Year: 2020
Type: article
Abstract: This article reviews deep learning methods for medical imaging (focusing on image reconstruction, segmentation, registration, and radiomics) and radiotherapy (ranging from planning and verification to prediction) as well as the connections between them. Then, future topics are discussed involving semantic analysis through natural language processing and graph neural networks. It is believed that deep learning in particular, and artificial intelligence and machine learning in general, will have a revolutionary potential to advance and synergize medical imaging and radiotherapy for unprecedented smart precision healthcare.
Authors: Hongming Shan, Xun Jia, Pingkun Yan, Yunyao Li, Harald Paganetti, Ge Wang
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,000
OpenAlex ID: W3014605657