{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T20:54:31Z","timestamp":1768856071737,"version":"3.49.0"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032144911","type":"print"},{"value":"9783032144928","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-14492-8_30","type":"book-chapter","created":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T07:07:49Z","timestamp":1768806469000},"page":"385-396","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Features for\u00a0Measuring the\u00a0Progression of\u00a0Gastric Atrophy Focused on\u00a0Gastric Areae Shadow Pattern in\u00a0X-Ray Images of\u00a0Stomach"],"prefix":"10.1007","author":[{"given":"Gaku","family":"Inoue","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Koji","family":"Abe","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Masahide","family":"Minami","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Minoru","family":"Katsuki","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Debabrata","family":"Roy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,20]]},"reference":[{"key":"30_CR1","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1038\/nature21056","volume":"542","author":"A Esteva","year":"2017","unstructured":"Esteva, A., et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115\u2013118 (2017)","journal-title":"Nature"},{"key":"30_CR2","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1038\/s41586-019-1799-6","volume":"577","author":"SM McKinney","year":"2020","unstructured":"McKinney, S.M., et al.: International evaluation of an AI system for breast cancer screening. Nature 577, 89\u201394 (2020)","journal-title":"Nature"},{"issue":"7","key":"30_CR3","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1002\/scj.4690220706","volume":"22","author":"J Hasegawa","year":"1991","unstructured":"Hasegawa, J., Tsutsui, T., Toriwaki, J.: Automated extraction of cancer lesions with convergent fold patterns in double contrast X-ray images of the stomach. Syst. Comput. Jpn. 22(7), 51\u201362 (1991)","journal-title":"Syst. Comput. Jpn."},{"key":"30_CR4","unstructured":"Mekada, Y., Hasegawa, J., Toriwaki, J., Nawano, S., Miyagawa, K.: Automated extraction of cancer lesions from double contrast X-ray images of stomach. In: Proceedings of the 1st International Workshop on Computer Aided Diagnosis, pp. 407\u2013412 (1998)"},{"key":"30_CR5","doi-asserted-by":"crossref","unstructured":"Abe, K., Nobuoka, T., Minami, M.: Computer-aided diagnosis of mass screenings for gastric cancer using double contrast X-ray images. In: Proceedings of the IEEE Pacific Rim Conference on Communications, Computers and Signal Processing, pp. 708\u2013713 (2011)","DOI":"10.1109\/PACRIM.2011.6032980"},{"issue":"6","key":"30_CR6","doi-asserted-by":"publisher","first-page":"22","DOI":"10.14738\/jbemi.16.781","volume":"1","author":"K Abe","year":"2014","unstructured":"Abe, K., Nakagawa, H., Minami, M., Tian, H.: Features for discriminating normal cases in mass screening for gastric cancer with double contrast X-ray images of stomach. J. Biomed. Eng. Med. Imaging 1(6), 22\u201332 (2014)","journal-title":"J. Biomed. Eng. Med. Imaging"},{"key":"30_CR7","doi-asserted-by":"crossref","unstructured":"Ishihara, K., Ogawa, T., Haseyama, M.: Helicobacter pylori infection detection from multiple X-ray images based on combination use of support vector machine and multiple kernel learning. In: Proceedings of ICIP, pp. 4728\u20134732 (2015)","DOI":"10.1109\/ICIP.2015.7351704"},{"issue":"4","key":"30_CR8","first-page":"337","volume":"4","author":"K Ishihara","year":"2016","unstructured":"Ishihara, K., Ogawa, T., Haseyama, M.: Classification of gastric cancer risk from X-ray images based on efficient image features related to serum hp antibody level and serum PG levels. ITE Trans. Media Technol. Appl. 4(4), 337\u2013348 (2016)","journal-title":"ITE Trans. Media Technol. Appl."},{"key":"30_CR9","doi-asserted-by":"crossref","unstructured":"Abe, K., Miura, D., Minami, M.: Features for discriminating helicobacter pylori infection from gastric X-ray images. In: Proceedings of International Conference on Signal Processing Systems, pp. 31\u201335 (2016)","DOI":"10.1145\/3015166.3015190"},{"issue":"1","key":"30_CR10","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/j.compbiomed.2017.03.007","volume":"84","author":"K Ishihara","year":"2017","unstructured":"Ishihara, K., Ogawa, T., Haseyama, M.: Helicobacter pylori infection detection from gastric X-ray images based on feature fusion and decision fusion. Comput. Biol. Med. 84(1), 69\u201378 (2017)","journal-title":"Comput. Biol. Med."},{"key":"30_CR11","doi-asserted-by":"crossref","unstructured":"Ishihara, K., Ogawa, T., Haseyama, M.: Detection of gastric cancer risk from X-ray images via patch-based convolutional neural network. In: Proceedings of ICIP, pp. 2055\u20132059 (2017)","DOI":"10.1109\/ICIP.2017.8296643"},{"issue":"4","key":"30_CR12","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1007\/s00535-018-1514-7","volume":"54","author":"R Togo","year":"2019","unstructured":"Togo, R., et al.: Detection of gastritis by a deep convolutional neural network from double-contrast upper gastrointestinal barium X-ray radiography. J. Gastroenterol. 54(4), 321\u2013329 (2019)","journal-title":"J. Gastroenterol."