• Jeremy R Burt, Neslisah Torosdagli, Naji Khosravan, Harish RaviPrakash, Aliasghar Mortazi, Fiona Tissavirasingham, Sarfaraz Hussein, and Ulas Bagci.
• 1 Department of Radiology, Florida Hospital , Orlando, FL , USA.
• Br J Radiol. 2018 Sep 1; 91 (1089): 20170545.

AbstractDeep learning has demonstrated tremendous revolutionary changes in the computing industry and its effects in radiology and imaging sciences have begun to dramatically change screening paradigms. Specifically, these advances have influenced the development of computer-aided detection and diagnosis (CAD) systems. These technologies have long been thought of as "second-opinion" tools for radiologists and clinicians. However, with significant improvements in deep neural networks, the diagnostic capabilities of learning algorithms are approaching levels of human expertise (radiologists, clinicians etc.), shifting the CAD paradigm from a "second opinion" tool to a more collaborative utility. This paper reviews recently developed CAD systems based on deep learning technologies for breast cancer diagnosis, explains their superiorities with respect to previously established systems, defines the methodologies behind the improved achievements including algorithmic developments, and describes remaining challenges in breast cancer screening and diagnosis. We also discuss possible future directions for new CAD models that continue to change as artificial intelligence algorithms evolve.

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