TY - JOUR
T1 - Synergizing medical imaging and radiotherapy with deep learning
AU - Shan, Hongming
AU - Jia, Xun
AU - Yan, Pingkun
AU - Li, Yunyao
AU - Paganetti, Harald
AU - Wang, Ge
N1 - Publisher Copyright:
© 2020 The Author(s).
PY - 2020/6
Y1 - 2020/6
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85089428665&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85089428665&partnerID=8YFLogxK
U2 - 10.1088/2632-2153/ab869f
DO - 10.1088/2632-2153/ab869f
M3 - Review article
AN - SCOPUS:85089428665
SN - 2632-2153
VL - 1
JO - Machine Learning: Science and Technology
JF - Machine Learning: Science and Technology
IS - 2
M1 - 021001
ER -