Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential.

ABSTRACT: The high-throughput extraction of quantitative imaging features from medical images for the purpose of radiomic analysis, i.e., radiomics in a broad sense, is a rapidly developing and emerging research field that has been attracting increasing interest, particularly in multimodality and multi-omics studies. In this context, the quantitative analysis of multidimensional data plays an essential role in assessing the spatio-temporal characteristics of different tissues and organs and their microenvironment. Herein, recent developments in this method, including manually defined features, data acquisition and preprocessing, lesion segmentation, feature extraction, feature selection and dimension reduction, statistical analysis, and model construction, are reviewed. In addition, deep learning-ba

SUBMITTER: Zhang X 

PROVIDER: S-EPMC8891653 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature