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Survey of deep learning in breast cancer image analysis

  • Taye Girma DebeleeEmail author
  • Friedhelm Schwenker
  • Achim Ibenthal
  • Dereje Yohannes
Review
  • 52 Downloads

Abstract

Computer-aided image analysis for better understanding of images has been time-honored approaches in the medical computing field. In the conventional machine learning approach, the domain experts in medical images are mandatory for image annotation that subsequently to be used for feature engineering. However, in deep learning, a big jump has been made to help the researchers do segmentation, feature extraction, classification, and detection from raw medical images obtained using digital breast tomosynthesis, digital mammography, magnetic resonance imaging, and ultrasound imaging modalities. As a result, deep learning (DL) has gained a state-of-the-art in many application areas, for example, breast cancer image analysis. In this survey paper, we reviewed the most common breast cancer imaging modalities, public, most cited and recently updated breast cancer databases, histopathological based breast cancer image analysis, and DL application types in medical image analysis. We finally conclude by pointing out the research gaps to be addressed in the future.

Keywords

Breast cancer Breast cancer databases Imaging modalities Medical image analysis Deep learning application 

Notes

Acknowledgements

The corresponding author would like to thank the Ethiopian Ministry of Education (MoE) and the Deutscher Akademischer Auslandsdienst (DAAD) for funding this research work (Funding number 57162925).

References

  1. Agliozzo S et al (2012) Computer-aided diagnosis for dynamic contrast-enhanced breast MRI of mass-like lesions using a multiparametric model combining a selection of morphological, kinetic, and spatiotemporal features. Med Phys 39(4):1704–1715CrossRefGoogle Scholar
  2. Agner SC et al (2011) Textural kinetics: a novel dynamic contrast-enhanced (DCE)-MRI feature for breast lesion classification. J Digit Imaging 24(3):446–463CrossRefGoogle Scholar
  3. Ahmad, Khurshid (2019) Classification of breast cancer histology images using transfer learning. In: 16th IEEE international Bhurban conference on applied sciences and technology (IBCAST), Pakistan.  https://doi.org/10.1109/IBCAST.2019.8667221
  4. American Cancer Society (2015) Breast cancer facts and figures 2015–2016. http://www.cancer.org/acs/groups/content/@research/documents/document/acspc-046381.pdf. Accesed 14 Apr 2015
  5. American College of Radiology Imaging Network (2017) ABOUT mammography and tomosynthesis—ACRIN. https://www.acrin.org. Accessed June 2017
  6. Amit G et al (2017) Classification of breast MRI lesions using small-size training sets: comparison of deep learning approaches. In: Proceedings of SPIE 10134, medical imaging 2017: computer-aided diagnosis, 101341H.  https://doi.org/10.1117/12.2249981
  7. Anavi Y et al (2015) A comparative study for chest radiograph image retrieval using binary texture and deep learning classification. In: 2015 37th annual international conference of the IEEE engineering in medicine and biology society (EMBC), pp 2940–2943.  https://doi.org/10.1109/EMBC.2015.7319008
  8. Anavi Y et al (2016) Visualizing and enhancing a deep learning framework using patients age and gender for chest X-ray image retrieval. In: Medical imaging, vol 9785 of Proceedings of the SPIE, p 978510Google Scholar
  9. Andersson I et al (2008) Breast tomosynthesis and digital mammography: a comparison of breast cancer visibility and BIRADS classification in a population of cancers with subtle mammographic findings. Eur Radiol 18(12):2817–25CrossRefGoogle Scholar
  10. Angelov P, Gu X (2017) MICE: multi-layer multi-model images classifier ensemble. In: 3rd IEEE international conference on cybernetics (CYBCONF), pp 1–8.  https://doi.org/10.1109/CYBConf.2017.7985788
  11. Angelov P, Gu X (2018) Deep rule-based classifier with human-level performance and characteristics. Inf Sci 463:196–213CrossRefGoogle Scholar
  12. Angelov P, Sperduti A (2016) Challenges in deep learning. In: ESANN 2016 proceedings, European symposium on artificial neural networks, Computational intelligence and machine learning. Bruges, Belgium, pp 27–29Google Scholar
