Поиск путей разработки системы содействия принятию решения в маммографической диагностике с использованием искусственных нейронных сетей

Поиск путей разработки системы содействия принятию решения в маммографической диагностике с использованием искусственных нейронных сетей

Автор: Карюхина Ксения Михайловна, Ворновских Ксения Алексеевна, Масликова Ульяна Владиславовна, Супильников Алексей Александрович

Журнал: Вестник медицинского института "РЕАВИЗ": реабилитация, врач и здоровье @vestnik-reaviz

Рубрика: Клиническая медицина

Статья в выпуске: 5 (41), 2019 года.

Бесплатный доступ

Рак молочной железы является весьма распространенным заболеванием. Главным принципом успешного лечения этого заболевания является раннее выявление изменений в тканях молочной железы, а основным методом диагностики является маммография. Чувствительность маммографии колеблется между приблизительно 70 % и 90 %, в зависимости от различных факторов. В последние десятилетия наблюдается растущий интерес к разработке и использованию методов обработки маммографических изображений с целью повышения точности диагностики рака молочной железы. В статье представлены характеристики различных способов повышения информативности маммографии.

Еще

Рак молочной железы, маммография, нейронные сети

Короткий адрес: https://sciup.org/143172258

IDR: 143172258

Список литературы Поиск путей разработки системы содействия принятию решения в маммографической диагностике с использованием искусственных нейронных сетей

