Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209–49.

Article  PubMed  Google Scholar 

Suh YS, Lee J, Woo H, Shin D, Kong SH, Lee HJ, et al. National cancer screening program for gastric cancer in Korea: Nationwide treatment benefit and cost. Cancer. 2020;126(9):1929–39.

Article  PubMed  Google Scholar 

Mabe K, Inoue K, Kamada T, Kato K, Kato M, Haruma K. Endoscopic screening for gastric cancer in Japan: current status and future perspectives. Dig Endosc. 2022;34(3):412–9.

Article  PubMed  Google Scholar 

Jun JK, Choi KS, Lee H-Y, Suh M, Park B, Song SH, et al. Effectiveness of the Korean National Cancer Screening Program in Reducing Gastric Cancer Mortality. Gastroenterology. 2017;152(6):1319-28.e7.

Article  PubMed  Google Scholar 

Suzuki H, Takizawa K, Hirasawa T, Takeuchi Y, Ishido K, Hoteya S, et al. Short-term outcomes of multicenter prospective cohort study of gastric endoscopic resection: ‘Real-world evidence’ in Japan. Dig Endosc. 2019;31(1):30–9.

Article  PubMed  Google Scholar 

Shichijo S, Uedo N, Kanesaka T, Ohta T, Nakagawa K, Shimamoto Y, et al. Long-term outcomes after endoscopic submucosal dissection for differentiated-type early gastric cancer that fulfilled expanded indication criteria: a prospective cohort study. J Gastroenterol Hepatol. 2021;36(3):664–70.

Article  PubMed  Google Scholar 

Chiu PWY, Uedo N, Singh R, Gotoda T, Ng EKW, Yao K, et al. An Asian consensus on standards of diagnostic upper endoscopy for neoplasia. Gut. 2019;68(2):186–97.

Article  PubMed  Google Scholar 

Sugita T, Suzuki S, Ichijima R, Ogura K, Kusano C, Ikehara H, et al. Diagnostic ability of high-definition imaging using ultraslim endoscopes in early gastric cancer. J Gastric Cancer. 2021;21(3):246.

Article  PubMed  PubMed Central  Google Scholar 

Ren W. Missed diagnosis of early gastric cancer or high-grade intraepithelial neoplasia. World J Gastroenterol. 2013;19(13):2092.

Article  PubMed  PubMed Central  Google Scholar 

Okabayashi T, Gotoda T, Kondo H, Ono H, Oda I, Fujishiro M, et al. Usefulness of indigo carmine chromoendoscopy and endoscopic clipping for accurate preoperative assessment of proximal gastric cancer. Endoscopy. 2000;32(10):Suppl 62.

Google Scholar 

Yasuda T, Yagi N, Omatsu T, Hayashi S, Nakahata Y, Yasuda Y, et al. Benefits of linked color imaging for recognition of early differentiated-type gastric cancer: in comparison with indigo carmine contrast method and blue laser imaging. Surg Endosc. 2021;35(6):2750–8.

Article  PubMed  Google Scholar 

Zhao Z, Yin Z, Wang S, Wang J, Bai B, Qiu Z, et al. Meta-analysis: the diagnostic efficacy of chromoendoscopy for early gastric cancer and premalignant gastric lesions. J Gastroenterol Hepatol. 2016;31(9):1539–45.

Article  PubMed  Google Scholar 

Strassburg CP, Nattermann J, Hüneburg R, Heling D, Kaczmarek DJ, Van Heteren P, et al. Dye chromoendoscopy leads to a higher adenoma detection in the duodenum and stomach in patients with familial adenomatous polyposis. Endosc Int Open. 2020;08(10):E1308–14.

Article  Google Scholar 

Hoyez H, Schockaert C, Rambach J, Mirbach B, Stricker D. Unsupervised image-to-image translation: a Review. Sensors. 2022;22(21):8540.

