메뉴 건너뛰기




Volumn 6, Issue , 2016, Pages

Discrimination of Breast Cancer with Microcalcifications on Mammography by Deep Learning

Author keywords

[No Author keywords available]

Indexed keywords

ADULT; AGED; BREAST TUMOR; CALCINOSIS; CHINA; DIAGNOSTIC IMAGING; FEMALE; HUMAN; MACHINE LEARNING; MAMMOGRAPHY; MIDDLE AGED; PATHOLOGY; PROCEDURES; SENSITIVITY AND SPECIFICITY;

EID: 84974555742     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/srep27327     Document Type: Article
Times cited : (258)

References (55)
  • 1
    • 13444294237 scopus 로고    scopus 로고
    • Mammographic screening: No longer controversial
    • Cady, B. & Chung, M. Mammographic screening: no longer controversial. American Journal of Clin Oncol 28(1), 1-4 (2005).
    • (2005) American Journal of Clin Oncol , vol.28 , Issue.1 , pp. 1-4
    • Cady, B.1    Chung, M.2
  • 3
    • 0037464536 scopus 로고    scopus 로고
    • Clinical Practice: Mammographic screening for breast cancer
    • Fletcher, S. W. & Elmore, J. G. Clinical Practice: mammographic screening for breast cancer. New Engl J Med 348(17), 1672-1680 (2003).
    • (2003) New Engl J Med , vol.348 , Issue.17 , pp. 1672-1680
    • Fletcher, S.W.1    Elmore, J.G.2
  • 4
    • 0034125106 scopus 로고    scopus 로고
    • The diagnosis and management of ductal carcinoma in situ of the breast
    • Winchester, D. P., Jeske, J. M. & Goldschmidt, R. A. The diagnosis and management of ductal carcinoma in situ of the breast. Am Cancer J Clin 50(3), 184 (2000).
    • (2000) Am Cancer J Clin , vol.50 , Issue.3 , pp. 184
    • Winchester, D.P.1    Jeske, J.M.2    Goldschmidt, R.A.3
  • 5
    • 84974640208 scopus 로고    scopus 로고
    • Breast cancer: Early detection
    • Schreer, I. & Luttges, J. Breast cancer: early detection. Eur J Radiol 11 (Suppl 2), S307-S314 (2001).
    • (2001) Eur J Radiol , vol.11 , pp. S307-S314
    • Schreer, I.1    Luttges, J.2
  • 6
    • 0033874338 scopus 로고    scopus 로고
    • Ductal carcinoma in situ Implications for screening mammography
    • Stephen, A. & Feig, M. D. Ductal carcinoma in situ. Implications for screening mammography. Radiol Clin N Am 38(4), 653-668 (2000).
    • (2000) Radiol Clin N Am , vol.38 , Issue.4 , pp. 653-668
    • Stephen, A.1    Feig, M.D.2
  • 7
    • 1542720840 scopus 로고    scopus 로고
    • Mammographic criteria for determining the diagnostic value of microcalcifications in the detection of early breast cancer
    • Yunus, M., Ahmed, N. & Masroor, I. Mammographic criteria for determining the diagnostic value of microcalcifications in the detection of early breast cancer. J Pak Med Assoc 4(1), 24-29 (2004).
    • (2004) J Pak Med Assoc , vol.4 , Issue.1 , pp. 24-29
    • Yunus, M.1    Ahmed, N.2    Masroor, I.3
  • 8
    • 70350495153 scopus 로고    scopus 로고
    • Breast calcifications: Which are malignant
    • Muttarak, M., Kongmebho, lP. & Sukhamwang, N. Breast calcifications: which are malignant. Singap Med J 50(9), 907-914 (2009).
    • (2009) Singap Med J , vol.50 , Issue.9 , pp. 907-914
    • Muttarak, M.1    Kongmebho, J.P.2    Sukhamwang, N.3
  • 9
    • 0030982763 scopus 로고    scopus 로고
    • Characteristics of breast carcinomas missed by screening radiologists
    • Goergen, S. K., Evans, J. & Cohen, G. P. Characteristics of breast carcinomas missed by screening radiologists. Radiology 204(1), 131-135 (1997).
    • (1997) Radiology , vol.204 , Issue.1 , pp. 131-135
    • Goergen, S.K.1    Evans, J.2    Cohen, G.P.3
  • 10
    • 11444270994 scopus 로고    scopus 로고
    • Accuracy of screening mammography interpretation by characteristics of radiologists
    • Barlow, W. E., Chi, C. & Carney, P. A. Accuracy of screening mammography interpretation by characteristics of radiologists. J Natl Cancer I 96(24), 1840-1850 (2004).
    • (2004) J Natl Cancer i , vol.96 , Issue.24 , pp. 1840-1850
    • Barlow, W.E.1    Chi, C.2    Carney, P.A.3
  • 13
    • 0005482571 scopus 로고    scopus 로고
    • Potential contribution of computer-aided detection to the sensitivity of screening mammography
    • Burhene, L. J. W., Wood, S. A. & D'Orsi, C. J. Potential contribution of computer-aided detection to the sensitivity of screening mammography. Radiology 215(2), 554-562 (2000).
    • (2000) Radiology , vol.215 , Issue.2 , pp. 554-562
    • Burhene, L.J.W.1    Wood, S.A.2    D'Orsi, C.J.3
  • 14
    • 29944438579 scopus 로고    scopus 로고
    • Comparison of independent double readings and computer-aided diagnosis (CAD) for the diagnosis of breast calcifications
    • Jiang, Y., Nishikawa, R. M., Schmidt, R. A. & Metz, C. E. Comparison of independent double readings and computer-aided diagnosis (CAD) for the diagnosis of breast calcifications. Acad Radiol 13(1), 84-94 (2006).
    • (2006) Acad Radiol , vol.13 , Issue.1 , pp. 84-94
    • Jiang, Y.1    Nishikawa, R.M.2    Schmidt, R.A.3    Metz, C.E.4
  • 15
    • 33745045657 scopus 로고    scopus 로고
    • CAD for mammography:The technique,results,current role and further developments
    • Malich, A., Fischer, D. R. & B(o)ttcher, J. CAD for mammography:the technique,results,current role and further developments. Eur Radiol 16(7), 1449-1460 (2006).
    • (2006) Eur Radiol , vol.16 , Issue.7 , pp. 1449-1460
    • Malich, A.1    Fischer, D.R.2    Bottcher, J.3
  • 16
    • 84900326685 scopus 로고    scopus 로고
    • Diagnosis of breast masses from dynamic contrast-enhanced and diffusionweighted MR: A machine learning approach
    • Cai, H. M., Peng, Y. X., Ou, C. W., Chen, M. S. & Li, L. Diagnosis of breast masses from dynamic contrast-enhanced and diffusionweighted MR: a machine learning approach. PLoS One 9(1), e87387 (2014).
    • (2014) PLoS One , vol.9 , Issue.1 , pp. e87387
    • Cai, H.M.1    Peng, Y.X.2    Ou, C.W.3    Chen, M.S.4    Li, L.5
  • 17
    • 84925408177 scopus 로고    scopus 로고
    • A comparison between different prediction models for invasive breast cancer occurrence in the French E3N cohort
    • Dartois, L. et al. A comparison between different prediction models for invasive breast cancer occurrence in the French E3N cohort. Breast Cancer Res Tr 150(2), 415-426 (2015).
    • (2015) Breast Cancer Res Tr , vol.150 , Issue.2 , pp. 415-426
    • Dartois, L.1
  • 18
    • 70349566412 scopus 로고    scopus 로고
    • Statistical analysis of mammographic features and its classification using support vector machine
    • Krishnan, M. et al. Statistical analysis of mammographic features and its classification using support vector machine. Expert Systems with Applications 37(1), 470-478 (2010).
    • (2010) Expert Systems with Applications , vol.37 , Issue.1 , pp. 470-478
    • Krishnan, M.1
  • 19
    • 79952757441 scopus 로고    scopus 로고
    • Automatic detection of breast cancers in mammograms using structured support vector machines
    • Wang, D., Shi, L. & Heng, P. A. Automatic detection of breast cancers in mammograms using structured support vector machines. Neurocomputing 72(13), 3296-3302 (2009).
