Titlebook: Computational Neurosurgery; Antonio Di Ieva,Eric Suero Molina,Carlo Russo Book 2024 The Editor(s) (if applicable) and The Author(s), under

Cultivate

Navigating Mathematical Basics: A Primer for Deep Learning in Scienceourse” aimed at quickly enabling scientists with understanding of the building blocks used in many equations, formulas, and algorithms that describe deep learning. While this short presentation cannot replace solid mathematical knowledge that needs multiple courses and years to solidify, our aim is

CAMP

Computational Fractal-Based Neurosurgery different fields, including physics, astronomy, geology, meteorology, finances, social sciences, and computer graphics. In the biomedical sciences, the use of fractal parameters has allowed the introduction of novel morphometric parameters, which have been shown to be useful to characterize any bio

Mnemonics

Graph Theory and Modeling of Network Topology in Clinical Neurosurgeryregions. This conceptual revolution, coupled with advancements in computing capabilities and resources, has enabled a wealth of research on delineating and treating aberrant brain networks. One approach to network neuroscience, graph theory, involves modeling network topologies as mathematical graph

debris

Computer Vision in Digital Neuropathologyosurgeons increasingly embrace digital platforms, computer vision techniques and computerized quantitative analyses (i.e., pathomics) play a pivotal role in automating and enhancing diagnostic processes, as well as in unlocking new insights from neuropathological images. This chapter explores the ro

Neuralgia

AGONY

種類

Large Language Models in Neurosurgery has been trained on a massive amount of text data to understand and generate human-like language. These models are typically built on architectures like transformers. The term “l(fā)arge” indicates that the neural network has a significant number of parameters, making it more powerful and capable of ca

PET-scan

延期

Affirm

Machine Learning and Radiomics in Gliomasosis, prognosis, and treatment of these complex tumors. Radiomics involves the extraction of a multitude of quantitative features from medical images. When these features are analyzed through ML algorithms, the precision of tumor characterization is enhanced beyond traditional methods..This chapter