[13] Suzuki, K. (2017) Survey of Deep Learning Applications to Medical Image Analysis. Medical Imaging Technology, 35, 212-226. [14] Kooi, T., van Ginneken, B., Larssemeijer, N. and den Heeten, A. (2017) Discriminating Solitary Cysts from Soft Tissue Lesions in Mammography Using a Pretrained Deep Convolutional Neural Network. Medical Physics, 44, 1017-1027.
https://www.ncbi.nlm.nih.gov/pubmed/28094850
https://doi.org/10.1002/mp.12110 [15] Lekadir, K., Galimzianova, A., Betriu, A., et al. (2017) A Convolutional Neural Network for Automatic Characterization of Plaque Composition in Carotid Ultrasound. IEEE Journal of Biomedical and Health Informatics, 21, 48-55.
https://ieeexplore.ieee.org/document/7752798/
https://doi.org/10.1109/JBHI.2016.2631401 [16] Shin, H.-C., Roth, H.R., Gao, M., et al. (2016) Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning. IEEE Transactions on Medical Imaging, 35, 1285-1298.
https://ieeexplore.ieee.org/document/7404017/
https://doi.org/10.1109/TMI.2016.2528162 [17] Kooi, T., Litjens, G., van Ginneken, B., et al. (2017) Large Scale Deep Learning for Computer Aided Detection of Mammographic Lesions. Medical Image Analysis, 35, 303-312.
https://doi.org/10.1016/j.media.2016.07.007 [18] Becker, A.S., Marcon, M., Ghafoor, S., et al. (2017) Deep Learning in Mammography: Diagnostic Accuracy of a Multipurpose Image Analysis Software in the Detection of Breast Cancer. Investigative Radiology, 52, 434-440.
https://doi.org/10.1097/RLI.0000000000000358 [19] Arevalo, J., Gonzáleza, F.A., Ramos-Pollán, R., et al. (2016) Representation Learning for Mammography Mass Lesion Classification with Convolutional Neural Networks. Computer Methods and Programs in Biomedicine, 127, 248-257.
https://doi.org/10.1016/j.cmpb.2015.12.014 [20] Jadoon, M.M., Zhang, Q., Haq, I.U., Butt, S. and Jadoon, A. (2017) Three-Class Mammogram Classification Based on Descriptive CNN Features. BioMed Research International, 2017, Article ID: 3640901.
https://doi.org/10.1155/2017/3640901 [21] Samala, R.K., Chan, H.-P., Hadjiiski, L., Cha, K. and Helvie, M.A. (2016) Deep-Learning Convolution Neural Network for Computer-Aided Detection of Microcalcifications in Digital Breast Tomosynthesis. Proceedings of SPIE, 9785, 1-7.
https://doi.org/10.1117/12.2217092 [22] Samala, R.K., Chan, H.-P., Hadjiiski, L., et al. (2016) Mass Detection in Digital Breast Tomosynthesis: Deep Convolutional Neural Network with Transfer Learning from Mammography. Medical Physics, 43, 6654-6666.
https://doi.org/10.1118/1.4967345 [23] Wang, J., Yang, X., Cai, H., et. al. (2016) Discrimination of Breast Cancer with Microcalcifications on Mammography by Deep Learning. Scientific Reports, 6, Article No. 27327.
https://www.nature.com/articles/srep27327
https://doi.org/10.1038/srep27327 [24] Kallenberg, M., Petersen, K., Nielsen, M., et al. (2016) Unsupervised Deep Learning Applied to Breast Density Segmentation and Mammographic Risk Scoring. IEEE Transactions on Medical Imaging, 35, 1322-1331.
https://doi.org/10.1109/TMI.2016.2532122 [25] Dubrovina, A., Kisilev, P., Ginsburg, B., Hashoul, S. and Kimmel, R. (2016) Computational Mammography Using Deep Neural Networks. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 6, 243-247.
https://www.tandfonline.com/doi/abs/10.1080/21681163.2015.1131197
https://doi.org/10.1080/21681163.2015.1131197 [26] Mohamed, A.A., Berg, W.A., Peng, H., et al. (2018) A Deep Learning Method for Classifying Mammographic Breast Density Categories. Medical Physics, 45, 314-321.
https://www.ncbi.nlm.nih.gov/pubmed/29159811
https://doi.org/10.1002/mp.12683 [27] America College of Radiology (2019) ACR BI-RADS Atlas 5th Edition.
https://www.acr.org/Clinical-Resources/Reporting-and-Data-Systems/Bi-Rads [28] Berg,W.A., Campassi, C., Langenberg, P. and Sexton, M.J. (2000) Breast Imaging Reporting and Data System: Inter- and Intraobserver Variability in Feature Analysis and Final Assessment. American Journal of Roentgenology, 174, 1769-1777.
https://doi.org/10.2214/ajr.174.6.1741769 [29] Winkler, N.S., Raza, S., Mackesy, M. and Birdwell, R.L. (2015) Breast Density: Clinical Implications and Assessment Methods. RadioGraphics, 35, 316-324.
https://doi.org/10.1148/rg.352140134 [30] Matsuyama, E. and Tsai, D.-Y. (2018) Automated Classification of Lung Diseases in Computed Tomography Images Using a Wavelet Based Convolutional Neural Network. Journal of Biomedical Science and Engineering, 11, 263-274.
https://file.scirp.org/pdf/JBiSE_2018102416370696.pdf
https://doi.org/10.4236/jbise.2018.1110022 [31] Krizhevsky, A., Sutskever, I. and Hinton, G.E. (2017) ImageNet Classification with Deep Convolutional Neural Networks. Communications of the ACM, 60, 84-90.
https://doi.org/10.1145/3065386 [32] Deng, J., Dong, W., Socher, R., et al. (2009) ImageNet: A Large-Scale Hierarchical Image Database. 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, 20-25 June 2009, 2-9.
https://ieeexplore.ieee.org/document/5206848/
https://doi.org/10.1109/CVPR.2009.5206848 [33] The Cancer Imaging Archive Collections (2019) Frederick National Laboratory for Cancer Research.
https://www.cancerimagingarchive.net [34] Matsuyama, E., Tsai, D.-Y., Lee, Y., et al. (2013) A Modified Undecimated Discrete Wavelet Transform Based Approach to Mammographic Image Denoising. Journal of Digital Imaging, 26, 748-758.
https://link.springer.com/article/10.1007/s10278-012-9555-6
https://doi.org/10.1007/s10278-012-9555-6 [35] Daubechies, I. (1992) Ten Lectures on Wavelets. The Society for Industrial and Applied Mathematics, Philadelphia, PA.
https://doi.org/10.1137/1.9781611970104 [36] Oshima, A., Kamiya, N., Shinohara, N., et al. (2019) Automatic Classification of Mammary Gland Density in Mammograms Using AlexNet. Medical Imaging and Information Sciences, 36, 59-63. [37] Koshidaka, M., Enomoto, K., Teramoto, A., et al. (2019) Preliminary Study on the Automated Classification of Breast Density in Mammogram Using Deep Convolutional Neural Network. Medical Imaging and Information Sciences, 36, 88-92.

  
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