Mammographic characteristics of 115 missed cancers later detected with screening mammography and the potential utility of computer-aided detection.

By R. Birdwell, D. Ikeda, K. O'Shaughnessy, E. Sickles. Radiology, 2001

472 citations recorded by the source corpus.

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A review of computer-aided diagnosis of breast cancer: Toward the detection of subtle signs (2007, background): “Birdwell et al. [22] evaluated the performance of a commercial CAD system in marking cancers that were overlooked by radiologists: the software detected ﬁve out of six cases of architectural distortion, and 77% of the previously missed lesions, at 2.9 false positives per image.”
A review of automatic mass detection and segmentation in mammographic images (2010, background): “However, it is also well known that expert radiologists can miss a signiﬁcant proportion of abnormalities (Bird et al., 1992; Birdwell et al., 2001).”
Detecting and classifying lesions in mammograms with Deep Learning (2017, background): “These controversial results indicate that CAD systems need to be improved before radiologists can ultimately beneﬁt from using the technology in everyday practice.”