[1] Sefidbakht S., Haseli S., Khalili N., Bazojoo V., Keshavarz P., Zeinali-Rafsanjani B. (2021). “Can shear wave elastography be utilized as an additional tool for the assessment of non-mass breast lesions?” Ultrasound, 1742271X21998721.
[2] Bray F., Ferlay J., Soerjomataram I., Siegel R. L., Torre L. A., Jemal A. (2018). “Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries”, CA: A Cancer Journal for Clinicians, 68(6), 394-424.
[3] Bi W. L., Hosny A., Schabath M. B., Giger M. L., Birkbak N. J., Mehrtash A., Aerts H. J. (2019). “Artificial intelligence in cancer imaging: clinical challenges and applications”, CA: A Cancer Journal for Clinicians, 69(2), 127-157.
[4] Sim L. L. W., Ban K. H. K., Tan T. W., Sethi S. K., Loh T. P. (2017). “Development of a clinical decision support system for diabetes care: A pilot study”, PloS one, 12(2), e0173021.
[5] Gandomkar Z., Suleiman M. E., Demchig D., Brennan P. C., McEntee M. F. (2019). “BI-RADS density categorization using deep neural networks” In Medical Imaging 2019: Image Perception, Observer Performance, and Technology Assessment (Vol. 10952, p. 109520N), International Society for Optics and Photonics.
[6] O’Connor J. P., Rose C. J., Waterton J. C., Carano R. A., Parker G. J., Jackson A. (2015). “Imaging intratumor heterogeneity: role in therapy response, resistance, and clinical outcome”, Clinical Cancer Research, 21(2), 249-257.
[7] Boyer B., Canale S., Arfi-Rouche J., Monzani Q., Khaled W., Balleyguier C. (2013). “Variability and errors when applying the BIRADS mammography classification”, European Journal of Radiology, 82(3), 388-397.
[8] Sefidbakht S., Jalli R., Izadpanah E. (2015). “Adherence of Academic Radiologists in a Non-English Speaking Imaging Center to the BI-RADS Standards of Reporting Breast MRI”, Journal of clinical imaging science, 5.
[9] Guo D., Wang Q., Liang M., Liu W., Nie J. (2019). “Molecular cavity topological representation for pattern analysis: a NLP analogy-based Word2Vec method”, International Journal of Molecular Sciences, 20(23), 6019.
[10] Dehghan P., Mogharabi M., Zabbah I., Layeghi K., Maroosi A. (2018). “Modeling Breast cancer using data mining methods (In Persian)”.
[11] Gao H., Bowles E. J. A., Carrell D., Buist D. S. (2015). “Using natural language processing to extract mammographic findings”, Journal of biomedical informatics, 54, 77-84.
[12] KENNETH W. C. (2017). “Word2Vec”, Natural Language Engineering, 23(1), 155-162.
[13] Kumar I., Virmani J., Bhadauria H. S., Thakur S. (2019). “A breast tissue characterization framework using PCA and weighted score fusion of neural network classifiers”, In Classification Techniques for Medical Image Analysis and Computer Aided Diagnosis (pp.129-151). Academic Press.
[14] Akbarin M. M., Shirdel A., Bari A., Mohaddes S. T., Rafatpanah H., Karimani E. G.,Torshizi R. (2017). “Evaluation of the role of TAX, HBZ, and HTLV-1 proviral load on the survival of ATLL patients”, Blood research, 52(2), 106-111.
[15] Boostani R., Karimzadeh F., Nami M. (2017). “A comparative review on sleep stage classification methods in patients and healthy individuals”, Computer methods and programs in biomedicine, 140, 77-91.
[16] Boroumandzadeh M., Parvinnia E. (2021). “Automated classification of BI-RADS in textual mammography reports”, Turkish Journal of Electrical Engineering & Computer Sciences, 29(2), 632-647.
[17] Kamali T., Boostani R., Parsaei H. (2013). “A multi-classifier approach to MUAP classification for diagnosis of neuromuscular disorders”, IEEE transactions on neural systems and rehabilitation engineering, 22(1), 191-200.
[18] Nezam T., Boostani R., Abootalebi V., Rastegar K. (2018). “A novel classification strategy to distinguish five levels of pain using the EEG signal features”, IEEE Transactions on Affective Computing, 12(1), 131-140.
[19] Torshizi R., Karimani E. G., Etminani K., Akbarin M. M., Jamialahmadi K., Shirdel A., Rafatpanah H. (2017). “Altered expression of cell cycle regulators in adult T-cell leukemia/lymphoma patients”, Reports of biochemistry & molecular biology, 6(1), 88.
[20] Zarei H. (2016). “The control parametrization enhancing technique for multi-objective optimal control of HIV dynamic”, Control and Optimization in Applied Mathematics, 1(2), 1-21.
[21] Boroumandzadeh M., Parvinnia E. (2021). “Proposing a clinical decision support system for breast cancer diagnosis”, Journal of Knowledge & Health in Basic Medical Sciences, 15 (3), 54-66.
[22] Esmaeili M., Ayyoubzadeh S. M., Ahmadinejad N., Ghazisaeedi M., Nahvijou A., Maghooli K. (2020). “A decision support system for mammography reports interpretation”, Health Information Science and Systems, 8(1), 1-8.
[23] Boumaraf S., Liu X., Ferkous C., Ma, X. (2020). “A new computer-aided diagnosis system with modified genetic feature selection for bi-RADS classification of breast masses in mammograms”, BioMed Research International, 2020.
[24] Borkowski K., Rossi C., Ciritsis A., Marcon M., Hejduk P., Stieb S., Berger N. (2020). “Fully automatic classification of breast MRI background parenchymal enhancement using a transfer learning approach”, Medicine, 99(29).