    • (2009) Neurocomputing , vol.72 , Issue.13 , pp. 3296-3302
    • Wang, D.1    Shi, L.2    Heng, P.A.3
  • 20
    • 56349089940 scopus 로고    scopus 로고
    • Support vector machines combined with feature selection for breast cancer diagnosis
    • Akay, M. F. Support vector machines combined with feature selection for breast cancer diagnosis. Expert systems with applications 36(2), 3240-3247 (2009).
    • (2009) Expert Systems with Applications , vol.36 , Issue.2 , pp. 3240-3247
    • Akay, M.F.1
  • 21
    • 84899766031 scopus 로고    scopus 로고
    • A data mining method for breast cancer identification based on a selection of variables
    • Holsbach, N., Fogliatto, F. S. & Anzanello, M. J. A data mining method for breast cancer identification based on a selection of variables. Ciência & Saúde Coletiva 19(4), 1295-1304 (2014).
    • (2014) Ciência & Saúde Coletiva , vol.19 , Issue.4 , pp. 1295-1304
    • Holsbach, N.1    Fogliatto, F.S.2    Anzanello, M.J.3
  • 22
    • 33846014233 scopus 로고    scopus 로고
    • A new hybrid method based on fuzzy-artificial immune system and k-nn algorithm for breast cancer diagnosis
    • Sahan, S., Polat, K., Kodaz, H. & Güne?, S. A new hybrid method based on fuzzy-artificial immune system and k-nn algorithm for breast cancer diagnosis. Comput BiolMed 37(3), 415-423 (2007).
    • (2007) Comput BiolMed , vol.37 , Issue.3 , pp. 415-423
    • Sahan, S.1    Polat, K.2    Kodaz, H.3    Güne, S.4
  • 24
    • 84878404694 scopus 로고    scopus 로고
    • Improving breast cancer classification with mammography, supported on an appropriate variable selection analysis
    • Pérez, N., Guevara, M. A. & Silva, A. Improving breast cancer classification with mammography, supported on an appropriate variable selection analysis. SPIE medical imaging. 867022-867022 (2013).
    • (2013) SPIE Medical Imaging , pp. 867022
    • Pérez, N.1    Guevara, M.A.2    Silva, A.3
  • 26
    • 84936878573 scopus 로고    scopus 로고
    • Deep neural networks reveal a gradient in the complexity of neural representations across the ventral stream
    • Güçlü, U. & Gerven, A. J. Deep neural networks reveal a gradient in the complexity of neural representations across the ventral stream. J Neurosci 35(27), 10005-10014 (2015).
    • (2015) J Neurosci , vol.35 , Issue.27 , pp. 10005-10014
    • Güçlü, U.1    Gerven, A.J.2
  • 27
    • 84938888109 scopus 로고    scopus 로고
    • Predicting the sequence specificities of DNA-and RNA-binding proteins by deep learning
    • Alipanahi, B., Delong, A., Weirauch, M. T. & Frey, B. J. Predicting the sequence specificities of DNA-and RNA-binding proteins by deep learning. Nat Biotechnol 33(8), 831-838 (2015).
    • (2015) Nat Biotechnol , vol.33 , Issue.8 , pp. 831-838
    • Alipanahi, B.1    Delong, A.2    Weirauch, M.T.3    Frey, B.J.4
  • 28
    • 84911403786 scopus 로고    scopus 로고
    • Automatic vaginal bacteria segmentation and classification based on superpixel and deep learning
    • Song, Y. Y. et al. Automatic vaginal bacteria segmentation and classification based on superpixel and deep learning. J Med Imag Health In 4(5), 781-786 (2014).
    • (2014) J Med Imag Health in , vol.4 , Issue.5 , pp. 781-786
    • Song, Y.Y.1
  • 29
    • 84855576467 scopus 로고    scopus 로고
    • Characterizing the clustered microcalcifications on mammograms to predict the pathological classification and grading: A mathematical modeling approach
    • Shao, Y. Z. et al. Characterizing the clustered microcalcifications on mammograms to predict the pathological classification and grading: A mathematical modeling approach. Journal of digital imaging 24(5), 764-771 (2011).