  13. Antropova N et al (2017b) A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets. Med Phys 44(10):5162–5171CrossRefGoogle Scholar
  14. Antropova HA, Giger ML (2018) Use of clinical MRI maximum intensity projections for improved breast lesion classification with deep convolutional neural networks. J Med Imaging 5(1):014503.  https://doi.org/10.1117/1.JMI.5.1.014503 CrossRefGoogle Scholar
  15. Antropova N, Huynh B, Giger M (2018) Recurrent neural networks for breast lesion classification based on DCE-MRIs. In: Proceedings of SPIE 10575, medical imaging 2018: computer-aided diagnosis, 105752M.  https://doi.org/10.1117/12.2293265
  16. Antropova N, Huynh B, Giger Maryellen (2017) Performance comparison of deep learning and segmentation-based radiomic methods in the task of distinguishing benign and malignant breast lesions on DCE-MRI. In: Proceedings of SPIE 10134, medical imaging 2017: computer-aided diagnosis, 101341G.  https://doi.org/10.1117/12.2255582
  17. Baker JA, Lo JY (2011) Breast tomosynthesis: state-of-theart and review of the literature. Acad Radiol 18(10):1298–310CrossRefGoogle Scholar
  18. Bar Y et al (2016) Chest pathology identification using deep feature selection with non-medical training. Comput Methods Biomech Biomed Eng Imaging Vis 6(3):259–263.  https://doi.org/10.1080/21681163.2016.1138324 CrossRefGoogle Scholar
  19. Bar Y, Diamant I, Wolf L, Greenspan H (2015) Deep learning with non-medical training used for chest pathology identification. In: Medical imaging, vol 9414 of Proceedings of the SPIE, p 94140VGoogle Scholar
  20. Becker AS et al (2018) Classification of breast cancer in ultrasound imaging using a generic deep learning analysis software: a pilot study. Br J Radiol 91:20170576Google Scholar
  21. Beroud C et al (2016) BRCA share: a collection of clinical BRCA gene variants. Hum Mutat 37(12):1318–1328CrossRefGoogle Scholar
  22. Brandt KR et al (2013) Can digital breast tomosynthesis replace conventional diagnostic mammography views for screening recalls without calcifications? A comparison study in a simulated clinical setting. Am J Roentgenol 200:291–298CrossRefGoogle Scholar
  23. Brennan ME, Turner RM, Ciatto S, Marinovich ML, French JR, Macaskill P, Houssami N (2011) Ductal carcinoma in situ at core-needle biopsy: meta-analysis of underestimation and predictors of invasive breast cancer. Radiology 260(1):119–128CrossRefGoogle Scholar
  24. Burgh V et al (2017) Deep learning predictions of survival based on MRI in amyotrophic lateral sclerosis. Neuro Image Clin 13:361–369Google Scholar
  25. Burrell HC, Sibbering D, Wilson A et al (1996) Screening interval breast cancers: mammographic features and prognostic factors. Radiology 199(3):811–817CrossRefGoogle Scholar
  26. Byra M, Sznajder T, Korzinek D (2018) Impact of ultrasound image reconstruction method on breast lesion classification with neural transfer learning. arXiv:1804.02119v1
  27. CBIS-DDSM (2019) Image dataset. https://wiki.cancerimagingarchive.net/display/Public/CBIS-DDSM. Accessed June 2019
  28. Cha KH et al (2016) Urinary bladder segmentation in CT urography using deep-learning convolutional neural network and level sets. Med Phys 43:1882–1896CrossRefGoogle Scholar
  29. Chan H-P et al (2008) Computer-aided detection of masses in digital tomosynthesis mammography: comparison of three approaches. Med Phys 35(9):4087–4095CrossRefGoogle Scholar
  30. Chang YC et al (2014) Computerized breast lesions detection using kinetic and morphologic analysis for dynamic contrast-enhanced MRI. Magn Reson Imaging 32(5):514–522CrossRefGoogle Scholar
  31. Ciatto S et al (2013) Integration of 3D digital mammography with tomosynthesis for population breast-cancer screening (STORM): a prospective comparison study. Lancet Oncol 14:583–589CrossRefGoogle Scholar
  32. Ciompi F et al (2015) Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of the-box. Med Image Anal 26:195–202CrossRefGoogle Scholar
  33. Conant EF et al (2016) Breast cancer screening using tomosynthesis in combination with digital mammography compared to digital mammography alone: a cohort study within the PROSPR consortium. Breast Cancer Res Treat 156:109–116CrossRefGoogle Scholar
