Comparison of Two Commercial CAD Systems for Digital Mammography

Leon, Stephanie; Brateman, Libby; Honeyman‐Buck, Janice C.; Marshall, Julia

doi:10.1007/s10278-008-9144-x

Cited by 9 publications

(4 citation statements)

References 5 publications

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“…In mammography, commercial CAD systems currently have about 1-3 marks per case. 17 A step of false positive reduction on initial voxel candidate suggested by other work [9][10][11] can be performed to achieve high sensitivity at an operating point with low FPs/volume. F.…”

Section: Discussionmentioning

confidence: 99%

Computer‐aided detection of breast cancers using Haar‐like features in automated 3D breast ultrasound

et al. 2015

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Section: Discussionmentioning

confidence: 99%

Computer‐aided detection of breast cancers using Haar‐like features in automated 3D breast ultrasound

et al. 2015

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“…Recent studies estimate a range of 0.45 to 0.55 false positive marks per image from digital mammograms [26,30], indicating that the false positive rate is comparable between film screen and digital mammograms with a mean of 2-3 marks per four-image mammogram. With v7.2, The et al [28] reported a mean false positive rate of 2.3 false positives per four-image case with digital mammography, which is the same as our mean of 2.3 false positives per four-image case with screen-film mammography.…”

Section: Discussionmentioning

confidence: 99%

“…False positive rate is commonly reported in the CAD literature, with earlier studies reporting means of 3-5 false positives per four-image case [3][4][5][22][23][24][25] and more recent studies reporting means of 2-3 false positives per fourimage case [26][27][28][29][30]. Recent studies estimate a range of 0.45 to 0.55 false positive marks per image from digital mammograms [26,30], indicating that the false positive rate is comparable between film screen and digital mammograms with a mean of 2-3 marks per four-image mammogram.…”

Section: Discussionmentioning

confidence: 99%

“…More recent CAD studies, some of which involved digital mammograms, have reported a mean of two false positive CAD marks per four-image mammogram [26][27][28][29][30]. In addition to retrospectively assessing the specificity and rate of false positive marks in two versions of CAD in screening mammography among unsuspicious cases in a university clinic setting, the purpose of this article is to assess the reviewing radiologist's ease of dismissing CAD false positives.…”

Section: Introductionmentioning

confidence: 99%

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False Positive Marks on Unsuspicious Screening Mammography with Computer-Aided Detection

Mahoney

Meganathan

2011

J Digit Imaging

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The contribution of computer-aided detection (CAD) systems as an interpretive aid in screening mammography can be hampered by a high rate of false positive detections. Specificity, false positive rate, and ease of dismissing false positive marks from two CAD systems are retrospectively evaluated. One hundred screening mammographic studies with a BI-RADS assessment code of 1 or 2 and at least 2-year normal mammographic followup were retrospectively reviewed using two CAD systems. Breast density, CAD marks, and radiologist"s ease of dismissing false positive marks were recorded. Specificities from the two CAD versions considering all marks were 23% and 15% (p value=0.07); mass marks, 35% and 17% (p value<0.01); and calcification marks 62% and 75% (p value = 0.01). The two CAD versions did not differ regarding mean and median marks per case for all marks (2.3, 2.0 and 2.3, 2.0, p value=0.65) or mass marks (1.6, 1.0 and 1.8, 2.0, p value=0.15), but differed for calcification marks (0.8, 0 and 0.5, 0, p value<0.01). Slightly higher specificity and fewer marks per case observed in dense breasts did not reach statistical significance. The reviewing radiologist classified most marks from both CAD systems (84% and 88%) as very easy/easy to dismiss. The two CAD versions had small differences in specificity and false positive marks. Differences, although not statistically significant, in specificities and false positive rates between dense and non-dense breasts warrant further research. Most false positive marks are easily dismissed and should not affect clinical performance.

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Wavelet Convolution Neural Network for Classification of Spiculated Findings in Mammograms

Jasionowska

Gacek

2019

Advances in Intelligent Systems and Computing

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Comparison of Two Commercial CAD Systems for Digital Mammography

Cited by 9 publications

References 5 publications

Computer‐aided detection of breast cancers using Haar‐like features in automated 3D breast ultrasound

Computer‐aided detection of breast cancers using Haar‐like features in automated 3D breast ultrasound

False Positive Marks on Unsuspicious Screening Mammography with Computer-Aided Detection

Wavelet Convolution Neural Network for Classification of Spiculated Findings in Mammograms

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