    • (2011) Journal of Digital Imaging , vol.24 , Issue.5 , pp. 764-771
    • Shao, Y.Z.1
  • 30
    • 33746600649 scopus 로고    scopus 로고
    • Reducing the dimensionality of data with neural networks
    • Hinton, G. E. & Salakhutdinov, R. R. Reducing the dimensionality of data with neural networks. Science 313(5786), 504-507 (2006).
    • (2006) Science , vol.313 , Issue.5786 , pp. 504-507
    • Hinton, G.E.1    Salakhutdinov, R.R.2
  • 31
    • 85006505269 scopus 로고    scopus 로고
    • Deep convolutional neural networks for Computer-Aided Detection: CNN architectures dataset characteristics and transfer learning
    • Shin, H. C. et al. Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning. IEEE T on Medical Imaging 99, 11 (2016).
    • (2016) IEEE T on Medical Imaging , vol.99 , pp. 11
    • Shin, H.C.1
  • 32
    • 84923814844 scopus 로고    scopus 로고
    • Latent feature representation with stacked auto-encoder for AD/MCI diagnosis
    • Suk, H. I. et al. Latent feature representation with stacked auto-encoder for AD/MCI diagnosis. Brain Structure and Function 220(2), 841-859 (2015).
    • (2015) Brain Structure and Function , vol.220 , Issue.2 , pp. 841-859
    • Suk, H.I.1
  • 33
    • 84907019192 scopus 로고    scopus 로고
    • Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis
    • Suk, H. I. et al. Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis. NeuroImage 101, 569-582 (2014).
    • (2014) NeuroImage , vol.101 , pp. 569-582
    • Suk, H.I.1
  • 34
    • 84955570567 scopus 로고    scopus 로고
    • Representation learning for mammography mass lesion classification with convolutional neural networks
    • Arevalo, J., González, F. A. & Ramos-Pollán, R. Representation learning for mammography mass lesion classification with convolutional neural networks. Computer Methods and Programs in Biomedicine 127, 248-257 (2016).
    • (2016) Computer Methods and Programs in Biomedicine , vol.127 , pp. 248-257
    • Arevalo, J.1    González, F.A.2    Ramos-Pollán, R.3
  • 36
    • 56449095373 scopus 로고    scopus 로고
    • A unified architecture for natural language processing: Deep neural networks with multitask learning
    • Collobert, R. & Weston, J. A unified architecture for natural language processing: Deep neural networks with multitask learning In Proc. 25th ICML 160-167 (2008).
    • (2008) Proc 25th ICML , pp. 160-167
    • Collobert, R.1    Weston, J.2
  • 38
    • 84879853539 scopus 로고    scopus 로고
    • Stacked autoencoders for unsupervised feature learning and multiple organ detection in a pilot study using 4D patient data
    • Shin, H. C., Orton, M. R., Collins, D. J., Doran, S. J. & Leach, M. O. Stacked autoencoders for unsupervised feature learning and multiple organ detection in a pilot study using 4D patient data. IEEE T Pattern Anal 35(8), 1930-1943 (2013).
    • (2013) IEEE T Pattern Anal , vol.35 , Issue.8 , pp. 1930-1943
    • Shin, H.C.1    Orton, M.R.2    Collins, D.J.3    Doran, S.J.4    Leach, M.O.5
  • 39
    • 84939231326 scopus 로고    scopus 로고
    • Single sample face recognition via learning deep supervised auto-Encoders
    • Gao, S. H., Zhang, Y., Jia, K., Lu, J. & Zhang, Y. Single sample face recognition via learning deep supervised auto-Encoders. IEEE T Inf Foren Sec 10(10), 2108-2118 (2015).
    • (2015) IEEE T Inf Foren Sec , vol.10 , Issue.10 , pp. 2108-2118
    • Gao, S.H.1    Zhang, Y.2    Jia, K.3    Lu, J.4    Zhang, Y.5
  • 41
    • 84923943411 scopus 로고    scopus 로고
    • Improving the Mann-Whitney statistical test for feature selection: An approach in breast cancer diagnosis on mammography
    • Pérez, N. P. et al. Improving the Mann-Whitney statistical test for feature selection: An approach in breast cancer diagnosis on mammography. Artificial intelligence in medicine 63(1), 19-31 (2015).
