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Image perception, observer performance, and technology assessment have driven many of the advances in breast imaging. Technology assessment metrics were used to develop mammography systems, first with screen-film mammography and then to digital mammography and digital breast tomosynthesis. To optimize these systems clinically, it became necessary to determine what type of information a radiologist needed to make a correct diagnosis. Image perception studies helped define what spatial frequencies were necessary to detect breast cancers and how different sources of noise affected detectability. Finally, observer performance studies were used to show that advances in the imaging system led to better detection and diagnoses by radiologists. In parallel to these developments, these three concepts were used to develop computer-aided diagnosis systems. In this talk, I will highlight how image perception, observer performance, and technology assessment were leveraged to produce technologies that allow radiologists to be highly effective in detecting breast cancer.
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