Article  PubMed  PubMed Central  Google Scholar 

Zhu JY, Park T, Isola P, Efros AA. Unpaired image-to-image translation using cycle-consistent adversarial networks. 2017 IEEE Int Conf Comput Vision (ICCV). 2017. https://doi.org/10.1109/ICCV.2017.244.

Article  Google Scholar 

Yuan X, Gong W, Hu B. Virtual indigo carmine dyeing: new artificial intelligence-based chromoendoscopy technique. Dig Endosc. 2023;35(1):e8–10.

Article  PubMed  Google Scholar 

Japanese classification of gastric carcinoma: 3rd English edition. Gastric Cancer. 2011;14(2):101–12.

Dixon MF, Genta RM, Yardley JH, Correa P. Classification and grading of gastritis: the updated Sydney system. Am J Surg Pathol. 1996;20(10):1161–81.

Article  CAS  PubMed  Google Scholar 

Widya AR, Monno Y, Okutomi M, Suzuki S, Gotoda T, Miki K. Stomach 3D reconstruction using virtual chromoendoscopic images. IEEE J Transl Eng Health Med. 2021;9:1700211.

Article  PubMed  Google Scholar 

He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. 2016 IEEE Conf Comput Vision Pattern Recogn (CVPR). 2016. https://doi.org/10.1109/CVPR.2016.90.

Article  Google Scholar 

Isola P, Zhu JY, Zhou T, Efros AA. Image-to-image translation with conditional adversarial networks. 2017 IEEE Conf Comput Vision Pattern Recogn (CVPR). 2017. https://doi.org/10.1109/CVPR.2017.632.

Article  Google Scholar 

Kojima T, Yao K, Ohtsu K, Kuan C, Tanabe H, Imamura K, et al. A comparative study of demarcation line diagnostic performance between non-magnifying observation with white light and non-magnifying observation with narrow-band light for early gastric cancer. Gastric Cancer. 2022;25(4):761–9.

Article  PubMed  Google Scholar 

Fu H, Xu Y, Wong DWK, Liu J (2016) Retinal vessel segmentation via deep learning network and fully-connected conditional random fields. 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI). IEEE Publications. doi: https://doi.org/10.1109/ISBI.2016.7493362.

Xu Z, Li X, Zhu X, Chen L, He Y, Chen Y. Effective immunohistochemistry pathology microscopy image generation using CycleGAN. Front Mol Biosci. 2020;7: 571180.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Zhou L, Schaefferkoetter JD, Tham IWK, Huang G, Yan J. Supervised learning with cyclegan for low-dose FDG PET image denoising. Med Image Anal. 2020;65: 101770.

Article  PubMed  Google Scholar 

Liu Y, Lei Y, Wang Y, Wang T, Ren L, Lin L, et al. MRI-based treatment planning for proton radiotherapy: dosimetric validation of a deep learning-based liver synthetic CT generation method. Phys Med Biol. 2019;64(14): 145015.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Connell M, Xin Y, Gerard SE, Herrmann J, Shah PK, Martin KT, et al. Unsupervised segmentation and quantification of COVID-19 lesions on computed tomography scans using CycleGAN. Methods. 2022;205:200–9.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Yoo TK, Choi JY, Kim HK. CycleGAN-based deep learning technique for artifact reduction in fundus photography. Graefes Arch Clin Exp Ophthalmol. 2020;258(8):1631–7.

Article  PubMed  Google Scholar 

Fukuda A, Miyamoto T, Kamba S, Sumiyama K. Generating virtual chromoendoscopic images and improving detectability and classification performance of endoscopic lesions. Domain adaptation and representation transfer and medical image learning with less labels and imperfect data. Cham: Springer International Publishing; 2019.

Google Scholar 

Chiba H, Ohata K, Tachikawa J, Yamada K, Kobayashi M, Okada N, et al. The feasibility and safety of endoscopic submucosal dissection of gastric lesions larger than 5 cm. Gastric Cancer. 2022;25(6):1031–8.

Article  PubMed  Google Scholar