    • (2015) Artificial Intelligence in Medicine , vol.63 , Issue.1 , pp. 19-31
    • Pérez, N.P.1
  • 42
    • 15944386717 scopus 로고    scopus 로고
    • Impact of breast density on computer-aided detection for breast cancer
    • Brem, R. F. et al. Impact of breast density on computer-aided detection for breast cancer. AJR Am J Roentgenol 184(2), 439-444 (2005).
    • (2005) AJR Am J Roentgenol , vol.184 , Issue.2 , pp. 439-444
    • Brem, R.F.1
  • 43
    • 0035195332 scopus 로고    scopus 로고
    • Tumor detection rate of a new commercially available computer-aided detection system
    • Malich, A. et al. Tumor detection rate of a new commercially available computer-aided detection system. Eur Radiol 12(10), 2454-2459 (2001).
    • (2001) Eur Radiol , vol.12 , Issue.10 , pp. 2454-2459
    • Malich, A.1
  • 44
    • 33748203640 scopus 로고    scopus 로고
    • Computerized analysis of tissue density effect on missed cancer detection in digital mammography
    • Li, L. H., Wu, Z. B. & Salem, A. F. Computerized analysis of tissue density effect on missed cancer detection in digital mammography. Comput Med Imag Grap 30(5), 291-297 (2006).
    • (2006) Comput Med Imag Grap , vol.30 , Issue.5 , pp. 291-297
    • Li, L.H.1    Wu, Z.B.2    Salem, A.F.3
  • 45
    • 48149108772 scopus 로고    scopus 로고
    • Fast fractal coding method for the detection of microcalcification in mammograms
    • Sankar, D. & Thomas, T. Fast fractal coding method for the detection of microcalcification in mammograms. Piscataway, NJ, IEEE 368-373 (2008).
    • (2008) Piscataway NJ IEEE , pp. 368-373
    • Sankar, D.1    Thomas, T.2
  • 46
    • 0033624996 scopus 로고    scopus 로고
    • CAD system for the automatic detection of clustered microcalcifications in digitized mammogram films
    • Yu, S. & Guan, L. A. CAD system for the automatic detection of clustered microcalcifications in digitized mammogram films. IEEE T Med Imaging 19(2), 115-126 (2000).
    • (2000) IEEE T Med Imaging , vol.19 , Issue.2 , pp. 115-126
    • Yu, S.1    Guan, L.A.2
  • 47
    • 33845339237 scopus 로고    scopus 로고
    • A genetic algorithm design for microcalcification detection and classification in digital mammograms
    • Jiang, J., Yao, B. & Wason, A. M. A genetic algorithm design for microcalcification detection and classification in digital mammograms. Comput Medical Imag Grap 31(1), 49-61 (2007).
    • (2007) Comput Medical Imag Grap , vol.31 , Issue.1 , pp. 49-61
    • Jiang, J.1    Yao, B.2    Wason, A.M.3
  • 48
    • 0345275908 scopus 로고    scopus 로고
    • Mammographic predictors of the presence and size of invasive carcinomasassociated with malignant microcalcification lesion without a mass
    • Stomper, P. C., Geradts, J. & Edge, S. B. Mammographic predictors of the presence and size of invasive carcinomasassociated with malignant microcalcification lesion without a mass. AJR Am J Roentgenol 181(6), 1679-1684 (2003).
    • (2003) AJR Am J Roentgenol , vol.181 , Issue.6 , pp. 1679-1684
    • Stomper, P.C.1    Geradts, J.2    Edge, S.B.3
  • 49
    • 0018951666 scopus 로고
    • Intramammary calcifications without an associated mass in benign and malignant diseases
    • Egan, R. L., McSweeney, M. B. & Sewell, C. W. Intramammary calcifications without an associated mass in benign and malignant diseases. Radiology 137(1), 1-7 (1980).
    • (1980) Radiology , vol.137 , Issue.1 , pp. 1-7
    • Egan, R.L.1    McSweeney, M.B.2    Sewell, C.W.3
  • 54
    • 84878398857 scopus 로고    scopus 로고
    • Training deep nets with imbalanced and unlabeled data
    • Berry, J. et al. Training deep nets with imbalanced and unlabeled data. Interspeech 1756-1759 (2012).
    • (2012) Interspeech , pp. 1756-1759
    • Berry, J.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.