References
[1]. Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L.
H., & Aerts, H. J. (2018). Artificial Intelligence in radiology.
Nature Reviews Cancer, 18(8),500–510. https://doi.org/
10.1038/s41568-018-0016-5
[2]. Kilickaya, M., Erdem, A., Ikizler-Cinbis, N., & Erdem, E.
(2017). Re-evaluating automatic metrics for Image
captioning. In Proceedings of the 15th Conference of the
European Chapter of the Association for Computational
Linguistics: Volume 1, Long Papers. https://doi.org/10.186
53/v1/e17-1019
[3]. Kohli, M., Prevedello, L. M., Filice, R. W., & Geis, J. R.
(2017). Implementing machine learning in radiology
practice and research. American Journal of
Roentgenology, 208(4), 754–760. https://doi.org/10.22
14/ajr.16.17224
[4]. Mazurowski, M. A. (2019). Artificial intelligence may
cause a significant disruption to the radiology workforce.
Journal of the American College of Radiology, 16(8), 1077–1082. https://doi.org/10.1016/j.jacr.2019.01.026
[5]. National Electrical Manufacturers Association.
(2003). Digital imaging and communications in
medicine (DICOM). Retrieved from https://ci.nii.ac.jp/
naid/10028228113/
[6]. Noguerol, T. M., Paulano-Godino, F., Martín-Valdivia,
M. T., Menias, C. O., & Luna, A. (2019). Strengths,
weaknesses, opportunities, and threats analysis of
artificial intelligence and machine learning applications
in radiology. Journal of the American College of
Radiology, 16(9), 1239-1247. https://doi.org/10.1016/j.
jacr.2019.05.047
[7]. Schier, R. (2018). Artificial intelligence and the
practice of radiology: An alternative view. Journal of the
American College of Radiology, 15(7), 1004-1007.
https://doi.org/10.1016/j.jacr.2018.03.046
[8]. Vedantam, R., Zitnick, C. L., & Parikh, D. (2015). Cider:
Consensus-based Image Description Evaluation. In 2015,
IEEE Conference on Computer Vision and Pattern
Recognition (CVPR). https://doi.org/10.1109/cvpr.2015.
7299087