Detecting and classifying lesions in mammograms with Deep Learning

Abstract

In the last two decades, Computer Aided Detection (CAD) systems were developed to help radiologists analyse screening mammograms, however benefits of current CAD technologies appear to be contradictory, therefore they should be improved to be ultimately considered useful. Since 2012, deep convolutional neural networks (CNN) have been a tremendous success in image recognition, reaching human performance. These methods have greatly surpassed the traditional approaches, which are similar to currently used CAD solutions. Deep CNN-s have the potential to revolutionize medical image analysis. We propose a CAD system based on one of the most successful object detection frameworks, Faster R-CNN. The system detects and classifies malignant or benign lesions on a mammogram without any human intervention. The proposed method sets the state of the art classification performance on the public INbreast database, AUC = 0.95. The approach described here has achieved 2nd place in the Digital Mammography DREAM Challenge with AUC = 0.85. When used as a detector, the system reaches high sensitivity with very few false positive marks per image on the INbreast dataset. Source code, the trained model and an OsiriX plugin are published online at https://github.com/riblidezso/frcnn_cad .

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References 43

1. 1.

   Ferlay, J., Héry, C., Autier, P. & Sankaranarayanan, R. Global burden of breast cancer. In Breast cancer epidemiology, 1–19 (Springer, 2010).

   Not in our library

2. 2.

   Broeders M, et al. The impact of mammographic screening on breast cancer mortality in europe: a review of observational studies. Journal of medical screening. 2012;19:14-25.

   Not in our library10.1258/jms.2012.012078

3. 3.

   Ponti, A. et al. Cancer screening in the european union. final report on the implementation of the council recommendation on cancer screening (2017).

   Not in our library

4. 4.

   Bae MS, et al. Breast cancer detected with screening us: reasons for nondetection at mammography. Radiology. 2014;270:369-377.

   Not in our library10.1148/radiol.13130724

5. 5.

   Bird RE, Wallace TW, Yankaskas BC. Analysis of cancers missed at screening mammography. Radiology. 1992;184:613-617.

   Not in our library10.1148/radiology.184.3.1509041

6. 6.

   Birdwell RL, Ikeda DM, O’Shaughnessy KF, Sickles EA. Mammographic characteristics of 115 missed cancers later detected with screening mammography and the potential utility of computer-aided detection 1. Radiology. 2001;219:192-202.

   Not in our library10.1148/radiology.219.1.r01ap16192

7. 7.

   Harvey JA, Fajardo LL, Innis CA. Previous mammograms in patients with impalpable breast carcinoma: retrospective vs blinded interpretation. 1993 arrs president’s award. AJR. American journal of roentgenology. 1993;161:1167-1172.

   Not in our library10.2214/ajr.161.6.8249720

8. 8.

   Hoff SR, et al. Breast cancer: missed interval and screening-detected cancer at full-field digital mammography and screen-film mammography—results from a retrospective review. Radiology. 2012;264:378-386.

   Not in our library10.1148/radiol.12112074

9. 9.

   Martin JE, Moskowitz M, Milbrath JR. Breast cancer missed by mammography. American Journal of Roentgenology. 1979;132:737-739.

   Not in our library10.2214/ajr.132.5.737

10. 10.

    Banks E, et al. Influence of personal characteristics of individual women on sensitivity and specificity of mammography in the million women study: cohort study. Bmj. 2004;329:477.

    Not in our library10.1136/bmj.329.7464.477

11. 11.

    Lehman CD, et al. Diagnostic accuracy of digital screening mammography with and without computer-aided detection. JAMA internal medicine. 2015;175:1828-1837.

    Not in our library10.1001/jamainternmed.2015.5231

12. 12.

    Smith-Bindman R, et al. Physician predictors of mammographic accuracy. Journal of the National Cancer Institute. 2005;97:358-367.

    Not in our library10.1093/jnci/dji060

13. 13.

    Blanks R, Wallis M, Moss S. A comparison of cancer detection rates achieved by breast cancer screening programmes by number of readers, for one and two view mammography: results from the uk national health service breast screening programme. Journal of Medical screening. 1998;5:195-201.

    Not in our library10.1136/jms.5.4.195

14. 14.

    Karssemeijer, N., Otten, J. D., Roelofs, A. A., van Woudenberg, S. & Hendriks, J. H. Effect of independent multiple reading of mammograms on detection performance. In Medical Imaging 2004, 82–89 (International Society for Optics and Photonics, 2004).

    Not in our library

15. 15.

    Christoyianni I, Koutras A, Dermatas E, Kokkinakis G. Computer aided diagnosis of breast cancer in digitized mammograms. Computerized medical imaging and graphics. 2002;26:309-319.

    Not in our library10.1016/s0895-6111(02)00031-9

16. 16.

    Brem RF, et al. Improvement in sensitivity of screening mammography with computer-aided detection: a multiinstitutional trial. American Journal of Roentgenology. 2003;181:687-693.

    Not in our library10.2214/ajr.181.3.1810687

17. 17.

