참고문헌
- Choi KT. Real-time Artificial Neural Network for High-dimensional Medical Image. Journal of the Korean Society of Radiology [Internet]. 2016 Dec; 10(8):637-43. Available from: https://doi.org/10.7742/JKSR.2016.10.8.637.
- Szegedy C, et al. Going deeper with convolutions. IEEE Conference on Computer Vision and Pattern Recognition [Internet]. 2015:1-9. Available from: https://ieeexplore.ieee.org/document/7298594.
- Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition [Internet]. arXiv. 2014. Avialable from: https://arxiv.org/abs/1409.1556.
- Abiyev RH, Ma'aitah MKS. Deep convolutional neural networks for chest diseases detection [Internet]. Journal of Healthcare Engineering. 2018. Available from: https://www.hindawi.com/journals/jhe/2018/4168538/.
- Xu S, Wu H, Bie R. Anomaly Detection on Chest X-Rays With Image-Based Deep Learning. IEEE Access. 2019;7:4466-77. https://doi.org/10.1109/ACCESS.2018.2885997
- Dunnmon JA, Yi D, Langlotz CP, et al. Assessment of Convolutional Neural Networks for Automated Classification of Chest Radiographs. Radiology. 2019;290(2):537-44. https://doi.org/10.1148/radiol.2018181422
- Baltruschat IM, Nickisch H, Grass M. Comparison of Deep Learning Approaches for Multi-Label Chest X-Ray Classification. Sci Rep. 2019;9(1):6381. https://doi.org/10.1038/s41598-019-42294-8
- Gil JW, Park JH, Park MH, Park CY, Kim SY, Shin DW, et al. Estimated Exposure Dose and Usage of Radiological Examination of the National Health Screening. Journal of Radiation Protection. 2014 Sep;39(3):142-9. https://doi.org/10.14407/jrp.2014.39.3.142
- Nahm KB. Automatic detection of the lung orientation in digital PA chest radiographs. Journal of the Optical Society of Korea. 1997;1(1):60-4. https://doi.org/10.3807/JOSK.1997.1.1.060
- Strickland NH. PACS (picture archiving and communication systems) filmless radiology. Arch dis Child. 2000;83:82-6. https://doi.org/10.1136/adc.83.1.82
- Boone JM, Seshagiri S, Steiner RM. Recognition of chest radiograph orientation for picture archiving and communications systems display using neural networks. Journal of digital imaging. 1992;5(3):190-3. https://doi.org/10.1007/BF03167769
- Sakai Y, Takahashi K, Shimizu Y, et al. Clinical application of biological fingerprints extracted from averaged chest radiographs and template-matching technique for preventing left-right flipping mistakes in chest radiography. Radiol Phys Technol. 2019;12(2):216-23. https://doi.org/10.1007/s12194-019-00504-y
- Shmizu Y, Matsunobu Y, Morishita J. Evaluation of the usefulness of modified biological fingerprints in chest radiographs for patient recognition and identification. Raiol Phys Technol. 2016;9(2):240-4. https://doi.org/10.1007/s12194-016-0355-4
- Shimizu Y, Morishita J. Development of a method of automated extraction of biological fingerprints from chest radiographs as preprocessing of patient recognition and identification. Radiol Phys Technol. 2017;10(3):376-81. https://doi.org/10.1007/s12194-017-0400-y
- Morishita J, Katsragawa S, Ssaki Y, Doi K. Potential Usefulness of Biological Fingerprints in Chest Radiographs for Automated Patient Recognition and Identification. Acad Radiol. 2004;11(3):309-15. https://doi.org/10.1016/S1076-6332(03)00655-X