  34. Debelee TG et al (2018) Classification of mammograms using convolutional neural network based feature extraction. ICT4DA 2017 LNICST 244:89–98Google Scholar
  35. Duijm LEM et al (1997) Sensitivity, specificity and predictive values of breast imagig in the detection of cancer. Br J Cancer 76(3):377–381CrossRefGoogle Scholar
  36. Durand MA et al (2015) Early clinical experience with digital breast tomosynthesis for screening mammography. Radiology 274:85–92CrossRefGoogle Scholar
  37. Ethiopian Cancer Association (2016) Learn about cancer. http://www.yeeca.org/Learnaboutcancer. Accessed Apr 2017
  38. Faridah Y (2008) Digital versus screen film mammography: a clinical comparison. Biomed Imaging Interv J 4(4):e31CrossRefGoogle Scholar
  39. Forsberg D, Sjoblom E, Sunshine JL (2017) Detection and labeling of vertebrae in MR images using deep learning with clinical annotations as training data. J Digit Imaging 30(4):406–412CrossRefGoogle Scholar
  40. Fotin SV et al (2016b) Detection of soft tissue densities from digital breast tomosynthesis: comparison of conventional and deep learning approaches. In: Medical imaging, vol 9785 of Proceedings of the SPIE, p. 97850XGoogle Scholar
  41. Freer PE, Wang JL, Rafferty EA (2014) Digital breast tomosynthesis in the analysis of fat-containing lesions. Radiographics 34:343–358CrossRefGoogle Scholar
  42. Gallego-Ortiz C, Martel AL (2015) Improving the accuracy of computer-aided diagnosis for breast MR imaging by differentiating between mass and nonmass lesions. Radiology 278(3):679–688.  https://doi.org/10.1148/radiol.2015150241 CrossRefGoogle Scholar
  43. Gallego-Posado JD et al (2016) Detection and diagnosis of breast tumors using deep Convolutional Neural Networks. In: Research Group on Mathematical Modeling School of Mathematical Sciences Universidad EAFIT Medell in, Colombia, pp 115-121Google Scholar
  44. Gao M et al (2016) Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks. Comput Methods Biomech Biomed Eng Imaging Vis 6(1):1–6.  https://doi.org/10.1080/21681163.2015.1124249 MathSciNetCrossRefGoogle Scholar
  45. Gennaro G et al (2010) Digital breast tomosynthesis versus digital mammography: a clinical performance study. Eur Radiol 20(7):1545–53CrossRefGoogle Scholar
  46. Ghafoorian M et al (2017) Deep multi-scale location aware 3D convolutional neural networks for automated detection of lacunes of presumed vascular origin. NeuroImage Clin 14:391–399CrossRefGoogle Scholar
  47. Gilbert FJ et al (2015) Accuracy of digital breast tomosynthesis for depicting breast cancer subproups in a UK retrospective reading study. Radiology 277(3):697–706CrossRefGoogle Scholar
  48. Griebsh I et al (2006) Cost-effectiveness of screening with contrast enhanced magnetic resonance imaging vs X-ray mammography of women at a high familial risk of breast cancer. Br J Cancer 95:801–810CrossRefGoogle Scholar
  49. Grimm LJ, Ryser MD, Partridge AH, Thompson AM, Thomas JS, Wesseling J, Hwang ES (2017) Surgical upstaging rates for vacuum assisted biopsy proven DCIS: implications for active surveillance trials. Ann Surg Oncol 24:3534–3540CrossRefGoogle Scholar
  50. Gubern-Mèrida A et al (2015) Breast segmentation and density estimation in breast MRI: a fully automatic framework. IEEE J Biomed Heal Inform 19(1):349–357CrossRefGoogle Scholar
  51. Gur D et al (2009) Digital breast tomosynthesis: observer performance study. Am J Roentgenol 193(2):586–591CrossRefGoogle Scholar
  52. Haas B et al (2013) Performance of digital breast tomosynthesis compared to conventional digital mammography for breast cancer screening. Radiology 269:694–700CrossRefGoogle Scholar
  53. Hagen AL et al (2007) Sensitivity of MRI versus conventional screening in the diagnosis of BRCA-associated breast cancer in a national prospective series. Breast 16(4):367–74CrossRefGoogle Scholar
  54. Han S et al (2017) A deep learning framework for supporting the classification of breast lesions in ultrasound images. Phys Med Biol 62:7714–7728CrossRefGoogle Scholar
  55. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition, In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, Las Vegas, NV, USA, pp 770–778Google Scholar
  56. Helvie MA (2010) Digital mammography imaging: breast tomosynthesis and advanced applications. Radiol Clin N Am 48(5):917–929.  https://doi.org/10.1016/j.rcl.2010.06.009 CrossRefGoogle Scholar
