Radiomics and Deep Learning: Hepatic Applications

Radiomics and Deep Learning

Applications and limitations of radiomics.

Radiomics is an emerging field in quantitative imaging that uses advanced imaging features to objectively and quantitatively describe tumour phenotypes. Radiomic features have recently drawn considerable interest due to its potential predictive power for treatment outcomes and cancer genetics, which may have important applications in personalized medicine. In this technical review, we describe ...

Deep Learning: Methods and Applications

This book is aimed to provide an overview of general deep learning methodology and its applications to a variety of signal and information processing tasks. The application areas are chosen with the following three criteria: 1) expertise or knowledge of the authors; 2) the application areas that have already been transformed by the successful use of deep learning technology, such as speech reco...

Applications | Uncertainty in Deep Learning

In previous chapters we linked stochastic regularisation techniques (SRTs) to approximate inference in Bayesian neural networks (NNs), and studied the resulting model uncertainty for popular SRTs such as dropout. We have yet to give any real-world applications stemming from this link though, leaving it somewhat in the realms of “theoretical work”. But a theory is worth very little if we can’t u...

Dissimilarity-based representation for radiomics applications

Radiomics is a term which refers to the analysis of the large amount of quantitative tumor features extracted from medical images to find useful predictive, diagnostic or prognostic information. Many recent studies have proved that radiomics can offer a lot of useful information that physicians cannot extract from the medical images and can be associated with other information like gene or prot...