There are ongoing community-wide efforts in terms of big data in medical imaging (MI) have made available and established validation frameworks used as a benchmark for the evaluation of different algorithms. Computer vision (CV) based models have indicated superiority among the other alternatives for most prediction tasks in radiology. Specifically in CV, using deep learning requires a lot of annotated datasets (across multiple institutions) to tune the algorithm (even when transfer learning is used) to obtain high prediction accuracy. There is no one algorithm works best for every problem (“No Free Lunch”). Each MI algorithm has its strengths and limitations. The development of accurate and CV models using different ML architectures is an active area of research. As with any algorithm that we use in radiation oncology today (e.g., dose calculation or deformable registration), ML algorithms will need acceptance, commissioning, and queries to ensure that the right algorithms or models are applied to the right application and that the model results make sense in each clinical situation. The field of radiation oncology is highly algorithmic and data-centric, and while the road ahead is filled with potholes, the destination holds tremendous promise.

The MI systems have been developed and deployed to do jobs on their own. Automated clinical processes in radiology could be auto-piloted with driving technologies to execute automated tasks. For example, data-driven planning is not fully automated at present as it requires expert oversight and/or intervention to ensure safely deliverable treatment plans. One challenge of achieving fully automatic planning using reinforcement learning lies in the close integration and the need for robustness. The automated process nature would lead to expediting radiation oncology workflow and reduce the time burden of human intervention.

In this Special Issue, our aim is to provide researchers and practitioners a platform to present innovative solutions based on the computer vision aspect of medical imaging for precision medicine. The focus of this Special Issue is to address the current research challenges by welcoming original research and review articles related to advanced big data analytics in radiological imaging for precision medicine.

Potential topics include but are not limited to the following:

• Treatment delivery using big radiomics analysis
• Patient follow-up using big deep analysis
• Patient diagnosis, assessment, and consultation in bigdata
• Computer-aided detection and diagnosis
• Treatment simulation using computer vision
• Image registration/fusion
• Image segmentation/auto-contouring
• Quality assurance using big quantitative analysis
• Treatment delivery using big radiomics analysis
• Treatment outcome, planning and visualization
• Deep learning in precision medicine
• Advance machine learning in medicine
• Big data analytics in medicine