Advertisement

Deep Learning in Breast Cancer Screening

  • Hugh HarveyEmail author
  • Andreas Heindl
  • Galvin Khara
  • Dimitrios Korkinof
  • Michael O’Neill
  • Joseph Yearsley
  • Edith Karpati
  • Tobias Rijken
  • Peter Kecskemethy
  • Gabor Forrai
Chapter

Abstract

Traditional computer aided detection (CAD) systems for breast cancer screening relied on machine learning with human-coded feature-engineering. They have largely failed to fulfill the promise of improving screening accuracy and workflow efficiency, and are often associated with increased recall rates and avoidable screening costs due to high instances of false positive markings. Advances in machine learning (such as deep learning) are on the cusp of providing more effective, more efficient, and even more patient-centric breast cancer screening support than ever before. By leveraging the consistent high sensitivity and specificity performance of autonomous systems, in combination with expert human oversight, the potential for efficient single-reader software-supported screening programs with low recall rates is on the horizon.

Keywords

Breast cancer Screening Mammography CAD Deep learning 

References

  1. 1.
    Ferlay J, Steliarova-Foucher E, Lortet-Tieulent J, Rosso S, Coebergh JWW, Comber H, Forman D, Bray F. Cancer incidence and mortality patterns in Europe: estimates for 40 countries in 2012. Eur J Cancer. 2013;49(6):1374–1403.CrossRefGoogle Scholar
  2. 2.
    Tabár L, Gad A, Holmberg LH, Ljungquist U, Fagerberg CJG, Baldetorp L, Gröntoft O, Lundström B, Månson JC, Eklund G, Day NE, Pettersson F. 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;325(8433):829–32.CrossRefGoogle Scholar
  3. 3.
    Lee CH, David Dershaw D, Kopans D, Evans P, Monsees B, Monticciolo D, James Brenner R, Bassett L, Berg W, Feig S, Hendrick E, Mendelson E, D’Orsi C, Sickles E, Burhenne LW. Breast cancer screening with imaging: recommendations from the society of breast imaging and the ACR on the use of mammography, breast MRI, breast ultrasound, and other technologies for the detection of clinically occult breast cancer. J Am Coll Radiol. 2010;7(1):18–27.PubMedCrossRefGoogle Scholar
  4. 4.
    Boyer B, Balleyguier C, Granat O, Pharaboz C. CAD in questions/answers: review of the literature. Eur J Radiol. 2009;69(1):24–33.PubMedCrossRefGoogle Scholar
  5. 5.
    Duijm LEM, Louwman MWJ, Groenewoud JH, Van De Poll-Franse LV, Fracheboud J, Coebergh JW. Inter-observer variability in mammography screening and effect of type and number of readers on screening outcome. Br J Cancer. 2009;100(6): 901–7.PubMedPubMedCentralCrossRefGoogle Scholar
  6. 6.
    Dinitto P, Logan-young W, Bonaccio E, Zuley ML, Willison KM. Breast imaging can computer-aided detection with double reading of screening mammograms help decrease the false-negative rate? Initial experience 1. Radiology. 2004;232(2):578–84.PubMedCrossRefGoogle Scholar
  7. 7.
    Beam CA, Sullivan DC, Layde PM. Effect of human variability on independent double reading in screening mammography. Acad Radiol. 1996;3(11): 891–7.PubMedCrossRefGoogle Scholar
  8. 8.
    Tice JA, Kerlikowske K. Screening and prevention of breast cancer in primary care. Prim Care. 2009;36(3):533–58.PubMedCrossRefPubMedCentralGoogle Scholar
  9. 9.
    Fletcher SW. Breast cancer screening: a 35-year perspective. Epidemiol Rev. 2011;33(1):165–75.PubMedCrossRefPubMedCentralGoogle Scholar
  10. 10.
    Hofvind S, Geller BM, Skelly J, Vacek PM. Sensitivity and specificity of mammographic screening as practised in Vermont and Norway. Br J Radiol. 2012;85(1020):e1226–32.PubMedPubMedCentralCrossRefGoogle Scholar
  11. 11.
    Domingo L, Hofvind S, Hubbard RA, Román M, Benkeser D, Sala M, Castells X. Cross-national comparison of screening mammography accuracy measures in U.S., Norway, and Spain. Eur Radiol. 2016;26(8):2520–8.PubMedCrossRefPubMedCentralGoogle Scholar
  12. 12.
