Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A (2018) Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 68:394–424
PubMed
Google Scholar
Munoz D, Near AM, van Ravesteyn NT et al (2014) Effects of screening and systemic adjuvant therapy on ER-specific US breast cancer mortality. J Natl Cancer Inst 106:dju289
PubMed
PubMed Central
Google Scholar
Youlden DR, Cramb SM, Dunn NA, Muller JM, Pyke CM, Baade PD (2012) The descriptive epidemiology of female breast cancer: an international comparison of screening, incidence, survival and mortality. Cancer Epidemiol 36:237–248
PubMed
Google Scholar
Berry DA, Cronin KA, Plevritis SK et al (2005) Effect of screening and adjuvant therapy on mortality from breast cancer. N Engl J Med 353:1784–1792
CAS
PubMed
Google Scholar
Althuis MD, Dozier JM, Anderson WF, Devesa SS, Brinton LA (2005) Global trends in breast cancer incidence and mortality 1973-1997. Int J Epidemiol 34:405–412
PubMed
Google Scholar
Tagliafico A, Houssami N, Calabrese M (2016) Digital breast tomosynthesis: a practical approach, 1st edn. Springer International Publishing, New York City, New York
Google Scholar
Niklason LT, Christian BT, Niklason LE et al (1997) Digital tomosynthesis in breast imaging. Radiology 205:399–406
CAS
PubMed
Google Scholar
Lång K, Andersson I, Rosso A, Tingberg A, Timberg P, Zackrisson S (2016) Performance of one-view breast tomosynthesis as a stand-alone breast cancer screening modality: results from the Malmo breast Tomosynthesis screening trial, a population-based study. Eur Radiol 26:184–190
PubMed
Google Scholar
Friedewald SM, Rafferty EA, Rose SL et al (2014) Breast cancer screening using tomosynthesis in combination with digital mammography. JAMA 311:2499–2507
CAS
PubMed
Google Scholar
Durand MA, Haas BM, Yao X et al (2015) Early clinical experience with digital breast tomosynthesis for screening mammography. Radiology 274:85–92
PubMed
Google Scholar
McCarthy AM, Kontos D, Synnestvedt M et al (2014) Screening outcomes following implementation of digital breast tomosynthesis in a general-population screening program. J Natl Cancer Inst 106:dju316
PubMed
PubMed Central
Google Scholar
Lourenco AP, Barry-Brooks M, Baird GL, Tuttle A, Mainiero MB (2015) Changes in recall type and patient treatment following implementation of screening digital breast tomosynthesis. Radiology 274:337–342
PubMed
Google Scholar
Skaane P, Bandos AI, Gullien R et al (2013) Comparison of digital mammography alone and digital mammography plus tomosynthesis in a population-based screening program. Radiology 267:47–56
PubMed
Google Scholar
Skaane P, Bandos AI, Gullien R et al (2013) Prospective trial comparing full-field digital mammography (FFDM) versus combined FFDM and tomosynthesis in a population-based screening programme using independent double reading with arbitration. Eur Radiol 23:2061–2071
PubMed
PubMed Central
Google Scholar
Ciatto S, Houssami N, Bernardi D et al (2013) Integration of 3D digital mammography with tomosynthesis for population breast-cancer screening (STORM): a prospective comparison study. Lancet Oncol 14:583–589
PubMed
Google Scholar
Haas BM, Kalra V, Geisel J, Raghu M, Durand M, Philpotts LE (2013) Comparison of tomosynthesis plus digital mammography and digital mammography alone for breast cancer screening. Radiology 269:694–700
PubMed
Google Scholar
Mall S, Noakes J, Kossoff M et al (2018) Can digital breast tomosynthesis perform better than standard digital mammography work-up in breast cancer assessment clinic? Eur Radiol 28:5182–5194
CAS
PubMed
Google Scholar
Dang PA, Freer PE, Humphrey KL, Halpern EF, Rafferty EA (2014) Addition of tomosynthesis to conventional digital mammography: effect on image interpretation time of screening examinations. Radiology 270:49–56
PubMed
Google Scholar
Bernardi D, Ciatto S, Pellegrini M et al (2012) Application of breast tomosynthesis in screening: incremental effect on mammography acquisition and reading time. Br J Radiol 85:e1174–e1178
CAS
PubMed
PubMed Central
Google Scholar
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 47:2467–2480
Google Scholar
Wei J, Chan HP, Sahiner B et al (2011) Computer-aided detection of breast masses in digital breast tomosynthesis (DBT): improvement of false positive reduction by optimization of object segmentation. In: SPIE medical imaging 2011, Lake Buena Vista, Florida, United States, 796311:1–6
Chan HP, Wei J, Sahiner B et al (2005) Computer-aided detection system for breast masses on digital tomosynthesis mammograms: preliminary experience. Radiology 237:1075–1080
PubMed
Google Scholar
Kim ST, Kim DH, Ro YM (2014) Breast mass detection using slice conspicuity in 3D reconstructed digital breast volumes. Phys Med Biol 59:5003–5023
PubMed
Google Scholar
Kim DH, Kim ST, Ro YM (2015) Improving mass detection using combined feature representations from projection views and reconstructed volume of DBT and boosting based classification with feature selection. Phys Med Biol 60:8809–8832
CAS
PubMed
Google Scholar