    Ciatto S, et al. Comparison of standard reading and computer aided detection (cad) on a national proficiency test of screening mammography. European journal of radiology. 2003;45:135-138.

    Not in our library10.1016/s0720-048x(02)00011-6

18. 18.

    Freer TW, Ulissey MJ. Screening mammography with computer-aided detection: prospective study of 12,860 patients in a community breast center. Radiology. 2001;220:781-786.

    Not in our library10.1148/radiol.2203001282

19. 19.

    Morton MJ, Whaley DH, Brandt KR, Amrami KK. Screening mammograms: interpretation with computer-aided detection—prospective evaluation. Radiology. 2006;239:375-383.

    Not in our library10.1148/radiol.2392042121

20. 20.

    Warren Burhenne LJ, et al. Potential contribution of computer-aided detection to the sensitivity of screening mammography 1. Radiology. 2000;215:554-562.

    Not in our library10.1148/radiology.215.2.r00ma15554

21. 21.

    Gilbert FJ, et al. Single reading with computer-aided detection for screening mammography. New England Journal of Medicine. 2008;359:1675-1684.

    Not in our library10.1056/nejmoa0803545

22. 22.

    Fenton JJ, et al. Influence of computer-aided detection on performance of screening mammography. New England Journal of Medicine. 2007;356:1399-1409.

    Not in our library10.1056/nejmoa066099

23. 23.

    Fenton JJ, et al. Effectiveness of computer-aided detection in community mammography practice. Journal of the National Cancer institute. 2011;103:1152-1161.

    Not in our library10.1093/jnci/djr206

24. 24.

    Hologic. Understanding ImageChecker® CAD 10.0 User Guide – MAN-03682 Rev002 (2017).

    Not in our library

25. 25.

    Hupse R, Karssemeijer N. Use of normal tissue context in computer-aided detection of masses in mammograms. IEEE Transactions on Medical Imaging. 2009;28:2033-2041.

    Not in our library10.1109/tmi.2009.2028611

26. 26.

    Hupse R, et al. Standalone computer-aided detection compared to radiologists’ performance for the detection of mammographic masses. European radiology. 2013;23:93-100.

    Not in our library10.1007/s00330-012-2562-7

27. 27.

    Kooi T, et al. Large scale deep learning for computer aided detection of mammographic lesions. Medical image analysis. 2017;35:303-312.

    Not in our library10.1016/j.media.2016.07.007

28. 28.

    Krizhevsky, A., Sutskever, I. & Hinton, G. E. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, 1097–1105 (2012).

    Not in our library

29. 29.

    He, K., Zhang, X., Ren, S. & Sun, J. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In Proceedings of the IEEE international conference on computer vision, 1026–1034 (2015).

    Not in our library

30. 30.

    Becker, A. S. et al. Deep learning in mammography: Diagnostic accuracy of a multipurpose image analysis software in the detection of breast cancer. Investigative Radiology (2017).

    Not in our library10.1097/rli.0000000000000358

31. 31.

    Dhungel, N., Carneiro, G. & Bradley, A. P. Fully automated classification of mammograms using deep residual neural networks. In Biomedical Imaging (ISBI 2017), 2017 IEEE 14th International Symposium on, 310–314(IEEE, 2017).

    Not in our library

32. 32.

    Lotter, W., Sorensen, G. & Cox, D. A multi-scale cnn and curriculum learning strategy for mammogram classification. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, 169–177 (Springer, 2017).

    Not in our library

33. 33.

    DREAM. The digital mammography dream challenge. https://www.synapse.org/Digital_Mammography_DREAM_challenge (2017).

    Not in our library

34. 34.

    Trister, A. D., Buist, D. S. & Lee, C. I. Will machine learning tip the balance in breast cancer screening? JAMA oncology (2017).

    Not in our library10.1001/jamaoncol.2017.0473

35. 35.

    Heath, M., Bowyer, K., Kopans, D., Moore, R. & Kegelmeyer, W. P. The digital database for screening mammography. In Proceedings of the 5th international workshop on digital mammography, 212–218 (Medical Physics Publishing, 2000).

    Not in our library

36. 36.

    Moreira IC, et al. Inbreast: toward a full-field digital mammographic database. Academic radiology. 2012;19:236-248.

    Not in our library10.1016/j.acra.2011.09.014

37. 37.

    Ren, S., He, K., Girshick, R. & Sun, J. Faster r-cnn: Towards real-time object detection with region proposal networks. In Advances in neural information processing systems, 91–99 (2015).

    Not in our library10.1109/tpami.2016.2577031

38. 38.

    Simonyan, K. & Zisserman, A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014).

    Not in our library

39. 39.

    Jia, Y. et al. Caffe: Convolutional architecture for fast feature embedding. arXiv preprint arXiv:1408.5093 (2014).

    Not in our library

40. 40.

    Bunch, P. C., Hamilton, J. F., Sanderson, G. K. & Simmons, A. H. A free response approach to the measurement and characterization of radiographic observer performance. In Application of Optical Instrumentation in Medicine VI, 124–135 (International Society for Optics and Photonics, 1977).

    Not in our library

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