Improving computer-aided detection assistance in breast cancer screening by removal of obviously false-positive findings:

Medical Physics

Volumn 44, Issue 4, 2017, Pages 1390-1401

  Mordang, Jan Jurre ^a   Gubern Mérida, Albert ^a   Bria, Alessandro ^b   Tortorella, Francesco ^b   Den Heeten, Gerard ^c   Karssemeijer, Nico ^a  

^a RADBOUD UNIVERSITY MEDICAL CENTER   (Netherlands)

^b UNIVERSITY OF CASSINO AND SOUTHERN LAZIO   (Italy)

^c ACADEMIC MEDICAL CENTER   (Netherlands)

Author keywords

breast cancer screening; computer aided detection; false positives; mammography; microcalcifications

Indexed keywords

CALCIFICATION (BIOCHEMISTRY); COMPUTER AIDED DIAGNOSIS; DISEASES; LEARNING SYSTEMS;

BREAST CANCER SCREENING; COMPUTER AIDED DETECTION; COMPUTER AIDED DETECTION SYSTEMS; DETECTION ALGORITHM; DETECTION METHODS; FALSE POSITIVE; MACHINE LEARNING METHODS; MAMMOGRAPHY SCREENING; MICRO-CALCIFICATION[MC]; MICROCALCIFICATION DETECTION;

MAMMOGRAPHY;

ARTERY CALCIFICATION; ARTICLE; BREAST CALCIFICATION; BREAST CANCER; CANCER DIAGNOSIS; CANCER SCREENING; COMPUTER ASSISTED RADIOGRAPHY; DIAGNOSTIC TEST ACCURACY STUDY; DIGITAL MAMMOGRAPHY; FALSE POSITIVE RESULT; HUMAN; MACHINE LEARNING; MAMMOGRAPHY SYSTEM; RADIODIAGNOSIS; ALGORITHM; BREAST NEOPLASMS; CALCINOSIS; COMPLICATION; COMPUTER ASSISTED DIAGNOSIS; MAMMOGRAPHY; MASS SCREENING; PROCEDURES;

ALGORITHMS; BREAST NEOPLASMS; CALCINOSIS; DIAGNOSIS, COMPUTER-ASSISTED; FALSE POSITIVE REACTIONS; HUMANS; MAMMOGRAPHY; MASS SCREENING;

EID: 85021308516     PISSN: 00942405     EISSN: 24734209     Source Type: Journal    
DOI: 10.1002/mp.12152     Document Type: Article

References (35)