[25] Bozkurt S., Gimenez F., Burnside E. S., Gulkesen K. H., Rubin D. L. (2016). “Using automatically extracted information from mammography reports for decision-support”, Journal of Biomedical Informatics, 62, 224-231.
[26] Castro S. M., Tseytlin E., Medvedeva O., Mitchell K., Visweswaran S., Bekhuis T., Jacobson R. S. (2017). “Automated annotation and classification of BI-RADS assessment from radiology reports”, Journal of Biomedical Informatics, 69, 177-187.
[27] Destrempes F., Trop I., Allard L., Chayer B., Garcia-Duitama J., El Khoury M., Cloutier G. (2020). “Added value of quantitative ultrasound and machine learning in BI-RADS 4–5 assessment of solid breast lesions”, Ultrasound in Medicine & Biology, 46(2), 436-444.
[28] Gupta A., Banerjee I., Rubin D. L. (2018). “Automatic information extraction from unstructured mammography reports using distributed semantics”, Journal of Biomedical Informatics, 78, 78-86.
[29] Percha B., Nassif H., Lipson J., Burnside E., Rubin, D. (2012). “Automatic classification of mammography reports by BI-RADS breast tissue composition class”, Journal of the American Medical Informatics Association, 19(5), 913-916.
[30] Sippo D. A., Warden G. I., Andriole K. P., Lacson R., Ikuta I., Birdwell R. L., Khorasani R. (2013). “Automated extraction of BI-RADS final assessment categories from radiology reports with natural language processing”, Journal of Digital Imaging, 26(5), 989-994.
[31] Zhang X., Zhang Y., Zhang Q., Ren Y., Qiu T., Ma J., Sun Q. (2019). “Extracting comprehensive clinical information for breast cancer using deep learning methods”, International Journal of Medical Informatics, 132, 103985.
[32] Chakraborty D., Chiracharit W., Chamnongthai K. (2021). “Video Shot Boundary Detection Using Principal Component Analysis (PCA) and Deep Learning”, In 2021 18th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON) (pp. 272-275). IEEE.
[33] Chen D., Lv J., Zhang Y. (2017). “Unsupervised multi-manifold clustering by learning deep representation”, In Workshops at the thirty-first AAAI Conference on Artificial Intelligence.
[34] Huber K. E., Carey L. A., Wazer D. E. (2009). “Breast cancer molecular subtypes in patients with locally advanced disease: impact on prognosis, patterns of recurrence, and response to therapy”, In Seminars in Radiation Oncology (Vol. 19, No. 4, pp. 204-210). WB Saunders.
[35] Alesheykh R. (2016). “Comparative analysis of machine learning algorithms with optimization purposes”, Control and Optimization in Applied Mathematics, 1(2), 63-75.
[36] Kalchbrenner N., Grefenstette E., Blunsom P. (2014). “A convolutional neural network for modeling sentences”, arXiv preprint arXiv:1404.2188.
[37] Xu Q., Zhang M., Gu Z., Pan G. (2019). “Overfitting remedy by sparsifying regularization on fully-connected layers of CNNs”, Neuro Computing, 328, 69-74.
[38] Jais I. K. M., Ismail A. R., Nisa S. Q. (2019). “Adam optimization algorithm for wide and deep neural network”, Knowledge Engineering and Data Science, 2(1), 41-46.
[39] Leung K. M. (2007). “Naive bayesian classifier”, Polytechnic University Department of Computer Science/Finance and Risk Engineering, 2007, 123-156.
[40] Akram M., Shahzadi G. (2020). “A hybrid decision-making model under q-rung orthopair fuzzy Yager aggregation operators”, Granular Computing, 1-15.
[41] Rizzi A., Panella M., Mascioli F. F. (2002). “Adaptive resolution min-max classifiers”, IEEE Transactions on Neural Networks, 13(2), 402-414.
[42] Shinde S. V., Kulkarni U. V. (2014). “Mining classification rules from fuzzy min-max neural network”, In Fifth International Conference on Computing, Communications and Networking Technologies (ICCCNT) (pp. 1-7). IEEE.
[43] Patrick K. S. (1992). “Fuzzy Min-Max Neural Networks-Part1: Classification”, IEEE Transactions on Neural Networks, 2, 776-786.
[44] Mohammed M. F., Lim C. P. (2017). “Improving the Fuzzy Min-Max neural network with a K-nearest hyperbox expansion rule for pattern classification”, Applied Soft Computing, 52, 135-145.
[45] Patro S., Sahu K. K. (2015). “Normalization: A preprocessing stage”, arXiv preprint arXiv:1503.06462.
[46] Tharwat A. (2018). “Classification assessment methods”, Applied Computing and Informatics. Advance online publication.
[47] Duan K. B., Keerthi S. S. (2005). “Which is the best multiclass SVM method? An empirical study”, In International workshop on multiple classifier systems (pp. 278-285). Springer, Berlin, Heidelberg.
[48] Li B., Drozd A., Guo Y., Liu T., Matsuoka S., Du X. (2019). “Scaling word2vec on big corpus”, Data Science and Engineering, 4(2), 157-175.
[49] Wu M., Zhong X., Peng Q., Xu M., Huang S., Yuan J., Tan T. (2019). “Prediction of molecular subtypes of breast cancer using BI-RADS features based on a “white box” machine learning approach in a multi-modal imaging setting”, European Journal of Radiology, 114, 175-184.
[50] Banerjee I., Bozkurt S., Alkim E., Sagreiya H., Kurian A. W., Rubin D. L. (2019). “Automatic inference of BI-RADS final assessment categories from narrative mammography report findings”, Journal of Biomedical Informatics, 92, 103137.
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