},{"key":"30_CR13","doi-asserted-by":"crossref","unstructured":"Kanai, M., Togo, R., Ogawa, T., Haseyama, M.: Fine-tuning of pre-trained DCNN for gastritis detection from gastric X-ray images. In: 2019 IEEE 1st Global Conference on Life Sciences and Technologies, pp. 196\u2013197 (2019)","DOI":"10.1109\/LifeTech.2019.8884069"},{"key":"30_CR14","doi-asserted-by":"crossref","unstructured":"Matsumoto, M., Saito, N., Ogawa, T., Haseyama, M.: Chronic gastritis detection from gastric X-ray images via deep autoencoding gaussian mixture models. In: Proceedings of IEEE Global Conference on Life Science and Technology, pp. 231\u2013232 (2019)","DOI":"10.1109\/LifeTech.2019.8884074"},{"key":"30_CR15","doi-asserted-by":"crossref","unstructured":"Kanai, M., Togo, R., Ogawa, T., Haseyama, M.: Gastritis detection from gastric X-ray images via fine-tuning of patch-based deep convolutional neural network. In: Proceedings of ICIP, pp. 1371\u20131375 (2019)","DOI":"10.1109\/ICIP.2019.8803705"},{"key":"30_CR16","doi-asserted-by":"crossref","unstructured":"Okamoto, H., Cap, Q.H., Nomura, T., Iyatomi, H., Hashimoto, J.: Stochastic gastric image augmentation for cancer detection from X-ray Images. In: IEEE International Conference on Big Data, pp. 4858\u20134663 (2019)","DOI":"10.1109\/BigData47090.2019.9006079"},{"issue":"6","key":"30_CR17","doi-asserted-by":"publisher","first-page":"1239","DOI":"10.1007\/s11517-020-02159-z","volume":"58","author":"Z Li","year":"2020","unstructured":"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(6), 1239\u20131250 (2020)","journal-title":"Med. Biol. Eng. Comput."},{"issue":"25","key":"30_CR18","doi-asserted-by":"publisher","first-page":"3650","DOI":"10.3748\/wjg.v26.i25.3650","volume":"26","author":"M Kanai","year":"2020","unstructured":"Kanai, M., Togo, R., Ogawa, T., Haseyama, M.: Ch ronic atrophic gastritis detection with a convolutional neural network considering stomach regions. World J. Gastroenterol. 26(25), 3650\u20133659 (2020)","journal-title":"World J. Gastroenterol."},{"key":"30_CR19","doi-asserted-by":"crossref","unstructured":"Ishii, R., Zhang, X., Homma, N.: An Interpretable DL-based method for diagnosis of H. Pylori infection using gastric X-ray images. In: Proceedings of IEEE 3rd Global Conference on Life Sciences and Technologies, pp. 6\u20137 (2021)","DOI":"10.1109\/LifeTech52111.2021.9391979"},{"key":"30_CR20","doi-asserted-by":"publisher","first-page":"1841","DOI":"10.1007\/s11548-023-02891-5","volume":"18","author":"G Li","year":"2023","unstructured":"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\u20131848 (2023)","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"30_CR21","doi-asserted-by":"crossref","unstructured":"Abe, K., Shirakawa, K., Minami, M., Miura, D.: Features for evaluating gastric atrophy using X-ray images. In: Proceedings of International Conference on Frontiers of Signal Processing, pp. 94\u201399 (2018)","DOI":"10.1109\/ICFSP.2018.8552048"},{"key":"30_CR22","unstructured":"Abe, K., Ito, K., Minami, M.: A feature value for measuring progression of gastric atrophy utilizing the distribution of folds in gastric X-ray images. In: Proceedings of the 7th IIEEJ International Conference on Image Electronics and Visual Computing, vol. A6-6, pp. 1\u20134 (2021)"},{"key":"30_CR23","unstructured":"Japan\u2019s Ministry of Health, Labour and Welfare. Guidelines for promoting cancer prevention and screening. https:\/\/www.mhlw.go.jp\/content\/10900000\/001073510.pdf. Accessed 07 May 2025"},{"key":"30_CR24","doi-asserted-by":"crossref","unstructured":"Abe, K., Shirakawa, K., Minami, M., Yoshikawa, K.: Automated extraction of the essential region in computer-aided diagnosis of helicobacter pylori infection using gastric X-ray images. In: Proceedings of International Congress on Advanced Applied Informatics, pp. 626\u2013629 (2018)","DOI":"10.1109\/IIAI-AAI.2018.00131"},{"issue":"9","key":"30_CR25","doi-asserted-by":"publisher","first-page":"1921","DOI":"10.1109\/TIP.2009.2021548","volume":"18","author":"T Arici","year":"2009","unstructured":"Arici, T., Dikbas, S., Altunbasak, Y.: A histogram modification framework and its application for image contrast enhancement. IEEE Trans. Image Process. 18(9), 1921\u20131935 (2009)","journal-title":"IEEE Trans. Image Process."},{"key":"30_CR26","doi-asserted-by":"crossref","unstructured":"Kobatake, H.: A convergence index filter for vector fields and its application to medical image processing. Electron. Commun. Jpn. (Part III: Fundam. Electron. Sci.) 89(6), 34\u201346 (2006)","DOI":"10.1002\/ecjc.20247"},{"issue":"2","key":"30_CR27","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1111\/j.2517-6161.1974.tb00994.x","volume":"36","author":"M Stone","year":"1974","unstructured":"Stone, M.: Cross-validatory choice and assessment of statistical predictions. J. Roy. Stat. Soc. Ser. B (Methodol.) 36(2), 111\u2013147 (1974)","journal-title":"J. Roy. Stat. Soc. Ser. B (Methodol.)"}],"container-title":["Lecture Notes in Computer Science","Advances in Visual Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-14492-8_30","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T07:07:52Z","timestamp":1768806472000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-14492-8_30"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032144911","9783032144928"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-14492-8_30","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"20 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ISVC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Visual Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Las Vegas, NV","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 November 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 November 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"isvc2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.isvc.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}