{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T16:34:17Z","timestamp":1758904457894,"version":"3.37.3"},"reference-count":26,"publisher":"Georg Thieme Verlag KG","issue":"12","funder":[{"name":"Construction Project of Improving the Ability of Diagnosis and Treatment of Difficult Diseases in Zhongnan Hospital of Wuhan University","award":["ZLYNXM202017"],"award-info":[{"award-number":["ZLYNXM202017"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["41671382,62072345"],"award-info":[{"award-number":["41671382,62072345"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Z Gastroenterol"],"published-print":{"date-parts":[[2022,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>\n          Background and study aim Chronic atrophic gastritis plays an important role in the process of gastric cancer. Deep learning is gradually introduced in the medical field, and how to better apply a convolutional neural network (CNN) to the diagnosis of chronic atrophic gastritis remains a research hotspot. This study was designed to improve the performance of CNN on diagnosing chronic atrophic gastritis by constructing and evaluating a network structure based on the characteristics of gastroscopic images.<\/jats:p><jats:p>\n          Methods Three endoscopists reviewed the endoscopic images of the gastric antrum from the Gastroscopy Image Database of Zhongnan Hospital and labelled available images according to pathological results. Two novel modules proposed recently were introduced to construct the Multi-scale with Attention net (MWA-net) considering the characters of similar medical images. After training the network using images of training sets, the diagnostic ability of the MWA-net was evaluated by comparing it with those of other deep learning models and endoscopists with varying degrees of expertise.<\/jats:p><jats:p>\n          Results As a result, 5,159 images of the gastric antrum from 2,240 patients were used to train and test the MWA-net. Compared with the direct application of famous networks, the MWA-net achieved the best performance (accuracy, 92.13%) with an increase of 1.80% compared to that of ResNet. The suspicious lesions indicated by the network are consistent with the conclusion of experts. The sensitivity and specificity of the convolutional network for gastric atrophy diagnosis are 90.19% and 94.51%, respectively, which are higher than those of experts.<\/jats:p><jats:p>\n          Conclusions Highly similar images of chronic atrophic gastritis can be identified by the proposed MWA-net, which has a better performance than other well-known networks. This work can further reduce the workload of gastroscopists, simplify the diagnostic process and provide medical assistance to more residents.<\/jats:p>","DOI":"10.1055\/a-1828-1441","type":"journal-article","created":{"date-parts":[[2022,6,13]],"date-time":"2022-06-13T23:10:28Z","timestamp":1655161828000},"page":"1770-1778","source":"Crossref","is-referenced-by-count":5,"title":["A deep learning network based on multi-scale and attention for the diagnosis of chronic atrophic gastritis"],"prefix":"10.1055","volume":"60","author":[{"given":"Yanwen","family":"Chong","sequence":"additional","affiliation":[{"name":"Wuhan University, Wuhan City, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ningdi","family":"Xie","sequence":"additional","affiliation":[{"name":"Wuhan University, Wuhan City, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Liu","sequence":"additional","affiliation":[{"name":"Wuhan University, Wuhan City, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Wuhan University Zhongnan Hospital, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fengxing","family":"Huang","sequence":"additional","affiliation":[{"name":"Wuhan University Zhongnan Hospital, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Fang","sequence":"additional","affiliation":[{"name":"Wuhan University Zhongnan Hospital, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fan","family":"Wang","sequence":"additional","affiliation":[{"name":"Wuhan University Zhongnan Hospital, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shaoming","family":"Pan","sequence":"additional","affiliation":[{"name":"Wuhan University, Wuhan City, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haihang","family":"Nie","sequence":"additional","affiliation":[{"name":"Wuhan University Zhongnan Hospital, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3230-6077","authenticated-orcid":false,"given":"Qiu","family":"Zhao","sequence":"additional","affiliation":[{"name":"Wuhan University, Wuhan City, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"194","published-online":{"date-parts":[[2022,6,13]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"783","DOI":"10.1097\/CM9.0000000000001474","article-title":"Changing profiles of cancer burden worldwide and in China: a secondary analysis of the global cancer statistics 2020","volume":"134","author":"W Cao","year":"2021","journal-title":"Chin Med J (Engl)"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"394","DOI":"10.3322\/caac.21492","article-title":"Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"68","author":"F Bray","year":"2018","journal-title":"CA Cancer J