Journal
PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS
Volume 83, Issue -, Pages 242-256Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.ejmp.2021.04.016
Keywords
Artificial intelligence; Medical imaging; Machine learning; Deep learning
Funding
- Walloon region in Belgium (PROTHERWAL/CHARP) [7289]
- National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health [K08EB026500]
- National Institutes of Health (NIH) [R01CA237269]
- Cancer Prevention & Research Institute of Texas (CPRIT) [IIRA RP150485]
- Walloon region (MECATECH/BIOWIN) [8090]
- research foundationFlanders (FWO) [1SA6121N]
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Artificial intelligence has become a popular buzzword in recent years, particularly in the field of medical imaging analysis, where it is now a mainstream tool for tasks such as diagnosis, segmentation, and classification. Ensuring the safe and efficient use of clinical AI applications relies on informed practitioners who understand the basic technological pillars of AI and the state-of-the-art machine learning methods.
Artificial intelligence (AI) has recently become a very popular buzzword, as a consequence of disruptive technical advances and impressive experimental results, notably in the field of image analysis and processing. In medicine, specialties where images are central, like radiology, pathology or oncology, have seized the opportunity and considerable efforts in research and development have been deployed to transfer the potential of AI to clinical applications. With AI becoming a more mainstream tool for typical medical imaging analysis tasks, such as diagnosis, segmentation, or classification, the key for a safe and efficient use of clinical AI applications relies, in part, on informed practitioners. The aim of this review is to present the basic technological pillars of AI, together with the state-of-the-art machine learning methods and their application to medical imaging. In addition, we discuss the new trends and future research directions. This will help the reader to understand how AI methods are now becoming an ubiquitous tool in any medical image analysis workflow and pave the way for the clinical implementation of AI-based solutions.
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