Biswas M., Suri J.S. (Ed.) Multimodality Imaging, Volume 1: Deep learning applications

OP Publishing Ltd, 2022. — 355 p. — ISBN 978-0-7503-2242-3.Мультимодальная визуализация, том 1: приложения для глубокого обучения
This research and reference text explores the finer details of deep learning models. It provides a brief outline on popular models including convolution neural networks, deep belief networks, autoencoders and residual neural networks. The text discusses some of the deep learning-based applications in gene identification. Sections in the book explore the foundation and necessity of deep learning in radiology, application of deep learning in the area of cardiovascular imaging and deep learning applications in the area of fatty liver disease characterization and COVID-19, respectively. This reference text is highly relevant for medical professionals and researchers in the area of artificial intelligence in medical imaging.Deep learning and its applications
Deep learning and augmented radiology
Deep learning in biomedical imaging
Deep learning in brain imaging
A review of artificial intelligence in brain tumor classification and segmentation
MRI based brain tumor classification and its validation: a transfer learning paradigm
Magnetic resonance based Wilson’s disease tissue characterization in an artificial intelligence framework using transfer learning
Deep learning in cardiovascular imaging
Artificial intelligence based carotid plaque tissue characterisation and classification from ultrasound images using a deep learning paradigm
Quantification of plaque volume using a two-stage deep learning paradigm
Stenosis measurement from ultrasound carotid artery images in the deep learning paradigm
A systematic review of conventional and deep learning models for the measurement of plaque burden
Machine and deep learning in liver imaging
Ultrasound fatty liver disease risk stratification using an extreme learning machine framework
Symtosis: deep learning based liver ultrasound tissue characterisation and risk stratification
Deep learning in COVID-19
Characterization of COVID-19 severity in infected lungs via artificial intelligence transfer learning