참고문헌
- http://www.ncc.re.kr, accessed on Apr. 24, 2018.
- H. Katai et al., "Five-year survival analysis of surgically resected gastric cancer cases in Japan: a retrospective analysis of more than 100,000 patients from the nationwide registry of the Japanese Gastric Cancer Association (2001-2007)," Gastric Cancer, vol. 21, no. 1, pp. 144-154, 2018. https://doi.org/10.1007/s10120-017-0716-7
- H. A. Park et al., "The Korean guideline for gastric cancer screening," J. Korean Med. Assoc., vol. 58, no. 5, pp. 373-384, 2015. https://doi.org/10.5124/jkma.2015.58.5.373
- T. Hirasawa et al., "Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images," Gastric Cancer, no.0123456789, pp. 1-8, 2018.
- S. Menon and N. Trudgill, "How commonly is upper gastrointestinal cancer missed at endoscopy? A meta-analysis," Endosc. Int. Open, vol. 02, no. 02, pp. E46-E50, 2014. https://doi.org/10.1055/s-0034-1365524
- Y. Shimodate et al., "Gastric superficial neoplasia : high miss rate but slow progression," no. December 2014, pp. 722-726, 2017.
- K. Y. Hosokawa O, Hattori M, Douden K, Hayashi H, Ohta K, "Difference in accuracy between gastroscopy and colonoscopy for detection of cancer.," Hepatogastroenterology, vol. 54, pp. 442-444, 2007.
- M. Hafner, A. Gangl, M. Liedlgruber, A. Uhl, A. Vecsei, and F. Wrba, "Combining Gaussian Markov random fields with the discretewavelet transform for endoscopic image classification," DSP 2009 16th Int. Conf. Digit. Signal Process. Proc., pp. 1-6, 2009.
- P. Wang, S. M. Krishnan, C. Kugean, and M. P. Tjoa, "Classification of endoscopic images based on texture and neural network," Annu. Reports Res. React. Institute, Kyoto Univ., vol. 4, pp. 3691-3695, 2001.
- G. H. Yann LeCun, Yoshua Bengio, "Deep learning," Nature, vol. 521, pp. 436-444, 2015. https://doi.org/10.1038/nature14539
- M. I. Razzak, S. Naz, and A. Zaib, "Deep Learning for Medical Image Processing: Overview, Challenges and Future," CoRR, vol. 1704.06825, pp. 1-30, 2017.
- A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet Classification with Deep Convolutional Neural Networks," Adv. Neural Inf. Process. Syst., pp. 1-9, 2012.
- K. He, "Delving Deep into Rectifiers : Surpassing Human-Level Performance on ImageNet Classification," 2014.
- V. Gulshan et al., "Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs," JAMA-J. Am. Med. Assoc., vol. 316, no. 22, pp. 2402-2410, 2016. https://doi.org/10.1001/jama.2016.17216
- A. Esteva et al., "Dermatologist-level classification of skin cancer with deep neural networks," Nature, vol. 542, no. 7639, pp. 115-118, 2017. https://doi.org/10.1038/nature21056
- H. C. Shin et al., "Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning," IEEE Trans. Med. Imaging, vol. 35, no. 5, pp. 1285-1298, 2016. https://doi.org/10.1109/TMI.2016.2528162
- G. Wimmer, A. Vecsei, and A. Uhl, "CNN Transfer Learning for the Automated Diagnosis of Celiac Disease," 2016.
- F. Zhang, X. Xu, and Y. Qiao, "Deep classification of vehicle makers and models: The effectiveness of pre-training and data enhancement," 2015 IEEE Int. Conf. Robot. Biomimetics, IEEE-ROBIO 2015, pp. 231-236, 2015.
- K. Simonyan and A. Zisserman, "Very Deep Convolutional Networks for Large-Scale Image Recognition," pp. 1-14, 2014.
- C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, "Rethinking the Inception Architecture for Computer Vision," 2015.
- K. He, X. Zhang, S. Ren, and J. Sun, "Deep Residual Learning for Image Recognition," 2016 IEEE Conf. Comput. Vis. Pattern Recognit., pp. 770-778, 2016.
- L. F.-F. Olga Russakovsky,Jia Deng,Hao Su,Jonathan Krause,Sanjeev Satheesh,Sean Ma,Zhiheng Huang,Andrej Karpathy,Aditya Khosla,Michael Bernstein,Alexander C. Berg, "ImageNet Large Scale Visual Recognition Challenge," Int. J. Comput. Vis., vol. 115, pp. 211-252, 2015. https://doi.org/10.1007/s11263-015-0816-y
- A. Y. Ng, "Preventing 'Overfitting' of Cross-Validation data," CEUR Workshop Proc., vol. 1542, pp. 33-36, 2015.
- R. R. Selvaraju, A. Das, R. Vedantam, M. Cogswell, D. Parikh, and D. Batra, "Grad-CAM: Why did you say that?," pp. 1-4, 2016.
- M. Lin, Q. Chen, and S. Yan, "Network In Network," pp. 1-10, 2013.