    Langreth R. Too many mammograms. Forbes; 2009.Google Scholar
  13. 13.
    Taylor P, Champness J, Given-Wilson R, Johnston K, Potts H. Impact of computer-aided detection prompts on the sensitivity and specificity of screening mammography. Health Technol Assess. 2005;9(6):iii, 1–58.PubMedCrossRefPubMedCentralGoogle Scholar
  14. 14.
    Philpotts LE. Can computer-aided detection be detrimental to mammographic interpretation? Radiology. 2009;253(1):17–22.PubMedCrossRefPubMedCentralGoogle Scholar
  15. 15.
    Gilbert FJ, Astley SM, Gillan MGC, Agbaje OF, Wallis MG, James J, Boggis CRM, Duffy SW. Single reading with computer-aided detection for screening mammography. N Engl J Med. 2008;359(16):1675–84.PubMedCrossRefPubMedCentralGoogle Scholar
  16. 16.
    Gilbert FJ, Astley SM, Gillan MG, Agbaje OF, Wallis MG, James J, Boggis CR, Duffy SW. CADET II: a prospective trial of computer-aided detection (CAD) in the UK Breast Screening Programme. J Clin Oncol. 2008;26(15 suppl):508.CrossRefGoogle Scholar
  17. 17.
    Taylor P, Potts HWW. Computer aids and human second reading as interventions in screening mammography: two systematic reviews to compare effects on cancer detection and recall rate. Eur J Cancer. 2008;44(6):798–807.PubMedCrossRefPubMedCentralGoogle Scholar
  18. 18.
    Noble M, Bruening W, Uhl S, Schoelles K. Computer-aided detection mammography for breast cancer screening: systematic review and meta-analysis. Arch Gynecol Obstet. 2009;279(6):881–90.PubMedCrossRefPubMedCentralGoogle Scholar
  19. 19.
    Karssemeijer N, Bluekens AM, Beijerinck D, Deurenberg JJ, Beekman M, Visser R, van Engen R, Bartels-Kortland A, Broeders MJ. Breast cancer screening results 5 years after introduction of digital mammography in a population-based screening program. Radiology. 2009;253(2):353–8.PubMedCrossRefPubMedCentralGoogle Scholar
  20. 20.
    Destounis S, Hanson S, Morgan R, Murphy P, Somerville P, Seifert P, Andolina V, Arieno A, Skolny M, Logan-Young W. Computer-aided detection of breast carcinoma in standard mammographic projections with digital mammography. Int J Comput Assist Radiol Surg. 2009;4(4):331–6.PubMedCrossRefGoogle Scholar
  21. 21.
    van den Biggelaar FJHM, Kessels AGH, Van Engelshoven JMA, Flobbe K. Strategies for digital mammography interpretation in a clinical patient population. Int J Cancer. 2009;125(12):2923–9.PubMedCrossRefGoogle Scholar
  22. 22.
    Sohns C, Angic B, Sossalla S, Konietschke F, Obenauer S. Computer-assisted diagnosis in full-field digital mammography-results in dependence of readers experiences. Breast J. 2010;16(5):490–7.PubMedCrossRefPubMedCentralGoogle Scholar
  23. 23.
    Murakami R, Kumita S, Tani H, Yoshida T, Sugizaki K, Kuwako T, Kiriyama T, Hakozaki K, Okazaki E, Yanagihara K, Iida S, Haga S, Tsuchiya S. Detection of breast cancer with a computer-aided detection applied to full-field digital mammography. J Digit Imaging. 2013;26(4):768–73.PubMedPubMedCentralCrossRefGoogle Scholar
  24. 24.
    Cole EB, Zhang Z, Marques HS, Edward Hendrick R, Yaffe MJ, Pisano ED. Impact of computer-aided detection systems on radiologist accuracy with digital mammography. Am J Roentgenol. 2014;203(4):909–16.CrossRefGoogle Scholar
  25. 25.
    Bargalló X, Santamaría G, Del Amo M, Arguis P, Ríos J, Grau J, Burrel M, Cores E, Velasco M. Single reading with computer-aided detection performed by selected radiologists in a breast cancer screening program. Eur J Radiol. 2014;83(11):2019–23.PubMedCrossRefPubMedCentralGoogle Scholar
  26. 26.
    Lehman CD, Wellman RD, Buist DSM, Kerlikowske K, Tosteson ANA, Miglioretti DL. Diagnostic accuracy of digital screening mammography with and without computer-aided detection. JAMA Intern Med. 2015;175(11):1828.PubMedPubMedCentralCrossRefGoogle Scholar
  27. 27.
