Computational Intelligence and Healthcare Informatics. Группа авторов

Simonyan, K. and Zisserman, A., Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, ICLR 2015, 1–14, 2014.

      61. Sirazitdinov, I., Kholiavchenko, M., Mustafaev, T., Yixuan, Y., Kuleev, R., Ibragimov, B., Deep neural network ensemble for pneumonia localization from a large-scale chest x-ray database. Comput. Electr. Eng., 78, 388–399, 2019.

      62. Soldati, G., Smargiassi, A., Inchingolo, R., Buonsenso, D., Perrone, T., Briganti, D.F., Tursi, F., Proposal for international standardization of the use of lung ultrasound for COVID-19 patients; a simple, quantitative, reproducible method. J. Ultrasound Med., 10, 39, 7, 1413–1419, 2020.

      63. Suk, H.I., Lee, S.W., Shen, D., Alzheimer’s Disease Neuroimaging Initiative. Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis. NeuroImage, 101, 569–582, 2014.

      64. Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A., Inception-v4, inception-resnet and the impact of residual connections on learning, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 31, No. 1, 2016.

      65. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Rabinovich, A., Going deeper with convolutions, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1–9, 2015.

      66. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z., Rethinking the inception architecture for computer vision, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2818–2826, 2016.

      67. Tang, Y.X., Tang, Y.B., Peng, Y., Yan, K., Bagheri, M., Redd, B.A., Summers, R.M., Automated abnormality classification of chest radiographs using deep convolutional neural networks. NPJ Digital Med., 3, 1, 1–8, 2020.

      68. Vajda, S., Karargyris, A., Jaeger, S., Santosh, K.C., Candemir, S., Xue, Z., Thoma, G., Feature selection for automatic tuberculosis screening in frontal chest radiographs. J. Med. Syst., 42, 8, 146, 2018.

      69. Wang, H. and Xia, Y., Chestnet: A deep neural network for classification of thoracic diseases on chest radiography. arXiv preprint arXiv:1807.03058, 1–8, 2018.

      70. Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., Summers, R.M., Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2097–2106, 2017.

      71. Yao, L., Poblenz, E., Dagunts, D., Covington, B., Bernard, D., Lyman, K., Learning to diagnose from scratch by exploiting dependencies among labels. arXiv preprint arXiv:1710.10501, 1–12, 2017.

      72. Zech, J.R., Badgeley, M.A., Liu, M., Costa, A.B., Titano, J.J., Oermann, E.K., Confounding variables can degrade generalization performance of radiological deep learning models. arXiv preprint arXiv:1807.00431, 1–15, 2018.

      73. Zhang, R., Making convolutional networks shift-invariant again. arXiv preprint arXiv:1904. 11486, In International Conference on Machine Learning, pp. 7324–7334, PMLR, 1–11, 2019.

      74. Zoph, B., Vasudevan, V., Shlens, J., Le, Q.V., Learning transferable architectures for scalable image recognition, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 8697–8710, 2018.

      1 *Corresponding author: [email protected]