  • Siegel R.L., Miller K.D., Jemal A., Cancer Stat., CA. Cancer J. Clin. 67 (2017) 7-30, DOI: 10.3322/caac.21387
  • I. Schreer, Dense breast tissue as an important risk factor for breast cancer and implications for early detection, Breast Care 4 (2009) 89-92, 10.1159/ 000211954. DOI: 10.1159/000211954
  • Kerlikowske K., Carney P.A., Geller B. et al. Performance of screening mammography among women with and without a first-degree relative with breast cancer, Ann. Intern. Med. 133 (2000) 855-863.
  • Berlin L. Radiologic errors, past, present and future, Diagnosis 1 (2014) 79-84, DOI: 10.1515/dx-2013-0012
  • Whang J.S., Baker S.R., Patel R., Luk L., Castro A. The causes of medical mal-practice suits against radiologists in the United States, Radiology 266 (2013) 548-554, DOI: 10.1148/radiol.12111119
  • Sickles E.A. Periodic mammographic follow-up of probably benign lesions: results in 3,184 consecutive cases, Radiology 179 (1991) 463-468, 10.1148/ Radiology.179.2.2014293.
  • DOI: 10.1148/radiology.179.2.2014293
  • Bengio Y. Learning deep architectures for AI, found, Trends® Mach. Learn 2 (2009) 1-127,
  • DOI: 10.1561/2200000006
  • Bengio Y., Courville, A. Vincent P. Representation learning: a review and new perspectives, IEEE Trans. Pattern Anal. Mach. Intell. 35 (2013) 1798-1828,
  • DOI: 10.1109/TPAMI.2013.50
  • Schmidhuber J. Deep learning in neural networks: an overview, neural netw. 61 (2015) 85-117,
  • DOI: 10.1016/j.neunet.2014.09.003
  • LeCun Y., Bengio Y., Hinton G. Deep learning, Nature 521 (2015) 436-444,
  • DOI: 10.1038/nature14539
  • LeCun Y., Kavukcuoglu K., Farabet C. Convolutional networks and applications in vision, in: Proc. 2010 IEEE Int. Symp. Circuits Syst., 2010, pp. 253-256,
  • DOI: 10.1109/ISCAS.2010.5537907
  • Ciompi F., B. de Hoop, S.J. van Riel et al. 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 (2015) 195-202,
  • DOI: 10.1016/j.media.2015.08.001
  • Chen H., Ni D., Qin J. et al. Standard plane local-ization in fetal ultrasound via domain transferred deep neural networks, IEEE
  • Biomed J. Health Inf. 19 (2015) 1627-1636,
  • DOI: 10.1109/JBHI.2015.2425041
  • Shin H.C., Roth H.R., Gao M. et al. Deep convolutional neural networks for computer-aided de-tection: CNN architectures, dataset characteristics and transfer learning, IEEE Trans. Med. Imaging 35 (2016) 1285-1298,
  • DOI: 10.1109/TMI.2016.2528162
  • Chougrad H. et al. / Computer Methods and Programs in Biomedicine 157 (2018) 19-30.
  • W. Peng, R.V. Mayorga, E.M.A. Hussein, An automated confirmatory system for analysis of mammograms, Comput. Methods Programs Biomed. 125 (2016) 134-144,
  • DOI: 10.1016/j.cmpb.2015.09.019
  • Carneiro G., Nascimento, J. Bradley A.P. Unregistered multiview mammogram analysis with pre-trained deep learning models, in: Int. Conf. Med. Image Com-put. Comput.-Assist. Interv., Springer, 2015, pp. 652-660. http://link.springer. com/chapter/.
  • DOI: 10.1007/978-3-319-24574-4_78
  • J. Arevalo, F.A. González, R. Ramos-Pollán, J.L. Oliveira, M.A. Guevara Lopez, Representation learning for mammography mass lesion classification with con-volutional neural networks, Comput. Methods Programs Biomed. 127 (2016) 248-257,
  • DOI: 10.1016/j.cmpb.2015.12.014
  • Jiao Z., Gao X., Wang Y., Li J. A deep feature based framework for breast masses classification, Neurocomputing 197 (2016) 221-231, 10.1016/j. neucom.2016.02.060.
  • DOI: 10.1016/j.neucom.2016.02.060
  • Oquab M., Bottou L., Laptev I., Sivic J. Learning and transferring mid-level image representations using convolutional neural networks, in: Proc. IEEE Conf. Comput. Vis. Pattern Recognit, 2014, pp. 1717-1724.
  • Chatfield K., Simonyan K., Vedaldi A., Zisserman A. Return of the devil in the details: delving deep into convolutional nets, ArXiv14053531 Cs. (2014). http: //arxiv.org/abs/1405.3531 (accessed June 2, 2017).
  • Simonyan K., Zisserman A. Very deep convolutional networks for large-scale image recognition, ArXiv14091556 Cs. (2014). http://arxiv.org/abs/1409.1556 (accessed February 10, 2017).
  • He K., Zhang X., Ren S., Sun J. Deep residual learning for image recognition, in: Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2016, pp. 770-778.
  • Szegedy C., Vanhoucke V., Ioffe S., Shlens J., Wojna Z. Rethinking the incep-tion architecture for computer vision, in: Proc. IEEE Conf. Comput. Vis.Pattern Recognit., 2016, pp. 2818-2826.
  • Deng J., Dong, W. Socher R., L.J. Li, K. Li, L. Fei-Fei, ImageNet: a large-scale hierarchical image database, in: 2009 IEEE Conf. Comput. Vis. Pattern Recognit., 2009, pp. 248-255,
  • DOI: 10.1109/CVPR.2009.5206848
  • Halevy A., Norvig P., Pereira F. The unreasonable effectiveness of data, IEEE Intell. Syst. 24 (2009) 8-12,
  • DOI: 10.1109/MIS.2009.36
  • Heath M., Bowyer K., Kopans D., Moore R., Kegelmeyer W.P. The digital database for screening mammography, in: Proc. 5th Int. Workshop Digit. Mammogr., Medical Physics Publishing, 2000, pp. 212-218.
  • Lopez M.G., Posada N., Moura D.C. et al. BCDR: a breast cancer digital repository, 15th Int. Conf. Exp. Mech., 2012 http://paginas.fe.up.pt/clme/ icem15/ICEM15_CD/data/papers/3004.pdf accessed January 27, 2017.
  • Moreira I.C., Amaral I., Domingues I. et al. INbreast: toward a full-field digital mammographic database, Acad. Radiol 19 (2012) 236-248,
  • DOI: 10.1016/j.acra.2011.09.014
  • Wong S.C., Gatt, A. Stamatescu V., McDonnell M.D. Understanding data aug-mentation for classification: when to warp?, ArXiv160908764 Cs. (2016). http: //arxiv.org/abs/1609.08764.
  • Bengio Y. Deep learning of representations for unsupervised and trans-fer learning, in: PMLR, 2012, pp. 17-36. http://proceedings.mlr.press/v27/ bengio12a.html. (accessed October 19, 2017).
  • Lakkaraju H., Socher R., Manning C. Aspect specific sentiment analysis using hierarchical deep learning, NIPS Workshop Deep Learn. Represent. Learn., 2014.
  • Jaderberg M., Simonyan K., Zisserman A. Spatial transformer networks, in: Adv. Neural Inf. Process. Syst., 2015, pp. 2017-2025.
  • Yosinski J., Clune J., Bengio Y., Lipson H. How transferable are features in deep neural networks? in: Adv. Neural Inf. Process. Syst., 2014, pp. 3320-3328.
  • Bengio Y., Practical Recommendations for Gradient-Based Training of Deep Ar-chitectures, in: Neural Netw. Tricks Trade, Springer, Berlin, Heidelberg, 2012, pp. 437-478,
  • DOI: 10.1007/978-3-642-35289-8_26
  • Kingma D.P., Ba J.,Adam: a method for stochastic optimization, ArXiv14126980 Cs. (2014). http://arxiv.org/abs/1412.6980 (accessed January 31, 2017).
  • Srivastava N., Hinton G.E., Krizhevsky A., Sutskever I., Salakhutdinov R. Dropout: a simple way to prevent neural networks from overfitting, J. Mach. Learn. Res. 15 (2014) 1929-1958.
  • Theano Development Team, Theano: a python framework for fast computa-tion of mathematical expressions, ArXiv E-Prints. abs/1605.02688 (2016). http: //arxiv.org/abs/1605.02688.
  • Suckling J et al. The mammographic image analysis society digital mammo-gram database exerpta medica, Int. Congr. Ser. 1069 (1994) 375-378.
  • Elmore J.G., Jackson S.L., Abraham L. et al. D.S.M. Buist, Variability in interpretive performance at screening mammogra-phy and radiologists' characteristics associated with accuracy1, Radiology 253 (2009) 641-651,
  • DOI: 10.1148/radiol.2533082308
  • Joy J.E., Penhoet E.E., Petitti D.B., I. of M. (US) and N.R.C. (US) C. on N.A. to E.D. and D. of B., Cancer, Benefits and Limitations of Mammography, National Academies Press, US, 2005 https://www.ncbi.nlm.nih.gov/books/NBK22311/ ac-cessed June 8, 2017.
  • Berg W.A. et al. Detection of breast cancer with addition of annual screening ultrasound or a single screen-ing MRI to mammography in women with elevated breast cancer risk, JAMA J. Am. Med. Assoc. 307 (2012) 1394-1404,
  • DOI: 10.1001/jama.2012.388
  • Salem D.S., Kamal R.M., Mansour S.M., Salah L.A., Wessam R. Breast imaging in the young: the role of magnetic resonance imaging in breast cancer screen-ing, diagnosis and follow-up, J. Thorac. Dis. 5 (2013) S9-S18, 10.3978/j.issn. 2072-1439.2013.05.02.
  • DOI: 10.3978/j.issn.2072-1439.2013.05.02
Еще
Статья научная
QR-код для установки приложения SciUp Наведите камеру и скачайте бесплатное приложение SciUp