    Berry DA. Computer-assisted detection and screening mammography: where’s the beef? J Natl Cancer Inst. 2011;103(15):1139–41.PubMedCrossRefPubMedCentralGoogle Scholar
  28. 28.
    Sanchez Gómez S, Torres Tabanera M, Vega Bolivar A, Sainz Miranda M, Baroja Mazo A, Ruiz Diaz M, Martinez Miravete P, Lag Asturiano E, Muñoz Cacho P, Delgado Macias T. Impact of a CAD system in a screen-film mammography screening program: a prospective study. Eur J Radiol. 2011;80(3):e317–21.PubMedCrossRefPubMedCentralGoogle Scholar
  29. 29.
    Freer TW, Ulissey MJ. Screening mammography with computer-aided detection: prospective study of 12,860 patients in a community breast center. Radiology. 2001;220(3):781–6.PubMedCrossRefPubMedCentralGoogle Scholar
  30. 30.
    The JS, Schilling KJ, Hoffmeister JW, Friedmann E, McGinnis R, Holcomb RG. Detection of breast cancer with full-field digital mammography and computer-aided detection. Am J Roentgenol. 2009;192(2):337–40.CrossRefGoogle Scholar
  31. 31.
    Rao VM, Levin DC, Parker L, Cavanaugh B, Frangos AJ, Sunshine JH. How widely is computer-aided detection used in screening and diagnostic mammography? J Am Coll Radiol. 2010;7(10):802–5.PubMedCrossRefGoogle Scholar
  32. 32.
    Onega T, Aiello Bowles EJ, Miglioretti DL, Carney PA, Geller BM, Yankaskas BC, Kerlikowske K, Sickles EA, Elmore JG. Radiologists’ perceptions of computer aided detection versus double reading for mammography interpretation. Acad Radiol. 2010;17(10):1217–26.PubMedPubMedCentralCrossRefGoogle Scholar
  33. 33.
    Kohli A, Jha S. Why CAD failed in mammography. J Am Coll Radiol. 2018;15(3 Pt B):535–7.PubMedCrossRefGoogle Scholar
  34. 34.
    Lehman CD, Arao RF, Sprague BL, Lee JM, Buist DSM, Kerlikowske K, Henderson LM, Onega T, Tosteson ANA, Rauscher GH, Miglioretti DL. National performance benchmarks for modern screening digital mammography: update from the breast cancer surveillance consortium. Radiology. 2017;283(1):49–58.PubMedCrossRefGoogle Scholar
  35. 35.
    Carney PA, Sickles EA, Monsees BS, Bassett LW, James Brenner R, Feig SA, Smith RA, Rosenberg RD, Andrew Bogart T, Browning S, Barry JW, Kelly MM, Tran KA, Miglioretti DL. Identifying minimally acceptable interpretive performance criteria for screening mammography. Radiology. 2010;255(2):354–61.PubMedPubMedCentralCrossRefGoogle Scholar
  36. 36.
    Miglioretti DL, Ichikawa L, Smith RA, Bassett LW, Feig SA, Monsees B, Parikh JR, Rosenberg RD, Sickles EA, Carney PA. Criteria for identifying radiologists with acceptable screening mammography interpretive performance on basis of multiple performance measures. Am J Roentgenol. 2015;204(4):W486–91.CrossRefGoogle Scholar
  37. 37.
    Myers ER, Moorman P, Gierisch JM, Havrilesky LJ, Grimm LJ, Ghate S, Davidson B, Mongtomery RC, Crowley MJ, McCrory DC, Kendrick A, Sanders GD. Benefits and harms of breast cancer screening: a systematic review. J Am Med Assoc. 2015;314:1615–34.CrossRefGoogle Scholar
  38. 38.
    Sahiner B, Chan HP, Petrick N, Wei D, Helvie MA, Adler DD, Goodsitt MM. Classification of mass and normal breast tissue: a convolution neural network classifier with spatial domain and texture images. IEEE Trans Med Imaging. 1996;15(5):598–610.PubMedCrossRefGoogle Scholar
  39. 39.
    Dhungel N, Carneiro G, Bradley AP. Automated mass detection from mammograms using deep learning and random forest. In: International conference on digital image computing: techniques and applications; 2015. p. 1–8.Google Scholar
  40. 40.