  57. Hosseini-Asl E, Gimel’farb G, El-Baz A (2016) Alzheimer’s disease diagnostics by a deeply supervised adaptable 3D convolutional network 1(23):584–596. arXiv: 1607.00556
  58. Huynh BQ et al (2017) Comparison of breast DCE-MRI contrast time points for predicting response to neoadjuvant chemotherapy using deep convolutional neural network features with transfer learning. In: Proceedings of SPIE 10134, medical imaging 2017: computer-aided diagnosis, p 101340U.  https://doi.org/10.1117/12.2255316
  59. Hwang S, Kim H-E, Jeong J, Kim H-J (2016) A novel approach for tuberculosis screening based on deep convolutional neural networks. In: Medical imaging, vol 9785 of Proceedings of the SPIE, pp 97852W-1Google Scholar
  60. Jadoon MM et al (2017) Three-class mammogram classification based on descriptive CNN features. Hindawi Biomed Res Int.  https://doi.org/10.1155/2017/3640901 CrossRefGoogle Scholar
  61. Jalalian A et al (2013) Computer-aided detection/diagnosis of breast cancer in mammography and ultrasound: a review. Clin Imaging 37:420–426CrossRefGoogle Scholar
  62. Janowczyk A, Madabhushi A (2016) Deep learning for digital pathology image analysis: a comprehensive tutorial with selected use cases. J Pathol Inf 7:29.  https://doi.org/10.4103/2153-3539.186902 CrossRefGoogle Scholar
  63. Jiang Y, Chen L, Zhang H, Xiao X (2019) Breast cancer histopathological image classification using convolutional neural networks with small SE-ResNet module. PLoS One 14(3):e0214587.  https://doi.org/10.1371/journal.pone.0214587 CrossRefGoogle Scholar
  64. Kallenberg et al (2016) Unsupervised deep learning applied to breast density segmentation and mammographic risk scoring. IEEE Trans Med Imaging 35:1322–1331CrossRefGoogle Scholar
  65. Kevin KM et al (2010) Breast cancer detection using automated whole breast ultrasound and mammography in radiographically dense breasts. Eur Radiol 20:734–742CrossRefGoogle Scholar
  66. Kim DH et al (2016) Latent feature representation with 3-D multi-view deep convolutional neural network for bilateral analysis in digital breast tomosynthesis. In: 2016 IEEE international conference on acoustics, speech and signal processing (ICASSP), Shanghai, 2016, pp 927–931.  https://doi.org/10.1109/ICASSP.2016.7471811
  67. Kim H, Hwang S (2016) Scale-invariant feature learning using deconvolutional neural networks for weakly-supervised semantic segmentation. ArXiv: 1602.04984
  68. Kooi T et al (2016) A comparison between a deep convolutional neural network and radiologists for classifying regions of interest in mammography. In: Proceedings of the 13th international workshop on digital mammography. Springer International Publishing, Geneva, pp 51–56Google Scholar
  69. Kooi et al (2017) Discriminating solitary cysts from soft tissue lesions in mammography using a pre-trained deep convolutional neural network. Med Phys 44(3):1017–1027CrossRefGoogle Scholar
  70. Kooi T et al (2017b) Large scale deep learning for computer aided detection of mammographic lesions. Med Image Anal 35:303–312CrossRefGoogle Scholar
  71. Kopans DB (2014) Digital breast tomosynthesis from concept to clinical care. Am J Roentgenol 202(2):299–308CrossRefGoogle Scholar
  72. Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: Proceedings of the 25th international conference on neural information processing systems, vol 1, pp 1097–1105Google Scholar
  73. Kuhl CK et al (2005) Mammography, breast ultrasound, and magnetic resonance imaging for surveillance of women at high familial risk for breast cancer. J Clin Oncol 23(33):8469–76CrossRefGoogle Scholar
  74. Kuhl CK et al (2007) MRI for diagnosis of pure ductal carcinoma in situ: a prospective observational study. Lancet 370:485–492CrossRefGoogle Scholar
  75. Kuhl CK et al (2014) Abbreviated breast magnetic resonance imaging (MRI): first postcontrast subtracted images and maximum-intensity projection-a novel approach to breast cancer screening with MRI. J Clin Oncol 32:2304–2310CrossRefGoogle Scholar
  76. Lang K et al (2016) Performance of oneview breast tomosynthesis as a stand-alone breast cancer screening modality: results from the Malmo Breast Tomosynthesis Screening Trial, a population based study. Eur Radiol 26:184–190CrossRefGoogle Scholar
