Computational Intelligence and Healthcare Informatics. Группа авторов

Чтение книги онлайн.

СКАЧАТЬ 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]

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