    Ertosun MG, Rubin DL. Probabilistic visual search for masses within mammography images using deep learning. In: IEEE international conference on bioinformatics and biomedicine; 2015. p. 1310–5.Google Scholar
  41. 41.
    Carneiro G, Nascimento J, Bradley AP. Unregistered multiview mammogram analysis with pre-trained deep learning models. In: Proceedings of the 18th international conference on medical image computing and computer-assisted intervention. Lecture notes in computer science. Vol 9351. Cham: Springer; 2015. p. 652–60.Google Scholar
  42. 42.
    Moreira IC, Amaral I, Domingues I, Cardoso A, Cardoso MJ, Cardoso JS. INbreast: toward a full-field digital mammographic database. Acad Radiol. 2012;19(2):236–48.PubMedCrossRefPubMedCentralGoogle Scholar
  43. 43.
    Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The cancer imaging archive (TCIA): maintaining and operating a public information repository. J Digit Imaging. 2013;26(6):1045–57.PubMedPubMedCentralCrossRefGoogle Scholar
  44. 44.
    Kooi T, Litjens G, van Ginneken B, Gubern-Mérida A, Sánchez CI, Mann R, den Heeten A, Karssemeijer N. Large scale deep learning for computer aided detection of mammographic lesions. Med Image Anal. 2017;35:303–12.PubMedCrossRefPubMedCentralGoogle Scholar
  45. 45.
    Teare P, Fishman M, Benzaquen O, Toledano E, Elnekave E. Malignancy detection on mammography using dual deep convolutional neural networks and genetically discovered false color input enhancement. J Digit Imaging. 2017;30(4): 499–505.PubMedPubMedCentralCrossRefGoogle Scholar
  46. 46.
    Kim E-K, Kim H-E, Han K, Kang BJ, Sohn Y-M, Woo OH, Lee CW. Applying data-driven imaging biomarker in mammography for breast cancer screening: preliminary study. Sci Rep. 2018;8(1):2762.PubMedPubMedCentralCrossRefGoogle Scholar
  47. 47.
    Elter M, Horsch A. CADx of mammographic masses and clustered microcalcifications: a review. Med Phys. 2009;36(6):2052–68.PubMedCrossRefPubMedCentralGoogle Scholar
  48. 48.
    Breast screening: consolidated programme standards - GOV.UK; 2017.Google Scholar
  49. 49.
    Rothschild J, Lourenco AP, Mainiero MB. Screening mammography recall rate: does practice site matter? Radiology. 2013;269(2):348–53.PubMedCrossRefPubMedCentralGoogle Scholar
  50. 50.
    Sage Bionetworks. The Digital Mammography DREAM Challenge; 2016.Google Scholar
  51. 51.
    Ribli D, Horváth A, Unger Z, Pollner P, Csabai I. Detecting and classifying lesions in mammograms with deep learning. Sci Rep. 2018;8(1):4165.PubMedPubMedCentralCrossRefGoogle Scholar
  52. 52.
    Dhungel N, Carneiro G, Bradley AP. The automated learning of deep features for breast mass classification from mammograms. In: International conference on medical image computing and computer-assisted intervention. Cham: Springer; 2016. p. 106–14.Google Scholar
  53. 53.
    Arevalo J, Gonzalez FA, Ramos-Pollan R, Oliveira JL, Lopez MAG. Convolutional neural networks for mammography mass lesion classification. In: IEEE Engineering in Medicine and Biology Society (EMBC). Washington: IEEE; 2015. p. 797–800.Google Scholar
  54. 54.
    Lévy D, Jain A. Breast mass classification from mammograms using deep convolutional neural networks; 2016. arxiv:1612.00542.Google Scholar
  55. 55.
    Chen L-C, Papandreou G, Kokkinos I, Murphy K, Yuille AL. DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans Pattern Anal Mach Intell. 2018;40(4):834–48.PubMedCrossRefPubMedCentralGoogle Scholar
  56. 56.
    Ren S, He K, Girshick R, Sun J. Faster R-CNN: towards real-time object detection with region proposal networks; 2016. arxiv:1506.01497.Google Scholar
  57. 57.
    Li Y, He K, Sun J. R-fcn: object detection via region-based fully convolutional networks. In: Advances in neural information processing systems; 2016.Google Scholar
  58. 58.
    Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C-Y, Berg AC. SSD: single shot multibox detector; 2016. arxiv:1512.02325.Google Scholar
  59. 59.
    Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation. In: Medical image computing and computer-assisted intervention – MICCAI 2015; 2015. p. 234–41.Google Scholar
  60. 60.
    Zhu W, Xiang X, Tran TD, Xie X. Adversarial deep structural networks for mammographic mass segmentation; 2017. arxiv:1612.05970.Google Scholar
  61. 61.
    de Moor T, Rodriguez-Ruiz A, Mérida AG, Mann R, Teuwen J. Automated soft tissue lesion detection and segmentation in digital mammography using a u-net deep learning network; 2018. arxiv:1802.06865.Google Scholar
  62. 62.
    Uijlings JRR, van de Sande KEA, Gevers T, Smeulders AWM. Selective search for object recognition. Int J Comput Vis. 2013;104(2):154–71.CrossRefGoogle Scholar
  63. 63.
    He K, Gkioxari G, Dollár P, Girshick R. Mask R-CNN; 2017. arxiv:1703.06870.Google Scholar
  64. 64.
    Assi V, Warwick J, Cuzick J, Duffy SW. Clinical and epidemiological issues in mammographic density. Nat Rev Clin Oncol. 2012;9(1):33–40.CrossRefGoogle Scholar
  65. 65.
    Colin C, Schott-Pethelaz A-M. Mammographic density as a risk factor: to go out of a 30-year fog. Acta Radiol. 2017;58(6):NP1.PubMedCrossRefGoogle Scholar
  66. 66.
    Colin C. Mammographic density: is there a public health significance linked to published relative risk data? Radiology. 2017;284(3):918–9.PubMedCrossRefGoogle Scholar
  67. 67.
    Martin LJ, Melnichouk O, Guo H, Chiarelli AM, Hislop TG, Yaffe MJ, Minkin S, Hopper JL, Boyd NF. Family history, mammographic density, and risk of breast cancer. Cancer Epidemiol Biomarkers Prev. 2010;19(2):456–63.PubMedCrossRefPubMedCentralGoogle Scholar
  68. 68.
    Shepherd JA, Kerlikowske K, Ma L, Duewer F, Fan B, Wang J, Malkov S, Vittinghoff E, Cummings SR. Volume of mammographic density and risk of breast cancer. Cancer Epidemiol Biomarkers Prev. 2011;20(7):1473–82.PubMedPubMedCentralCrossRefGoogle Scholar
  69. 69.
    Boyd N, Martin L, Gunasekara A, Melnichouk O, Maudsley G, Peressotti C, Yaffe M, Minkin S. Mammographic density and breast cancer risk: evaluation of a novel method of measuring breast tissue volumes. Cancer Epidemiol Biomarkers Prev. 2009;18(6):1754–62.PubMedCrossRefPubMedCentralGoogle Scholar
  70. 70.
    Aitken Z, McCormack VA, Highnam RP, Martin L, Gunasekara A, Melnichouk O, Mawdsley G, Peressotti C, Yaffe M, Boyd NF, dos Santos Silva I. Screen-film mammographic density and breast cancer risk: a comparison of the volumetric standard mammogram form and the interactive threshold measurement methods. Cancer Epidemiol Biomarkers Prev. 2010;19(2):418–28.PubMedPubMedCentralCrossRefGoogle Scholar
  71. 71.
    Gastounioti A, Conant EF, Kontos D. Beyond breast density: a review on the advancing role of parenchymal texture analysis in breast cancer risk assessment. Breast Cancer Res. 2016;18(1):91.PubMedPubMedCentralCrossRefGoogle Scholar
  72. 72.
    Astley SM, Harkness EF, Sergeant JC, Warwick J, Stavrinos P, Warren R, Wilson M, Beetles U, Gadde S, Lim Y, Jain A, Bundred S, Barr N, Reece V, Brentnall AR, Cuzick J, Howell T, Evans DG. A comparison of five methods of measuring mammographic density: a case-control study. Breast Cancer Res. 2018;20(1):10.PubMedPubMedCentralCrossRefGoogle Scholar
  73. 73.
    Manduca A, Carston MJ, Heine JJ, Scott CG, Pankratz VS, Brandt KR, Sellers TA, Vachon CM, Cerhan JR. Texture features from mammographic images and risk of breast cancer. Cancer Epidemiol Biomarkers Prev. 2009;18(3):837–45.PubMedPubMedCentralCrossRefGoogle Scholar
  74. 74.
