AIMC Topic: Neural Networks, Computer

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Artificial Intelligence in Brain Tumors.

Advances in experimental medicine and biology
The introduction of "intelligent machines" goes back to Alan Turing in the 1940s. Artificial intelligence (AI) is a broad umbrella covering different methodologies, such as machine learning and deep learning. Deep learning, characterized by multilaye...

Large Language Models in Neurosurgery.

Advances in experimental medicine and biology
A large language model (LLM), in the context of natural language processing and artificial intelligence, refers to a sophisticated neural network that has been trained on a massive amount of text data to understand and generate human-like language. T...

Bayesian Neural Networks in Predictive Neurosurgery.

Advances in experimental medicine and biology
"Bayesian Neural Networks in Predictive Neurosurgery" explains both conceptually and theoretically the combination of statistical techniques for clinical prediction models, including artificial neural networks, Bayesian regression, and Bayesian neura...

Navigating Mathematical Basics: A Primer for Deep Learning in Science.

Advances in experimental medicine and biology
We present a gentle introduction to elementary mathematical notation with the focus of communicating deep learning principles. This is a "math crash course" aimed at quickly enabling scientists with understanding of the building blocks used in many e...

Deep Learning: A Primer for Neurosurgeons.

Advances in experimental medicine and biology
This chapter explores the transformative impact of deep learning (DL) on neurosurgery, elucidating its pivotal role in enhancing diagnostic performance, surgical planning, execution, and postoperative assessment. It delves into various deep learning ...

A neural network model for predicting the effectiveness of treatment in patients with neovascular glaucoma associated with diabetes mellitus.

Romanian journal of ophthalmology
INTRODUCTION: The study hypothesizes that neural networks can be an effective tool for predicting treatment outcomes in patients with diabetic neovascular glaucoma (NVG), considering not only baseline intraocular pressure (IOP) values but also inflam...

PSAA-nnUNet: An Efficient Method for CT Carotid Artery Image Segmentation.

Advances in experimental medicine and biology
Carotid artery (CA) stenosis (CAS) constitutes a significant factor to ischaemic cerebrovascular events which exhibiting no overt symptoms in the early stages. Early detection of CAS can prevent ischaemic stroke and improve patient prognosis. In this...

ADT Network: A Novel Nonlinear Method for Decoding Speech Envelopes From EEG Signals.

Trends in hearing
Decoding speech envelopes from electroencephalogram (EEG) signals holds potential as a research tool for objectively assessing auditory processing, which could contribute to future developments in hearing loss diagnosis. However, current methods stru...

The Study of Echocardiography of Left Ventricle Segmentation Combining Transformer and Convolutional Neural Networks.

International heart journal
Accurate prediction of echocardiographic parameters is essential for diagnosis and treatment of cardiac disease, especially for segmentation of the left ventricle to obtain measurements such as left ventricular ejection fraction and volume. However, ...

Revolution of Medical Review: The Application of Meta-Analysis and Convolutional Neural Network-Natural Language Processing in Classifying the Literature for Head and Neck Cancer Radiotherapy.

Cancer control : journal of the Moffitt Cancer Center
This study explored the application of meta-analysis and convolutional neural network-natural language processing (CNN-NLP) technologies in classifying literature concerning radiotherapy for head and neck cancer. It aims to enhance both the efficienc...