Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A: Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 74:229–263, 2024. https://doi.org/10.3322/caac.21834

Article  PubMed  Google Scholar 

Zhao F, Yang D, Lan Y, Li X: Different trends of gastric cancer in China, Japan, Republic of Korea and United States of America. iScience 27:110074, 2024. https://doi.org/10.1016/j.isci.2024.110074

Article  PubMed  PubMed Central  Google Scholar 

Isobe Y, Nashimoto A, Akazawa K, Oda I, Hayashi K, Miyashiro I, Katai H, Tsujitani S, Kodera Y, Seto Y, Kaminishi M: Gastric cancer treatment in Japan: 2008 annual report of the JGCA nationwide registry. Gastric Cancer 14:301–316, 2011. https://doi.org/10.1007/s10120-011-0085-6

Article  PubMed  PubMed Central  Google Scholar 

Tsubono Y, Hisamichi S: Screening for gastric cancer in Japan. Gastric Cancer 3:9–18, 2000. https://doi.org/10.1007/pl00011692

Article  PubMed  Google Scholar 

Hamashima C; Systematic Review Group and Guideline Development Group for Gastric Cancer Screening Guidelines, Guideline Development Group for Gastric Cancer Screening Guidelines: Update version of the Japanese Guidelines for Gastric Cancer Screening. Jpn J Clin Oncol 48:673–683, 2018. https://doi.org/10.1093/jjco/hyy077

Article  PubMed  Google Scholar 

Hibino M, Hamashima C, Iwata M, Terasawa T: Radiographic and endoscopic screening to reduce gastric cancer mortality: A systematic review and meta-analysis. Lancet Reg Health West Pac 35:100741, 2023. https://doi.org/10.1016/j.lanwpc.2023.100741

Article  PubMed  PubMed Central  Google Scholar 

Sugano K: Screening of gastric cancer in Asia. Best Pract Res Clin Gastroenterol 29:895–905, 2015. https://doi.org/10.1016/j.bpg.2015.09.013

Article  PubMed  Google Scholar 

Tashiro A, Sano M, Kinameri K, Fujita K, Takeuchi Y: Comparing mass screening techniques for gastric cancer in Japan. World J Gastroenterol 12:4873–4874, 2006. https://doi.org/10.3748/wjg.v12.i30.4873

Article  PubMed  PubMed Central  Google Scholar 

Hagiwara H, Moki F, Yamashita Y, Saji K, Iesaki K, Suda H: Gastric cancer mortality related to direct radiographic and endoscopic screening: A retrospective study. World J Gastroenterol 27:5595–5609, 2021. https://doi.org/10.3748/wjg.v27.i33.5595

Article  PubMed  PubMed Central  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 34:412–419, 2022. https://doi.org/10.1111/den.14063

Article  PubMed  Google Scholar 

Hamashima C, Goto R: Potential capacity of endoscopic screening for gastric cancer in Japan. Cancer Sci 108:101–107, 2017. https://doi.org/10.1111/cas.13100

Article  CAS  PubMed  Google Scholar 

Kato M, Asaka M: Recent development of gastric cancer prevention. Jpn J Clin Oncol 42:987–994, 2012. https://doi.org/10.1093/jjco/hys151

Article  PubMed  Google Scholar 

Kanai M, Togo R, Ogawa T, Haseyama M: Chronic atrophic gastritis detection with a convolutional neural network considering stomach regions. World J Gastroenterol 26:3650–3659, 2020. https://doi.org/10.3748/wjg.v26.i25.3650

Article  PubMed  PubMed Central  Google Scholar 

Yamamichi N, Hirano C, Ichinose M, Takahashi Y, Minatsuki C, Matsuda R, Nakayama C, Shimamoto T, Kodashima S, Ono S, Tsuji Y, Niimi K, Sakaguchi Y, Kataoka Y, Saito I, Asada-Hirayama I, Takeuchi C, Yakabi S, Kaikimoto H, Matsumoto Y, Yamaguchi D, Kageyama-Yahara N, Fujishiro M, Wada R, Mitsushima T, Koike K: Atrophic gastritis and enlarged gastric folds diagnosed by double-contrast upper gastrointestinal barium x-ray radiography are useful to predict future gastric cancer development based on the 3-year prospective observation. Gastric Cancer 19:1016–1022, 2016. https://doi.org/10.1007/s10120-015-0558-0