1. Tabár L, Fagerberg CJ, Gad A, et al., Reduction in mortality from breast cancer after mass screening with mammography. Randomised trial from the breast cancer screening working group of the swedish national board of health and welfare. Lancet. 1985; 1: 829-832.
2. Tabár L, Vitak B, Chen H-H, et al., Swedish two-county trial: Impact of mammographic screening on breast cancer mortality during 3 decades. Radiol. 2011; 260: 658-663.
3. Huynh PT, Jarolimek AM, Daye S., The false-negative mammogram. Radio-graphics. 1998; 18: 1137-54;quiz 1243-4.
4. Burhenne LJW, Wood SA, D'Orsi CJ, et al., Potential contribution of computer-aided detection to the sensitivity of screening mammography. Radiol. 2000; 215: 554-562.
5. Karssemeijer N, Bluekens AM, Beijerinck D, et al., Breast cancer screening results 5 yrs after introduction of digital mammography in a population-based screening program. Radiol. 2009; 253: 353-358.
6. Cole EB, Zhang Z, Marques HS, et al., Assessing the stand-alone sensitivity of computer- aided detection with cancer cases from the digital mammographic imaging screening trial. Am J Roentgenol. 2012; 199: W392-W401.
7. Nishikawa RM., Current status and future directions of computer-aided diagnosis in mammography. Comput Med Imaging Graph. 2007; 31: 224-235.
8. Eadie LH, Taylor P, Gibson AP, A systematic review of computer-assisted diagnosis in diagnostic cancer imaging. Eur J Radiol. 2012; 81: e70-e76.
9. Fenton JJ, Taplin SH, Carney PA, et al., Influence of computer-aided detection on performance of screening mammography. N Engl J Med. 2007; 356: 1399-1409.
10. Lehman CD, Wellman RD, Buist DSM, et al., Diagnostic accuracy of digital screening mammography with and without computer-aided detection. JAMA Intern Med. 2015; 175: 1828-1837.
11. Cupples TE, Cunningham JE, Reynolds JC, Impact of computer-aided detection in a regional screening mammography program. Am J Roentgenol. 2005; 185: 944-950.
12. Philpotts LE., Can computer-aided detection be detrimental to mammographic interpretation? Radiol. 2009; 253: 17-22.
13. Bria A, Karssemeijer N, Tortorella F., Learning from unbalanced data: A cascade-based approach for detecting clustered microcalcifications. Med Image Anal. 2013; 18: 241-252.
14. Friedman J, Hastie T, Tibshirani R., Special invited paper. Additive logistic regression: A statistical view of boosting. Ann Stat. 2000; 28: 337-374.
15. Papageorgiou C, Oren M, Poggio T., A general framework for object detection. In: Computer Vision, 1998. Sixth International Conference on Computer Vision, Bombay; 1998: 555-562.
16. Lienhart R, Maydt J., An extended set of Haar-like features for rapid object detection. In: Image Processing. 2002. Proceedings. 2002 International Conference on Image Processing, Vol. 1, Rochester, NY; 2002: I900-I903.
17. Veldkamp WJH, Karssemeijer N., Improved method for detection of microcalcification clusters in digital mammograms. In: Medical Imaging, Proceedings of the SPIE, Vol. 3661, San Diego, CA; 1999: 512-522.
18. Mordang J-J, Gubern-Mérida A, den Heeten G, Karssemeijer N., Reducing false positives of microcalcification detection systems by removal of breast arterial calcifications. Med Phys. 2016; 43: 1676-1687.
19. Hu M-K., Visual pattern recognition by moment invariants, computer methods in image analysis. IRE Trans Inf Theory. 1962; 8: 179-187.
20. Sklansky J., Finding the convex hull of a simple polygon. Pattern Recognit Lett. 1982; 1: 79-83.
21. Frangi AF, Niessen WJ, Hoogeveen RM, van Walsum T, Viergever MA, Quantitation of vessel morphology from 3d MRA. In:, Taylor C, Colchester A, eds. Medical Image Computing and Computer-Assisted Intervention, Lecture Notes in Computer Science, Vol. 1679, Berlin: Springer; 1999: 358-367.
22. Efron B, Tibshirani RJ., An introduction to the bootstrap, Vol. 57, Boca Raton, FL: CRC press; 1994.
23. Bornefalk H, Hermansson AB., On the comparison of froc curves in mammography CAD systems. Med Phys. 2005; 32: 412-417.
24. Samuelson F, Petrick N., Comparing image detection algorithms using resampling. In: IEEE International Symposium on Biomedical Imaging, Arlington, VA; 2006: 1312-1315.
25. Torralba A, Murphy K, Freeman W., Sharing visual features for multiclass and multiview object detection. IEEE Trans Pattern Anal Mach Intell. 2007; 29: 854-869.
26. Cortes C, Vapnik V., Support-vector networks. Mach Learn. 1995; 20: 273-97.
27. Breiman L., Random forest. Mach Learn. 2001; 45: 5-32.
28. Hsu C-W, Chang V, Lin C-J., A practical guide to support vector classification, 2010.
29. Horsch K, Pesce LL, Giger ML, Metz CE, Jiang Y., A scaling transformation for classifier output based on likelihood ratio: Applications to a cad workstation for diagnosis of breast cancer. Med Phys. 2012; 39: 2787-2804.
30. He X, Samuelson F, Gallas BD, Sahiner B, Myers K., The equivalence of a human observer and an ideal observer in binary diagnostic tasks. In: Medical Imaging. Lake Buena Vista, FL: International Society for Optics and Photonics; 2013: 86730E-86730E.
31. Veldkamp WJ, Karssemeijer V, Otten JD, Hendriks JH., Automated classification of clustered microcalcifications into malignant and benign types. Med Phys. 2000; 27: 2600-2608.
32. Wei L, Yang Y, Nishikawa RM, Jiang Y., A study on several machine-learning methods for classification of malignant and benign clustered microcalcifications. IEEE Trans Med Imaging. 2005; 24: 371-380.
33. Elangeeran M, Ramasamy S, Arumugam K., A novel method for benign and malignant characterization of mammographic microcalcifications employing waveatom features and circular complex valued-extreme learning machine. In: Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), 2014 IEEE Ninth International Conference on Singapore; 2014: 1-6.
34. van Luijt PA, Fracheboud J, Heijnsdijk EAM, den Heeten GJ, de Koning HJ, National Evaluation Team for Breast Cancer Screening in Netherlands Study Group (NETB), Nation-wide data on screening performance during the transition to digital mammography: Observations in 6 million screens. Eur J Cancer. 2013; 49: 3517-3525.
35. Brown J, Bryan S, Warren R., Mammography screening: An incremental cost effectiveness analysis of double versus single reading of mammograms. Br Med J. 1996; 312: 809-812.