    Li J, Szekely L, Eriksson L, Heddson B, Sundbom A, Czene K, Hall P, Humphreys K. High-throughput mammographic-density measurement: a tool for risk prediction of breast cancer. Breast Cancer Res. 2012;14(4):R114.PubMedPubMedCentralCrossRefGoogle Scholar
  75. 75.
    Häberle L, Wagner F, Fasching PA, Jud SM, Heusinger K, Loehberg CR, Hein A, Bayer CM, Hack CC, Lux MP, Binder K, Elter M, Münzenmayer C, Schulz-Wendtland R, Meier-Meitinger M, Adamietz BR, Uder M, Beckmann MW, Wittenberg T. Characterizing mammographic images by using generic texture features. Breast Cancer Res. 2012;14(2):R59.PubMedPubMedCentralCrossRefGoogle Scholar
  76. 76.
    Bott R. ACR BI-RADS atlas. In: Igarss 2014; 2014.Google Scholar
  77. 77.
    Gram IT, Funkhouser E, Tabár L. The Tabar classification of mammographic parenchymal patterns. Eur J Radiol. 1997;24:131–6.PubMedCrossRefGoogle Scholar
  78. 78.
    Petersen K, Nielsen M, Diao P, Karssemeijer N, Lillholm M. Breast tissue segmentation and mammographic risk scoring using deep learning. In: International workshop on breast imaging. Lecture notes in computer science. Vol 8539. Cham: Springer; 2014. p. 88–94.Google Scholar
  79. 79.
    Wu N, Geras KJ, Shen Y, Su J, Gene Kim S, Kim E, Wolfson S, Moy L, Cho K. Breast density classification with deep convolutional neural networks; 2017. arxiv:1711.03674.Google Scholar
  80. 80.
    Shin SY, Lee S, Yun ID, Jung HY, Heo YS, Kim SM, Lee SM. A novel cascade classifier for automatic microcalcification detection. Public Libr Sci. 2015;10(12):e0143725.Google Scholar
  81. 81.
    Chen T, Xu B, Zhang C, Guestrin C. Training deep nets with sublinear memory cost; 2016. arxiv:1604.06174.Google Scholar
  82. 82.
    Gomez AN, Ren M, Urtasun R, Grosse RB. The reversible residual network: backpropagation without storing activations; 2017. arxiv:1707.04585.Google Scholar
  83. 83.
    Lee RS, Gimenez F, Hoogi A, Miyake KK, Gorovoy M, Rubin DL. Data descriptor: a curated mammography data set for use in computer-aided detection and diagnosis research. Sci Data. 2017;4:170177.PubMedPubMedCentralCrossRefGoogle Scholar
  84. 84.
    Xi P, Shu C, Goubran R. Abnormality detection in mammography using deep convolutional neural networks; 2018. arxiv:1803.01906.Google Scholar
  85. 85.
    Chawla N, Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE: synthetic minority over-sampling technique. J Artif Intell Res. 2002;16:321–57.CrossRefGoogle Scholar
  86. 86.
    Keller BM, Nathan DL, Gavenonis SC, Chen J, Conant EF, Kontos D. Reader variability in breast density estimation from full-field digital mammograms: the effect of image postprocessing on relative and absolute measures. Acad Radiol. 2013;20(5):560–8.PubMedPubMedCentralCrossRefGoogle Scholar
  87. 87.
    Redondo A, Comas M, Macià F, Ferrer F, Murta-Nascimento C, Maristany MT, Molins E, Sala M, Castells X. Inter- and intraradiologist variability in the BI-RADS assessment and breast density categories for screening mammograms. Br J Radiol. 2012;85(1019):1465–70.PubMedPubMedCentralCrossRefGoogle Scholar
  88. 88.
    Lee AY, Wisner DJ, Aminololama-Shakeri S, Arasu VA, Feig SA, Hargreaves J, Ojeda-Fournier H, Bassett LW, Wells CJ, De Guzman J, Flowers CI, Campbell JE, Elson SL, Retallack H, Joe BN. Inter-reader variability in the use of BI-RADS descriptors for suspicious findings on diagnostic mammography: a multi-institution study of 10 academic radiologists. Acad Radiol. 2017;24(1):60–6.PubMedCrossRefPubMedCentralGoogle Scholar
  89. 89.
    Heath M, Bowyer K, Kopans D, Kegelmeyer P, Moore R, Chang K, Munishkumaran S. Current status of the digital database for screening mammography. In: Digital mammography. Dordrecht: Springer; 1998. p. 457–60.CrossRefGoogle Scholar
  90. 90.