  77. Leach MO, Boggis CR, Dixon AK, Easton DF, Eeles RA, Evans DG, Gilbert FJ, Griebsch I, Hoff RJ, Kessar P, Lakhani SR, Moss SM, Nerurkar A, Padhani AR, Pointon LJ, Thompson D, Warren RM, MARIBS study group (2005) Screening with magnetic resonance imaging and mammography of a UK population at high familial risk of breast cancer: a prospective multicentre cohort study (MARIBS). Lancet 365(9473):1769–1778.  https://doi.org/10.1016/S0140-6736(05)66481-1 CrossRefGoogle Scholar
  78. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444CrossRefGoogle Scholar
  79. Lee RS et al (2017) A curated mammography data set for use in computer-aided detection and diagnosis research. Sci Data 4:170177CrossRefGoogle Scholar
  80. Lee et al (2016) Curated breast imaging subset of DDSM. Cancer Imaging Arch.  https://doi.org/10.7937/K9/TCIA.2016.7O02S9CY CrossRefGoogle Scholar
  81. Lian C et al (2017) Spatial evidential clustering with adaptive distance metric for tumor segmentation in FDG-PET images. IEEE Trans Biomed Eng 65(1):21–30CrossRefGoogle Scholar
  82. Lian C, Ruan S, Denoeux T (2015) An evidential classifier based on feature selection and two-step classification strategy. Pattern Recogn 48(7):2318–2327CrossRefGoogle Scholar
  83. Li J, Fan M, Zhang J, Li L (2017) Discriminating between benign and malignant breast tumors using 3D convolutional neural network in dynamic contrast enhanced-MR images. In: Proceedings of SPIE 10138, medical imaging 2017: imaging informatics for healthcare, research, and applications, p 1013808.  https://doi.org/10.1117/12.2254716
  84. Lin SP, Brown JJ (2007) MR contrast agents: physical and pharmacologic basics. J Magn Reson Imaging 25:884–899CrossRefGoogle Scholar
  85. Liu J et al (2018) Radiation dose reduction in digital breast tomosynthesis (DBT) by means of deep-learning-based supervised image processing. In: Proceedings of SPIE 10574, medical imaging 2018: image processing, p 105740F.  https://doi.org/10.1117/12.2293125
  86. Liu M et al (2017) View-aligned hypergraph learning for Alzheimer’s disease diagnosis with incomplete multi-modality data. Med Image Anal 36:123–134CrossRefGoogle Scholar
  87. Lourenco AP et al (2015) Changes in recall type and patient treatment following implementation of screening digital breast tomosynthesis. Radiology 274:337–342CrossRefGoogle Scholar
  88. Mahrooghy M et al (2015) Pharmacokinetic tumor heterogeneity as a prognostic biomarker for classifying breast cancer recurrence risk. IEEE Trans Biomed Eng 62(6):1585–1594CrossRefGoogle Scholar
  89. Mall S et al (2017) The role of digital breast tomosynthesis in the breast assessment clinic: a review. J Med Radiat Sci 64:203–211CrossRefGoogle Scholar
  90. Mariscotti G et al (2014) Accuracy of mammography, digital breast tomosynthesis, ultrasound and MR imaging in preoperative assessment of breast cancer. Anticancer Res 34:1219–1226Google Scholar
  91. Mazurowski MA et al (2015) Recurrence-free survival in breast cancer is associated with MRI tumor enhancement dynamics quantified using computer algorithms. Eur J Radiol 84(11):2117–2122CrossRefGoogle Scholar
  92. Mazurowski MA (2015) Radiogenomics: what it is and why it is important. J Am Coll Radiol 12(8):862–866CrossRefGoogle Scholar
  93. McCarthy AM et al (2014) Screening outcomes following implementation of digital breast tomosynthesis in a general population screening program. J Natl Cancer Inst 2014:106CrossRefGoogle Scholar
  94. McDonald ES et al (2015) Baseline screening mammography: performance of full field digital mammography versus digital breast tomosynthesis. AJR 205:1143–1148CrossRefGoogle Scholar
  95. Mendel KR, Li H, Sheth D, Giger ML (2018) Transfer learning with convolutional neural networks for lesion classification on clinical breast tomosynthesis. In: Proceedings of SPIE 10575, medical imaging 2018: computer-aided diagnosis, p 105750T.  https://doi.org/10.1117/12.2294973
  96. Michell MJ et al (2012) A comparison of the accuracy of filmscreen mammography, full-field digital mammography, and digital breast tomosynthesis. Clin Radiol 67(10):976–981CrossRefGoogle Scholar
  97. Mobadersany P et al (2018) Predicting cancer outcomes from histology and genomics using convolutional networks. PNAS 115(13):E2970–E2979.  https://doi.org/10.1073/pnas.1717139115 CrossRefGoogle Scholar
