Synergizing medical imaging and radiotherapy with deep learning
Hongming Shan, Xun Jia, Pingkun Yan, LI Yun-yao, Harald Paganetti, Ge Wang
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.
Topics & Concepts
Deep learningComputer scienceArtificial intelligenceMedical imagingSegmentationRadiomicsRadiation therapyImage segmentationMedicineRadiologyRadiomics and Machine Learning in Medical ImagingMedical Imaging Techniques and ApplicationsLung Cancer Diagnosis and Treatment