decision Trees (DT) to integrate sociodemogra...

An informed machine learning based environmental risk score for hypertension in European adults.

Artificial intelligence in medicine
BACKGROUND: The exposome framework seeks to unravel the cumulated effects of environmental exposures on health. However, existing methods struggle with challenges including multicollinearity, non-linearity and confounding. To address these limitation...