Changes in breast cancer detection and mammography recall rates after the introduction of a computer-aided detection system.

By D. Gur, J. Sumkin, H. Rockette, M. Ganott, C. Hakim, L. Hardesty, W. Poller, Ratan Shah, Luisa P. Wallace. Journal of the National Cancer Institute, 2004

333 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Oncology, Pulmonary and Respiratory Medicine

View Paper: https://www.semanticscholar.org/paper/40bfd373950216ff995eddd1be2d70d2bb769e6c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Oncology

In Pulmonary and Respiratory Medicine

Citations in the corpus

Current status and future potential of computer-aided diagnosis in medical imaging. (2005, background): “Because a number of commercial CAD systems for detection of breast cancer on mammograms are currently available for clinical use, some recent research on mammographic CAD has been focused on clinical studies evaluating the effect of CAD on radiologists’ performance and on clinical outcomes [83–88]; ”
A review of automatic mass detection and segmentation in mammographic images (2010, background): “These are only two examples of a still open debate in the medical imaging ﬁeld (Gur et al., 2004; Nishikawa and Kallergi, 2006; Fenton et al., 2007).”
Computer-aided detection/diagnosis of breast cancer in mammography and ultrasound: a review. (2013, background): “The organs that are mostly studied by CAD include lung [14–16], colon [17], and breast [18–24], but other organs such as liver, brain, and vascular systems recently have also been investigated through this method [25].”
Computer-aided diagnosis in medical imaging: Historical review, current status and future potential (2007, background): “[2] Gur D, Sumkin JH, Rockette HE, Ganott M, Hakim C, Hardesty L, et al.”
Computer aided detection (CAD): an overview (2005, methodology): “Another approach is the ‘historical control’ trial, in which the percentage change in disease detection and recall/workup rates is determined by comparing data before and after the implementation of CAD into a clinical practice [6,7] .”
Why CAD Failed in Mammography. (2018, background): “Because the computer sees more cancers during its training than its test, there is veri ﬁ cation bias, and the speci ﬁ city drifts [14,15].”