  98. Moreira et al (2011) INbreast: toward a full-field digital mammographic database. Acad Radiol 19(236):48.  https://doi.org/10.1016/j.acra.2011.09.014 CrossRefGoogle Scholar
  99. Morrow M, Waters J, Morris E (2011) MRI for breast cancer screening, diagnosis, and treatment. Lancet 378:1804–1811CrossRefGoogle Scholar
  100. National Cancer Institute (2018) BRCA mutations: cancer risk and genetic testing. https://www.cancer.gov/about-cancer/causes-prevention/genetics/brca-fact-sheet. Accessed June 2019
  101. Oliver MA (2007) Automatic mass segmentation in mammographic images. Ph.D. Thesis, Universitat De GironaGoogle Scholar
  102. Palma G, Bloch I, Muller S (2014) Detection of masses and architectural distortions in digital breast tomosynthesis images using fuzzy and a contrario approaches. Pattern Recogn 47(7):2467–2480CrossRefGoogle Scholar
  103. Pang Z et al (2015) A computer-aided diagnosis system for dynamic contrast enhanced MR images based on level set segmentation and Relief feature selection. Comput Math Methods Med 2015:450531zbMATHCrossRefGoogle Scholar
  104. Patterson SK, Roubidoux MA (2014) Update on new technologies in digital mammography. Int J Women’s Health 6:781–788CrossRefGoogle Scholar
  105. Phi XA et al (2016) Contribution of mammography to MRI screening in BRCA mutation carriers by BRCA status and age: individual patient data meta-analysis. Br J Cancer 114(6):631–637CrossRefGoogle Scholar
  106. Phi XA et al (2017) Accuracy of screening women at familial risk of breast cancer without a known gene mutation: Individual patient data meta-analysis. Eur J Cancer 85:31–38CrossRefGoogle Scholar
  107. Poplack SP, Tosteson TD, Kogel CA, Nagy HM (2007) Digital breast tomosynthesis: initial experience in 98 women with abnormal digital screening mammography. Am J Roentgenol 189(3):616–623CrossRefGoogle Scholar
  108. Rafferty EA (2007) Digital mammography: novel applications. Radiol Clin N Am 45:831–843CrossRefGoogle Scholar
  109. Rafferty EA et al (2013) Assessing radiologist performance using combined digital mammography and breast tomosynthesis compared with digital mammography alone: results of a multicenter. Multireader Trial Radiol 266(1):104–113Google Scholar
  110. Rafferty EA et al (2016) Breast cancer screening using tomosynthesis and digital mammography in dense and non-dense breasts. JAMA 315:1784–1786CrossRefGoogle Scholar
  111. Rajkomar A, Lingam S, Taylor AG, Blum M, Mongan J (2017) High-throughput classification of radiographs using deep convolutional neural networks. J Digit Imaging 30:95–101CrossRefGoogle Scholar
  112. Rakhlin A et al (2018) Deep convolutional neural networks for breast cancer histology image analysis. 1–9, ArXiv:1802.00752v2
  113. Regina RJ et al (2017) Advances in digital breast tomosynthesis. AJR 208:256–266CrossRefGoogle Scholar
  114. Reiser I et al (2006) Computerized mass detection for digital breast tomosynthesis directly from the projection images. Med Phys 33(2):482–491CrossRefGoogle Scholar
  115. Renz DM et al (2012) Detection and classification of contrast-enhancing masses by a fully automatic computer assisted diagnosis system for breast MRI. J Magn Reson Imaging 35(5):1077–1088CrossRefGoogle Scholar
  116. Rodrigues PS (2017) Breast ultrasound image, Mendeley data, vol 1.  https://doi.org/10.17632/wmy84gzngw.1
  117. Rodriguez-Ruiz A et al (2018) Pectoral muscle segmentation in breast tomosynthesis with deep learning. In: Proceedings of SPIE 10575, medical imaging 2018: computer-aided diagnosis, p 105752J.  https://doi.org/10.1117/12.2292920
  118. Ronneberger O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation. arXiv: 1505.04597v1
  119. Samala RK et al (2016a) Deep-learning convolution neural network for computer aided detection of micro-calciications in digital breast tomosynthesis. In: Medical imaging, vol 9785 of Proceedings of the SPIE, p 97850YGoogle Scholar
  120. Samala RK et al (2016b) Mass detection in digital breast tomosynthesis: Deep convolutional neural network with transfer learning from mammography. Med Phys 43(12):6654–6666CrossRefGoogle Scholar
  121. Samala RK et al (2017) Multi-task transfer learning deep convolutional neural network: application to computer-aided diagnosis of breast cancer on mammograms. Phys Med Biol 62:8894–8908CrossRefGoogle Scholar
