Computational Intelligence and Healthcare Informatics - Группа авторов

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J., Identity mappings in deep residual networks, in: European conference on computer vision, 2016, October, Springer, Cham, pp. 630–645.

      23. Ho, T.K.K. and Gwak, J., Multiple feature integration for classification of thoracic disease in chest radiography. Appl. Sci., 9, 19, 4130, 2019.

      24. https://www.who.int/news-room/fact-sheets/detail/tuberculosis [accessed on 24 Nov. 2020]

      25. Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q., Densely connected convolutional networks, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4700–4708, 2017.

      26. Huang, Z., Lin, J., Xu, L., Wang, H., Bai, T., Pang, Y., Meen, T.H., Fusion High-Resolution Network for Diagnosing ChestX-ray Images. Electronics, 9, 1, 190, 2020.

      27. Hwang, S., Kim, H.E., Jeong, J., Kim, H.J., A novel approach for tuberculosis screening based on deep convolutional neural networks, in: Medical imaging 2016: computer-aided diagnosis, vol. 9785, pp. 97852W, International Society for Optics and Photonics, 2016 March.

      28. Islam, M.T., Aowal, M.A., Minhaz, A.T., Ashraf, K., Abnormality detection and localization in chest x-rays using deep convolutional neural networks. arXiv preprint arXiv:1705.09850, 1–16, 2017.

      29. Jaeger, S., Candemir, S., Antani, S., Wáng, Y.X.J., Lu, P.X., Thoma, G., Two public chest X-ray datasets for computer-aided screening of pulmonary diseases. Quant. Imaging Med. Surg., 4, 6, 475, 2014.

      30. Jaeger, S., Karargyris, A., Candemir, S., Folio, L., Siegelman, J., Callaghan, F., Thoma, G., Automatic tuberculosis screening using chest radiographs. IEEE Trans. Med. Imaging, 33, 2, 233–245, 2013.

      31. Jain, G., Mittal, D., Thakur, D., Mittal, M.K., A deep learning approach to detect Covid-19 coronavirus with X-Ray images. Biocybern. Biomed. Eng., 40, 4, 1391–1405, 2020.

      32. Jain, R., Gupta, M., Taneja, S., Hemanth, D.J., Deep learning based detection and analysis of COVID-19 on chest X-ray images. Appl. Intell., 51, 3, 1690–1700, 2020.

      33. Karargyris, A., Siegelman, J., Tzortzis, D., Jaeger, S., Candemir, S., Xue, Z., Thoma, G.R., Combination of texture and shape features to detect pulmonary abnormalities in digital chest X-rays. Int. J. Comput. Assist. Radiol. Surg., 11, 1, 99–106, 2016.

      34. Krizhevsky, A., Sutskever, I., Hinton, G.E., Imagenet classification with deep convolutional neural networks. Commun. ACM, 60, 6, 84–90, 2017.

      35. Lakhani, P. and Sundaram, B., Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology, 284, 2, 574–582, 2017.

      36. Li, R., Zhang, W., Suk, H.I., Wang, L., Li, J., Shen, D., Ji, S., Deep learning based imaging data completion for improved brain disease diagnosis, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, 2014, September, Springer, Cham, pp. 305–312.

      37. Li, Z., Wang, C., Han, M., Xue, Y., Wei, W., Li, L.J., Fei-Fei, L., Thoracic disease identification and localization with limited supervision, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8290–8299, 2018.

      38. Litjens, G., Kooi, T., Bejnordi, B.E., Setio, A.A.A., Ciompi, F., Ghafoorian, M., Sánchez, C.I., A survey on deep learning in medical image analysis. Med. Image Anal., 42, 60–88, 2017.

      40. Lopes, U.K. and Valiati, J.F., Pre-trained convolutional neural networks as feature extractors for tuberculosis detection. Comput. Biol. Med., 89, 135–143, 2017.