    Gal Y, Ghahramani Z. Dropout as a Bayesian approximation: representing model uncertainty in deep learning; 2015. arxiv:1506.02142.Google Scholar
  91. 91.
    Kendall A, Gal Y. What uncertainties do we need in Bayesian deep learning for computer vision?; 2017. arxiv:1703.04977.Google Scholar
  92. 92.
    Guo C, Pleiss G, Sun Y, Weinberger KQ. On calibration of modern neural networks; 2017. arxiv:1706.04599.Google Scholar
  93. 93.
    Cobb AD, Roberts SJ, Gal Y. Loss-calibrated approximate inference in Bayesian neural networks; 2018. arxiv:1805.03901.Google Scholar
  94. 94.
    Nishikawa RM, Bae KT. Importance of better human-computer interaction in the era of deep learning: mammography computer-aided diagnosis as a use case. J Am Coll Radiol. 2018;15(1): 49–52.PubMedCrossRefPubMedCentralGoogle Scholar
  95. 95.
    Simonyan K, Vedaldi A, Zisserman A. Deep inside convolutional networks: visualising image classification models and saliency maps; 2013. arxiv:1312.6034.Google Scholar
  96. 96.
    Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y. Generative adversarial nets. In: Advances in neural information processing systems; 2014. p. 2672–80.Google Scholar
  97. 97.
    Kingma DP, Welling M. Auto-encoding variational Bayes. In: International conference on learning representations; 2014.Google Scholar
  98. 98.
    van den Oord A, Kalchbrenner N, Kavukcuoglu K. Pixel recurrent neural networks. In: International conference on machine learning. Vol 48; 2016. p. 1747–56.Google Scholar
  99. 99.
    Salehinejad H, Valaee S, Dowdell T, Colak E, Barfett J. Generalization of deep neural networks for chest pathology classification in X-rays using generative adversarial networks. In: IEEE international conference on acoustics, speech and signal processing (ICASSP); 2018.Google Scholar
  100. 100.
    Costa P, Galdran A, Meyer MI, Niemeijer M, Abramoff M, Mendonca AM, Campilho A. End-to-end adversarial retinal image synthesis. IEEE Trans Med Imaging. 2018;37(3):781–91.PubMedCrossRefGoogle Scholar
  101. 101.
    Korkinof D, Rijken T, O’Neill M, Yearsley J, Harvey H, Glocker B. High-resolution mammogram synthesis using progressive generative adversarial networks; 2018. arxiv:1807.03401.Google Scholar
  102. 102.
    Adiwardana D, et al. Using generative models for semi-supervised learning. In: Medical image computing and computer-assisted intervention – MICCAI 2016; 2016. p. 106–14.Google Scholar
  103. 103.
    Lahiri A, Ayush K, Biswas PK, Mitra P. Generative adversarial learning for reducing manual annotation in semantic segmentation on large scale microscopy images: automated vessel segmentation in retinal fundus image as test case. In: IEEE Computer Society conference on computer vision and pattern recognition workshops, July 2017; 2017. p. 794–800.Google Scholar
  104. 104.
    Kamnitsas K, Baumgartner C, Ledig C, Newcombe V, Simpson J, Kane A, Menon D, Nori A, Criminisi A, Rueckert D, Glocker B. Unsupervised domain adaptation in brain lesion segmentation with adversarial networks. In: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics). Lecture notes in computer science. Vol 10265. Cham: Springer; 2017. p. 597–609.Google Scholar
  105. 105.
    Wolterink JM, Leiner T, Viergever MA, Isgum I. Generative adversarial networks for noise reduction in low-dose CT. IEEE Trans Med Imaging. 2017;36(12):2536–45.PubMedPubMedCentralCrossRefGoogle Scholar
  106. 106.
    Gennaro G, Bernardi D, Houssami N. Radiation dose with digital breast tomosynthesis compared to digital mammography: per-view analysis. Eur Radiol. 2018;28(2):573–81.PubMedCrossRefGoogle Scholar
  107. 107.
    Vedantham S, Karellas A, Vijayaraghavan GR, Kopans DB. Digital breast tomosynthesis: state of the art. Radiology. 2015;277(3):663–84.PubMedPubMedCentralCrossRefGoogle Scholar
  108. 108.
