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by Michalis Vrigkas (Guest Editor), Christophoros Nikou (Guest Editor), Ioannis A. Kakadiaris (Guest Editor)
Rapid growth in algorithms and computing power over recent years has spurred the emergence of machine learning and image processing techniques as new tools, which are rapidly entering every aspect of our life, from intelligent personal assistance such as Siri, Alexa, and Google Home to self-driving cars. The medical community has begun taking advantage of these new possibilities to create new predictive models and improve existing models. For example, novel methods applying machine learning and image processing/analysis methods that are robust and theoretically sound to solve the learning task efficiently and intuitively have become widespread across different facets of biomedical imaging for identifying complex patterns. Likewise, advances in biomedical imaging may lead to new technologies for developing predictive models for all diseases and guide the decision of who should receive preventive therapy.