Kim DH, Kim ST, Baddar WJ, Ro YM (2015) Feature extraction from bilateral dissimilarity in digital breast tomosynthesis reconstructed volume. In: 2015 IEEE international conference on image processing (ICIP), Quebec City, Quebec, Canada, 4521–4524
Chan HP, Wu YT, Sahiner B et al (2010) Characterization of masses in digital breast tomosynthesis: comparison of machine learning in projection views and reconstructed slices. Med Phys 37:3576–3586
PubMed
PubMed Central
Google Scholar
Shen D, Wu G, Suk HI (2017) Deep learning in medical image analysis. Annu Rev Biomed Eng 19:221–248
CAS
PubMed
PubMed Central
Google Scholar
LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444
CAS
PubMed
Google Scholar
Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117
PubMed
Google Scholar
Samala RK, Chan HP, Hadjiiski LM, Helvie MA, Wei J, Cha KH (2016) Mass detection in digital breast tomosynthesis: deep convolutional neural network with transfer learning from mammography. Med Phys 43:6654–6666
PubMed
PubMed Central
Google Scholar
Fotin SV, Yin Y, Haldankar H, Hoffmeister JW, Periaswamy S (2016) Detection of soft tissue densities from digital breast tomosynthesis: comparison of conventional and deep learning approaches. In: SPIE medical imaging 2016, San Diego, California, United States, 97850X:1–6
Kim DH, Kim ST, Ro YM (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, China, 927–931
Kim DH, Kim ST, Chang JM, Ro YM (2017) Latent feature representation with depth directional long-term recurrent learning for breast masses in digital breast tomosynthesis. Phys Med Biol 62:1009–1031
PubMed
Google Scholar
Samala RK, Chan HP, Hadjiiski LM, Helvie MA, Richter CD, Cha KH (2018) Evolutionary pruning of transfer learned deep convolutional neural network for breast cancer diagnosis in digital breast tomosynthesis. Phys Med Biol 63:095005
PubMed
PubMed Central
Google Scholar
Mendel K, Li H, Sheth D, Giger M (2018) Transfer learning from convolutional neural networks for computer-aided diagnosis: a comparison of digital breast Tomosynthesis and full-field digital mammography. Acad Radiol. https://doi.org/10.1016/j.acra.2018.06.019
PubMed
Google Scholar
Samala RK, Chan H, Hadjiiski L, Helvie MA, Richter CD, Cha KH (2019) Breast cancer diagnosis in digital breast tomosynthesis: effects of training sample size on multi-stage transfer learning using deep neural nets. IEEE Trans Med Imaging 38:686–696
PubMed
PubMed Central
Google Scholar
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. In: arXiv e-prints. Available via https://arxiv.org/abs/1409.1556v6. Accessed 10 Apr 2015
Tran D, Bourdev L, Fergus R, Torresani L, Paluri M (2014) Learning spatiotemporal features with 3D convolutional networks. In: arXiv e-prints. Available via https://arxiv.org/abs/1412.0767v4. Accessed 7 Oct 2015
Perez L, Wang J (2017) The effectiveness of data augmentation in image classification using deep learning. In: arXiv e-prints. Available via https://arxiv.org/abs/1712.04621v1. Accessed 13 Dec 2017
Fitzpatrick JM, Sonka M (2000) Handbook of medical imaging: volume 2. Medical image processing and analysis. SPIE, Bellingham, Washington
Seeram E (2010) Digital radiography: an introduction, 1st edn. Delmar Learning, Clifton Park, New York
Google Scholar
Gonzalez RC, Woods RE (2017) Digital image processing, 4th edn. Pearson, Hoboken
Google Scholar
Wu T, Moore RH, Rafferty EA, Kopans DB (2004) A comparison of reconstruction algorithms for breast tomosynthesis. Med Phys 31:2636–2647
PubMed
Google Scholar
Reiser I, Bian J, Nishikawa RM, Sidky EY, Pan X (2009) Comparison of reconstruction algorithms for digital breast tomosynthesis. In: arXiv e-prints. Available via https://arxiv.org/abs/0908.2610v1. Accessed 01 Aug 2009
D’Orsi CJ, Sickles EA, Mendelson EB, Morris EA (2013) ACR BI-RADS® atlas: breast imaging reporting and data system, 5th edn. American College of Radiology, Reston
Google Scholar
Lehman CD, Arao RF, Sprague BL et al (2016) National Performance Benchmarks for modern screening digital mammography: update from the breast Cancer surveillance consortium. Radiology 283:49–58
PubMed
Google Scholar
Sprague BL, Arao RF, Miglioretti DL et al (2017) National Performance Benchmarks for modern diagnostic digital mammography: update from the breast Cancer surveillance consortium. Radiology 283:59–69
PubMed
Google Scholar
Seo BK, Pisano ED, Kuzmiak CM et al (2006) The positive predictive value for diagnosis of breast Cancer: full-field digital mammography versus film-screen mammography in the diagnostic mammographic population. Acad Radiol 13:1229–1235
PubMed
Google Scholar
Liberman L, Abramson AF, Squires FB, Glassman JR, Morris EA, Dershaw DD (1998) The breast imaging reporting and data system: positive predictive value of mammographic features and final assessment categories. AJR Am J Roentgenol 171:35–40
CAS
PubMed
Google Scholar
Zou XN (2017) Epidemic trend, screening, and early detection and treatment of cancer in Chinese population. Cancer Biol Med 14:50–59
PubMed
PubMed Central
Google Scholar
Zech JR, Badgeley MA, Liu M, Costa AB, Titano JJ, Oermann EK (2018) Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: a cross-sectional study. PLoS Med 15:e1002683
PubMed
PubMed Central
Google Scholar