    Gilbert FJ, Tucker L, Gillan MGC, Willsher P, Cooke J, Duncan KA, Michell MJ, Dobson HM, Lim YY, Suaris T, Astley SM, Morrish O, Young KC, Duffy SW. Accuracy of digital breast tomosynthesis for depicting breast cancer subgroups in a UK retrospective reading study (TOMMY trial). Radiology. 2015;277(3):697–706.PubMedCrossRefGoogle Scholar
  109. 109.
    Connor SJ, Lim YY, Tate C, Entwistle H, Morris J, Whiteside S, Sergeant J, Wilson M, Beetles U, Boggis C, Gilbert F, Astley S. A comparison of reading times in full-field digital mammography and digital breast tomosynthesis. Breast Cancer Res. 2012;14(S1):P26.CrossRefGoogle Scholar
  110. 110.
    Chan HP, Wei J, Zhang Y, Helvie MA, Moore RH, Sahiner B, Hadjiiski L, Kopans DB. Computer-aided detection of masses in digital tomosynthesis mammography: comparison of three approaches. Med Phys. 2008;35(9):4087–95.PubMedPubMedCentralCrossRefGoogle Scholar
  111. 111.
    Sahiner B, Chan HP, Hadjiiski LM, Helvie MA, Wei J, Zhou C, Lu Y. Computer-aided detection of clustered microcalcifications in digital breast tomosynthesis: a 3D approach. Med Phys. 2011;39(1):28–39.PubMedCentralCrossRefPubMedGoogle Scholar
  112. 112.
    Samala RK, Chan HP, Lu Y, Hadjiiski L, Wei J, Sahiner B, Helvie MA. Computer-aided detection of clustered microcalcifications in multiscale bilateral filtering regularized reconstructed digital breast tomosynthesis volume. Med Phys. 2014;41(2):021901.PubMedPubMedCentralCrossRefGoogle Scholar
  113. 113.
    Morra L, Sacchetto D, Durando M, Agliozzo S, Carbonaro LA, Delsanto S, Pesce B, Persano D, Mariscotti G, Marra V, Fonio P, Bert A. Breast cancer: computer-aided detection with digital breast tomosynthesis. Radiology. 2015;277(1): 56–63.PubMedCrossRefGoogle Scholar
  114. 114.
    Killelea BK, Chagpar AB, Bishop J, Horowitz NR, Christy C, Tsangaris T, Raghu M, Lannin DR. Is there a correlation between breast cancer molecular subtype using receptors as surrogates and mammographic appearance? Ann Surg Oncol. 2013;20(10):3247–53.PubMedCrossRefGoogle Scholar
  115. 115.
    Nguyen NG, Tran VA, Ngo DL, Phan D, Lumbanraja FR, Faisal MR, Abapihi B, Kubo M, Satou K. DNA sequence classification by convolutional neural network. J Biomed Sci Eng. 2016;9(9):280–6.CrossRefGoogle Scholar
  116. 116.
    Yin B, Balvert M, Zambrano D, Sander M, Wiskunde C. An image representation based convolutional network for DNA classification; 2018. arxiv:1806.04931.Google Scholar
  117. 117.
    Rutman AM, Kuo MD. Radiogenomics: creating a link between molecular diagnostics and diagnostic imaging. Eur J Radiol. 2009;70(2):232–41.PubMedCrossRefGoogle Scholar
  118. 118.
    Grimm LJ. Breast MRI radiogenomics: current status and research implications. J Magn Reson Imaging. 2016;43(6):1269–78.PubMedCrossRefGoogle Scholar
  119. 119.
    Incoronato M, Aiello M, Infante T, Cavaliere C, Grimaldi AM, Mirabelli P, Monti S, Salvatore M. Radiogenomic analysis of oncological data: a technical survey. Int J Mol Sci. 2017;18(4):pii: E805.Google Scholar
  120. 120.
    Perry N. European guidelines for quality assurance in breast cancer screening and diagnosis. Ann Oncol. 2006;12(4):295–9.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Hugh Harvey
    • 1
    Email author
  • Andreas Heindl
    • 1
  • Galvin Khara
    • 1
  • Dimitrios Korkinof
    • 1
  • Michael O’Neill
    • 1
  • Joseph Yearsley
    • 1
  • Edith Karpati
    • 1
  • Tobias Rijken
    • 1
  • Peter Kecskemethy
    • 1
  • Gabor Forrai
    • 2
  1. 1.Kheiron Medical TechnologiesLondonUK
  2. 2.European Society of Breast ImagingViennaAustria

Personalised recommendations