Guest Editors
Dr. Sudipta Roy, Washington University, USA.
Dr. Tai-hoon Kim, Konkuk University, Korea.
Dr. Javad Rahebi, Altinbas University, Turkey.
Dr. Mamta Mittal, G.B. PANT Government Engineering College, India.
Summary
The radiologists are overwhelmed with scientific literature, rapidly evolving treatment techniques, and the exponentially increasing amount of preclinical and clinical data. Translating all these data into knowledge that supports decision-making in routine clinical practice is required. In 2017, the American College of Radiology has established the Data Science Institute with a core purpose to empower the advancement, validation, and implementation of machine intelligence (MI) in medical imaging and radiological science for the benefit of patients, society, and the profession. A machine learning (ML) algorithm are in a sense “soft-coded”, as they automatically alter or adapt their architecture through repetition (i.e., experience) so that they become better and better at achieving the desired task.
This Special Section in IEEE Access aims to provide both the opportunities and challenges posed to radiological research by increasing the ability to tackle large datasets. “Big data” analytics in this context: recent advances may appear to promise a revolution, sweeping away conventional approaches to medical science. However, their real promise lies in their synergy with, not a replacement of, classical hypothesis-driven methods. The generation of novel, data-driven hypotheses based on interpretable models will always require stringent validation and experimental testing. Thus, hypothesis-generating research founded on large datasets adds to, rather than replaces, traditional hypothesis-driven science. Each can benefit from the other and it is through using both that we can improve clinical practice.
In the big data era, the utilization of MI algorithms in radiological imaging research is an emerging fields. Its applications include treatment response modeling, treatment planning, organ segmentation, image-guidance, motion tracking, quality assurance, and more. ML techniques could compensate for human limitations in handling a large amount of flowing information in an efficient manner, in which simple errors can make the difference between life and death.
Nowadays, most developed countries in the world are working to empower the advancement, validation, and implementation of MI in large amount of radiological imaging and radiological science for the benefit of patients, society, and the profession. In this special issue, we want to provide ongoing advances and cutting-edge applications of the MI methods for large data set towards precision medicine.
The topics of interest include, but are not limited to:
• Treatment delivery using big radiomics analysis
• Patient follow-up using big deep analysis
• Treatment outcome using big data analytics
• Radiomics for “precision medicine” radiotherapy
• Deep learning in precision medicine
• Advance machine learning in medicine
• Radiomics in precision medicine
• Bigdata analytics in medicine
• Advanced machine intelligence in health care informatics
• Patient diagnosis, assessment, and consultation in bigdata
• Computer-aided detection
• Computer-aided diagnosis
• Assessment and consultation
• Treatment simulation using deep learning
• image acquisition
• Image reconstruction
• Image registration/fusion
• Image segmentation/auto-contouring
• Knowledge-based treatment planning
• Automated planning of the diagnostic process
• Quality assurance using big quantitative analysis
• Treatment delivery using big radiomics analysis
• Patient follow-up using big deep analysis
• Treatment outcome
• Radiomics for “precision medicine” radiotherapy
• Deep learning in precision medicine
• Advance machine learning in medicine
• Radiomics in precision medicine
• Bigdata analytics in medicine
• Advanced machine intelligence in health care informatics
Keywords
• Big Data
• Biomedical Engineering
• Computational and Artificial Intelligence
• Medical imaging
• Quantitative imaging
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