  122. Samala R, Chan H-P, Hadjiiski LM, Helvie MA, Richter C, Cha K (2018a) Compression of deep convolutional neural network for computer-aided diagnosis of masses in digital breast tomosynthesis. Proceedings of SPIE, medical imaging: computer-aided diagnosis, 72.  https://doi.org/10.1117/12.2293400
  123. Samala R, Chan H-P, Hadjiiski LM, Helvie MA, Richter C, Cha K (2018b) Cross-domain and multi-task transfer learning of deep convolutional neural network for breast cancer diagnosis in digital breast tomosynthesis. In: Proceedings of SPIE 10575, medical imaging 2018: computer-aided diagnosis.  https://doi.org/10.1117/12.2293412
  124. Sampat M, Markey M, Bovik A (2005) Computer-aided detection and diagnosis in mammography. In: Handbook of image and video processing. Elsevier, Academic Press, pp 1195-1217.  https://doi.org/10.1016/B978-012119792-6/50130-3
  125. Sargano AB et al (2017b) Human action recognition using transfer learning with deep representations. In: International joint conference on neural networks (IJCNN), pp 463–469Google Scholar
  126. Saslow D et al (2007) American Cancer Society guidelines for breast screening with MRI as an adjunct to mammography. CA Cancer J Clin 57:75–89CrossRefGoogle Scholar
  127. Shah A, Conjeti S, Navab N, Katouzian A (2016) Deeply learnt hashing forests for content based image retrieval in prostate MR images. Med Imaging 9784:1–8Google Scholar
  128. Shen D, Wu G, Suk H-I (2017) Deep learning in medical image analysis. Annu Rev Biomed Eng 36(5):1172–1181Google Scholar
  129. Shin HC et al (2016a) Learning to read chest X-rays: recurrent neural cascade model for automated image annotation. ArXiv:1603.08486
  130. Shin HC et al (2016b) Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging 35(5):1285–1298CrossRefGoogle Scholar
  131. Shin SY et al (2017) Joint weakly and semi-supervised deep learning for localization and classification of masses in breast ultrasound images. arXiv: 1710.03778v1
  132. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556
  133. Skaane P (2009) Studies comparing screen-film mammography and full-field digital mammography in breast cancer screening: updated review. Acta Radiologica 501:3–14CrossRefGoogle Scholar
  134. Skaane P et al (2013) Comparison of digital mammography alone and digital mammography plus tomosynthesis in a population-based screening program. Radiology 267(1):47–56CrossRefGoogle Scholar
  135. Spampinato C et al (2017) Deep learning for automated skeletal bone age assessment in X-ray images. Med Image Anal 36:41–51CrossRefGoogle Scholar
  136. Sudarshan VK et al (2016) Application of wavelet techniques for cancer diagnosis using ultrasound images: a review. Comput Biol Med 69:97–111CrossRefGoogle Scholar
  137. Sumkin JH et al (2015) Recall rate reduction with tomosynthesis during baseline screening examinations. Acad Radiol 22:1477–1482CrossRefGoogle Scholar
  138. Sun J, Binder A (2017) Comparison of deep learning architectures for H\&E histopathology images. In: 2017 IEEE Conference on Big Data and Analytics (ICBDA). IEEE, Kuching, Malaysia, pp 43–48.  https://doi.org/10.1109/ICBDAA.2017.8284105
  139. Szegedy C et al. (2015) Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1–9Google Scholar
  140. Turkbey B et al (2009) The role of dynamic contrast enhanced MR imaging in cancer diagnosis and treatment. Diagn Interv Radiol 13:45–53Google Scholar
  141. van Schie G et al (2013) Mass detection in reconstructed digital breast tomosynthesis volumes with a computer aided detection system trained on 2D mammograms. Med Phys 40(4):041902CrossRefGoogle Scholar
  142. Wallis MG, Moa E, Zanca F, Leifland K, Danielsson M (2012) Two-view and single-view tomosynthesis versus full-field digital mammography: high-resolution X-ray imaging observer study. Radiology 262(3):788–96CrossRefGoogle Scholar
  143. Wang J et al (2017) Detecting cardiovascular disease from mammograms with deep learning. IEEE Trans Med Imaging 36(5):1172–1181CrossRefGoogle Scholar
  144. Wang C, Elazab A, Wu J, Hu Q (2016a) Lung nodule classification using deep feature fusion in chest radiography. Comput Med Imaging Gr 57:10–18CrossRefGoogle Scholar
