This short review aims at providing the readers with an update on the current status, as well as future perspectives in the quickly evolving field of radiomics applied to the field of PET/CT imaging. Numerous pitfalls have been identified in study de...
This paper presents a review of deep learning (DL)-based medical image registration methods. We summarized the latest developments and applications of DL-based registration methods in the medical field. These methods were classified into seven catego...
Journal of the American College of Radiology : JACR
Oct 20, 2020
Although artificial intelligence (AI)-based algorithms for diagnosis hold promise for improving care, their safety and effectiveness must be ensured to facilitate wide adoption. Several recently proposed regulatory frameworks provide a solid foundati...
Machine learning (ML) techniques are increasingly being used in clinical medical imaging to automate distinct processing tasks. In post-mortem forensic radiology, the use of these algorithms presents significant challenges due to variability in organ...
Artificial intelligence (AI) is an overarching term for a multitude of technologies which are currently being discussed and introduced in several areas of medicine and in medical imaging specifically. There is, however, limited literature and informa...
Identification and classification of leukemia cells in a rapid and label-free fashion is clinically challenging and thus presents a prime arena for implementing new diagnostic tools. Quantitative phase imaging, which maps optical path length delays i...
Scarce or absent radiology resources impede adoption of artificial intelligence (AI) for medical imaging by resource-poor health institutions. They face limitations in local equipment, personnel expertise, infrastructure, data-rights frameworks, and ...
Development of world-class artificial intelligence (AI) for medical imaging requires access to massive amounts of training data from clinical sources, but effective data sharing is often hindered by uncertainty regarding data protection. We describe ...
Health data that are publicly available are valuable resources for digital health research. Several public datasets containing ophthalmological imaging have been frequently used in machine learning research; however, the total number of datasets cont...
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