Journal ArticleDOI
Computer-aided mammographic screening for spiculated lesions.
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TLDR
It is demonstrated that computer analysis of mammograms can provide a substantial and statistically significant increase in radiologist screening efficacy.Abstract:
PURPOSE: To study the use of a computer vision method as a second reader for the detection of spiculated lesions on screening mammograms. MATERIALS AND METHODS: An algorithmic computer process for the detection of spiculated lesions on digitized screen-film mammograms was applied to 85 four-view clinical cases: 36 cases with cancer proved by means of biopsy and 49 cases with negative findings at examination and follow-up. The computer detections were printed as film with added outlines that indicated the suspected cancers. Four radiologists screened the 85 cases twice, once without and once with the computer reports as ancillary films. RESULTS: The algorithm alone achieved 100% sensitivity, with a specificity of 82%. The computer reports increased the average radiologist sensitivity by 9.7% (P = .005), moving from 80.6% to 90.3%, with no decrease in average specificity. CONCLUSION: The study demonstrated that computer analysis of mammograms can provide a substantial and statistically significant increase ...read more
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Journal ArticleDOI
Screening mammography with computer-aided detection: prospective study of 12,860 patients in a community breast center.
TL;DR: The use of CAD in the interpretation of screening mammograms can increase the detection of early-stage malignancies without undue effect on the recall rate or positive predictive value for biopsy.
Journal ArticleDOI
Computer-Aided Detection and Diagnosis of Breast Cancer With Mammography: Recent Advances
TL;DR: An overview of recent advances in the development of CAD systems and related techniques for breast cancer detection and diagnosis focuses on key CAD techniques developed recently, including detection of calcifications, detection of masses, Detection of architectural distortion, detectionof bilateral asymmetry, image enhancement, and image retrieval.
Journal ArticleDOI
Mammographic Characteristics of 115 Missed Cancers Later Detected with Screening Mammography and the Potential Utility of Computer-aided Detection
TL;DR: In this article, a multicenter retrospective study accrued 1,083 consecutive cases of breast cancer detected at screening mammography and evaluated the ability of computer-aided detection (CAD) to mark the missed cancers.
Journal ArticleDOI
Current status and future potential of computer-aided diagnosis in medical imaging.
TL;DR: A number of CAD schemes are presented, with emphasis on potential clinical applications, including detection and classification of lung nodules on digital chest radiographs and quantitative analysis of diffuse lung diseases on high resolution CT.
Journal ArticleDOI
Classification of mass and normal breast tissue: a convolution neural network classifier with spatial domain and texture images
Berkman Sahiner,Heang Ping Chan,Nicholas Petrick,Datong Wei,Mark A. Helvie,Dorit D. Adler,Mitchell M. Goodsitt +6 more
TL;DR: The authors' results demonstrate the feasibility of using a convolution neural network for classification of masses and normal tissue on mammograms using a generalized, fast and stable implementation of the CNN.