  145. Warner E et al (2004) Surveillance of BRCA1 and BRCA2 mutation carriers with magnetic resonance imaging, ultrasound, mammography, and clinical breast examination. JAMA 292(11):1317–25CrossRefGoogle Scholar
  146. Warner E et al (2008) Systematic review: using magnetic resonance imaging to screen women at high risk for breast cancer. Ann Intern Med 148(9):671–679CrossRefGoogle Scholar
  147. Wu S, Weinstein SP, Conant EF, Schnall MD, Kontos D (2013) Automated chest wall line detection for whole-breast segmentation in sagittal breast MR images. Med Phys 40(4):042301CrossRefGoogle Scholar
  148. Wu A, Xu Z, Gao M, Buty M, Mollura DJ (2016) Deep vessel tracking: a generalized probabilistic approach via deep learning. IEEE Int Symp Biomed Imaging 5(6):1363–1367Google Scholar
  149. Xie et al (2019) Deep learning based analysis of histopathological images of breast cancer. Front Genet 10:80.  https://doi.org/10.3389/fgene.2019.00080 CrossRefGoogle Scholar
  150. Yap MH et al (2018b) End-to-end breast ultrasound lesions recognition with a deep learning approach. In: Proceedings of SPIE 10578, medical imaging 2018: biomedical applications in molecular. structural, and functional imaging, p 1057819.  https://doi.org/10.1117/12.2293498
  151. Yap MH et al (2018a) Automated breast ultrasound lesions detection using convolutional neural networks. IEEE J Biomed Health Inform 22(4):1218–1226CrossRefGoogle Scholar
  152. Yousefi M, Krzyzak Adam, Suen Ching Y (2018) Mass detection in digital breast tomosynthesis data using convolutional neural networks and multiple instance learning. Comput Biol Med 96:283–293CrossRefGoogle Scholar
  153. Zhang J et al (2018) Automatic deep learning-based normalization of breast dynamic contrast-enhanced magnetic resonance images. 1–11. arXiv:1807.02152v1
  154. Zhang J et al (2018) Breast mass detection in mammography and tomosynthesis via fully convolutional network-based heatmap regression. In: Proceedings of SPIE 10575, medical imaging 2018: computer-aided diagnosis, p 1057525.  https://doi.org/10.1117/12.2295443
  155. Zhang J et al (2018) Breast tumor segmentation in DCE-MRI using fully convolutional networks with an application in radiogenomics. In: Proceedings of SPIE 10575, medical imaging 2018: computer-aided diagnosis, 105750U.  https://doi.org/10.1117/12.2295436
  156. Zhang J et al (2018) Convolutional encoder-decoder for breast mass segmentation in digital breast tomosynthesis. In: Proceedings of SPIE 10575, medical imaging 2018: computer-aided diagnosis, p 105752V.  https://doi.org/10.1117/12.2295437
  157. Zhang J et al (2016) Automatic craniomaxillofacial land mark digitization via segmentation-guided partially-joint regression forest model and multiscale statistical features. IEEE Trans Biomed Eng 63(9):1820–1829CrossRefGoogle Scholar
  158. Zhang J et al (2017) Alzheimer’s disease diagnosis using landmark-based features from longitudinal structural MR images. IEEE J Biomed Health Inform 21(3):1607–1616CrossRefGoogle Scholar
  159. Zhu Z et al (2018) Deep learning-based features of breast MRI for prediction of occult invasive disease following a diagnosis of ductal carcinoma in situ: preliminary data. In: Proceedings of SPIE 10575, medical imaging 2018: computer-aided diagnosis, 105752W.  https://doi.org/10.1117/12.2295470
  160. Zhu Z et al (2016) Faithful completion of images of scenic landmarks using internet images. IEEE Trans Vis Comput Gr 22(8):1945–1958CrossRefGoogle Scholar
  161. Zhu Y et al (2017) MRI based prostate cancer detection with high-level representation and hierarchical classification. Med Phys 44(3):1028–1039CrossRefGoogle Scholar
  162. Zhu Z et al (2017) An optimization approaches for localization refinement of candidate traffic signs. IEEE Trans Vis Comput Gr 23(5):1561–1573CrossRefGoogle Scholar
  163. Zilly J et al (2017) Glaucoma detection using entropy sampling and ensemble learning for automatic optic cup and disc segmentation. Comput Med Imaging Gr 55:28–41CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Institute of Neural Information ProcessingUniversity of UlmUlmGermany
  2. 2.Department of Computer EngineeringAddis Ababa Science and Technology UniversityAddis AbabaEthiopia
  3. 3.HAWK University of Applied Sciences and ArtsGöttingenGermany

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