      41. Ma, Y., Zhou, Q., Chen, X., Lu, H., Zhao, Y., Multi-attention network for thoracic disease classification and localization, in: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019, May, IEEE, pp. 1378–1382.

      42. Melendez, J., Sánchez, C.I., Philipsen, R.H., Maduskar, P., Dawson, R., Theron, G., Van Ginneken, B., An automated tuberculosis screening strategy combining X-ray-based computer-aided detection and clinical information. Sci. Rep., 6, 25265, 2016.

      43. Mukherjee, A., Feature Engineering for Cardio-Thoracic Disease Detection from NIH Chest Radiographs, in: Computational Intelligence in Pattern Recognition, pp. 277–284, Springer, Singapore, 2020.

      44. Müller, R., Kornblith, S., Hinton, G.E., When does label smoothing help?, in: Advances in Neural Information Processing Systems, pp. 4694–4703, 2019.

      45. Ozturk, T., Talo, M., Yildirim, E.A., Baloglu, U.B., Yildirim, O., Acharya, U.R., Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput. Biol. Med., 121, 103792, 2020.

      46. Pasa, F., Golkov, V., Pfeiffer, F., Cremers, D., Pfeiffer, D., Efficient deep network architectures for fast chest X-ray tuberculosis screening and visualization. Sci. Rep., 9, 1, 1–9, 2019.

      47. Pham, H.H., Le, T.T., Tran, D.Q., Ngo, D.T., Nguyen, H.Q., Interpreting chest X-rays via CNNs that exploit disease dependencies and uncertainty labels. medRxiv, 19013342, 1–27, 2019.

      48. Qin, C., Yao, D., Shi, Y., Song, Z., Computer-aided detection in chest radiography based on artificial intelligence: a survey. Biomed. Eng. Online, 17, 1, 113, 2018.

      49. Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H., Duan, T., Lungren, M.P., Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning. arXiv preprint arXiv:1711.05225, 05225, 1–6, 2017.

      50. Roth, H.R., Lu, L., Liu, J., Yao, J., Seff, A., Cherry, K., Summers, R.M., Improving computer-aided detection using convolutional neural networks and random view aggregation. IEEE Trans. Med. Imaging, 35, 5, 1170–1181, 2015.

      51. Roy, S., Menapace, W., Oei, S., Luijten, B., Fini, E., Saltori, C., Peschiera, E., Deep learning for classification and localization of COVID-19 markers in point-of-care lung ultrasound. IEEE Trans. Med. Imaging, 39, 8, 2676–2687, 2020.

      52. Roy, S., Siarohin, A., Sangineto, E., Bulo, S.R., Sebe, N., Ricci, E., Unsupervised domain adaptation using feature-whitening and consensus loss, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9471–9480, 2019.

      53. Rozenberg, E., Freedman, D., Bronstein, A., Localization with Limited Annotation for Chest X-rays, in: Machine Learning for Health Workshop, 2020, April, PMLR, pp. 52–65.

      54. Ryoo, S. and Kim, H.J., Activities of the Korean institute of tuberculosis. Osong Public Health Res. Perspect., 5, S43–S49, 2014.

      55. Sajjadi, M., Javanmardi, M., Tasdizen, T., Regularization with stochastic transformations and perturbations for deep semi-supervised learning. Adv. Neural Inf. Process. Syst., 29, 1163–1171, 2016.

      56. Setio, A.A.A., Ciompi, F., Litjens, G., Gerke, P., Jacobs, C., Van Riel, S.J., van Ginneken, B., Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks. IEEE Trans. Med. Imaging, 35, 5, 1160–1169, 2016.

      57. Shen, W., Zhou, M., Yang, F., Yang, C., Tian, J., Multi-scale convolutional neural networks for lung nodule classification, in: International Conference on Information Processing in Medical Imaging, 2015, June, Springer, Cham, pp. 588–599.

      59. Shiraishi, J., Katsuragawa, S., Ikezoe, J., Matsumoto,

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