Article  PubMed  Google Scholar 

Gonçalves WGE, Dos Santos MHP, Lobato FMF, Ribeiro-Dos-Santos Â, de Araújo GS: Deep learning in gastric tissue diseases: A systematic review. BMJ Open Gastroenterol 7:e000371, 2020. https://doi.org/10.1136/bmjgast-2019-000371

Article  PubMed  PubMed Central  Google Scholar 

Togo R, Ishihara K, Mabe K, Oizumi H, Ogawa T, Kato M, Sakamoto N, Nakajima S, Asaka M, Haseyama M: Preliminary study of automatic gastric cancer risk classification from photofluorography. World J Gastrointest Oncol 10:62–70, 2018. https://doi.org/10.4251/wjgo.v10.i2.62

Article  PubMed  PubMed Central  Google Scholar 

Li G, Togo R, Ogawa T, Haseyama M: Self-supervised learning for gastritis detection with gastric X-ray images. Int J Comput Assist Radiol Surg 18:1841–1848, 2023. https://doi.org/10.1007/s11548-023-02891-5

Article  CAS  PubMed  Google Scholar 

Li Z, Togo R, Ogawa T, Haseyama M: Chronic gastritis classification using gastric X-ray images with a semi-supervised learning method based on tri-training. Med Biol Eng Comput 58:1239–1250, 2020. https://doi.org/10.1007/s11517-020-02159-z

Article  PubMed  Google Scholar 

Togo R, Yamamichi N, Mabe K, Takahashi Y, Takeuchi C, Kato M, Sakamoto N, Ishihara K, Ogawa T, Haseyama M: Detection of gastritis by a deep convolutional neural network from double-contrast upper gastrointestinal barium X-ray radiography. J Gastroenterol 54:321–329, 2019. https://doi.org/10.1007/s00535-018-1514-7

Article  CAS  PubMed  Google Scholar 

Togo R, Ishihara K, Ogawa T, Haseyama M: Estimation of salient regions related to chronic gastritis using gastric X-ray images. Comput Biol Med 77:9–15, 2016. https://doi.org/10.1016/j.compbiomed.2016.07.014

Article  PubMed  Google Scholar 

Togo R, Watanabe H, Ogawa T, Haseyama M: Deep convolutional neural network-based anomaly detection for organ classification in gastric X-ray examination. Comput Biol Med 123:103903, 2020. https://doi.org/10.1016/j.compbiomed.2020.103903

Article  PubMed  Google Scholar 

Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, Uszkoreit J, Houlsby N: An image is worth 16x16 words: Transformers for image recognition at scale. 2020. https://doi.org/10.48550/arXiv.2010.11929, arXiv:2010.11929

Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, van der Laak JAWM, van Ginneken B, Sánchez CI: A survey on deep learning in medical image analysis. Med Image Anal 42:60–88, 2017. https://doi.org/10.1016/j.media.2017.07.005

Article  PubMed  Google Scholar 

GitHub – huggingface/pytorch-image-models. https://github.com/huggingface/pytorch-image-models. Accessed 1 Sep 2024

timm/vit_small_patch8_224.dino· Hugging Face. https://huggingface.co/timm/vit_small_patch8_224.dino. Accessed 1 Sep 2024

timm/tf_efficientnetv2_m, p in21k_ft_in1k· Hugging Face. https://huggingface.co/timm/tf_efficientnetv2_m.in21k_ft_in1k. Accessed 1 Sep 2024

Tan M, Le Q: EfficientNetV2: Smaller models and faster training. 2021. https://doi.org/10.48550/arXiv.2104.00298, arXiv:2104.00298

Akiba T, Sano S, Yanase T, Ohta T, Koyama M: Optuna: A next-generation hyperparameter optimization framework. 2019. https://doi.org/10.1145/3292500.3330701, arXiv:1907.10902