Clin"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"1517","DOI":"10.3760\/cma.j.issn.0254-6450.2019.12.003","article-title":"Epidemiological characteristics of gastric cancer in China, 2015","volume":"40","author":"SM Wang","year":"2019","journal-title":"Zhonghua Liu Xing Bing Xue Za Zhi"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"784","DOI":"10.1056\/NEJMoa001999","article-title":"Helicobacter pylori infection and the development of gastric cancer","volume":"345","author":"N Uemura","year":"2001","journal-title":"N Engl J Med"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"380","DOI":"10.1111\/apt.14424","article-title":"Reversibility of atrophic gastritis and intestinal metaplasia after Helicobacter pylori eradication \u2013 a prospective study for up to 10 years","volume":"47","author":"YJ Hwang","year":"2018","journal-title":"Aliment Pharmacol Ther"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1097\/MCG.0000000000000164","article-title":"The accuracy of confocal laser endomicroscopy, narrow band imaging, and chromoendoscopy for the detection of atrophic gastritis","volume":"49","author":"T Liu","year":"2015","journal-title":"J Clin Gastroenterol"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1097\/MCG.0000000000000309","article-title":"Confocal laser endomicroscopy for the detection of atrophic gastritis: a new application for confocal endomicroscopy?","volume":"49","author":"A Imaeda","year":"2015","journal-title":"J Clin Gastroenterol"},{"key":"ref8","doi-asserted-by":"publisher","first-page":"E46","DOI":"10.1055\/s-0034-1365524","article-title":"How commonly is upper gastrointestinal cancer missed at endoscopy? A meta-analysis","volume":"2","author":"S Menon","year":"2014","journal-title":"Endosc Int Open"},{"key":"ref9","doi-asserted-by":"publisher","first-page":"505","DOI":"10.1148\/rg.2017160130","article-title":"Machine Learning for Medical Imaging","volume":"37","author":"BJ Erickson","year":"2017","journal-title":"Radiographics"},{"key":"ref10","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"39","author":"S Ren","year":"2017","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"ref11","doi-asserted-by":"publisher","first-page":"833","DOI":"10.1007\/978-3-030-01234-2_49","article-title":"Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation","volume":"11211","author":"LCE Chen","year":"2018","journal-title":"Computer Vision \u2013 Eccv"},{"issue":"01","key":"ref12","doi-asserted-by":"publisher","first-page":"e000371","DOI":"10.1136\/bmjgast-2019-000371","article-title":"Deep learning in gastric tissue diseases: a systematic review","volume":"7","author":"WGE Goncalves","year":"2020","journal-title":"BMJ Open Gastroenterol"},{"key":"ref13","doi-asserted-by":"publisher","first-page":"936","DOI":"10.1109\/Cvpr.2017.106","article-title":"Feature Pyramid Networks for Object Detection","author":"TY Lin","year":"2017","journal-title":"30th Ieee Conference on Computer Vision and Pattern Recognition (Cvpr 2017)"},{"key":"ref14","doi-asserted-by":"publisher","first-page":"652","DOI":"10.1109\/Tpami.2019.2938758","article-title":"Res2Net: A New Multi-Scale Backbone Architecture","volume":"43","author":"SH Gao","year":"2021","journal-title":"Ieee Transactions on Pattern Analysis and Machine Intelligence"},{"key":"ref15","doi-asserted-by":"publisher","first-page":"5395","DOI":"10.1109\/Tim.2019.2958580","article-title":"Detecting Trees in Street Images via Deep Learning With Attention Module","volume":"69","author":"Q Xie","year":"2020","journal-title":"Ieee Transactions on Instrumentation and Measurement"},{"key":"ref16","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-01234-2_1","article-title":"CBAM: Convolutional Block Attention Module","volume":"11211","author":"SH Woo","year":"2018","journal-title":"Computer Vision \u2013 Eccv"},{"key":"ref17","first-page":"32","article-title":"PyTorch: An Imperative Style, High-Performance Deep Learning Library","author":"A Paszke","year":"2019","journal-title":"Advances in Neural Information Processing Systems 32 (Nips 2019)"},{"key":"ref18","doi-asserted-by":"publisher","first-page":"1717","DOI":"10.1109\/Cvpr.2014.222","article-title":"Learning and Transferring Mid-Level Image Representations using Convolutional Neural Networks","author":"M Oquab","year":"2014","journal-title":"Ieee Conference on Computer Vision and Pattern Recognition (Cvpr)"},{"key":"ref19","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"F Pedregosa","year":"2011","journal-title":"Journal of Machine Learning Research"},{"key":"ref20","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1038\/s41586-020-2649-2","article-title":"Array programming with NumPy","volume":"585","author":"CR Harris","year":"2020","journal-title":"Nature"},{"key":"ref22","doi-asserted-by":"publisher","first-page":"770","DOI":"10.1109\/Cvpr.2016.90","article-title":"Deep Residual Learning for Image Recognition","author":"KM He","year":"2016","journal-title":"2016 Ieee Conference on Computer Vision and Pattern Recognition (Cvpr)"},{"key":"ref23","doi-asserted-by":"publisher","first-page":"2261","DOI":"10.1109\/Cvpr.2017.243","article-title":"Densely Connected Convolutional Networks","author":"G Huang","year":"2017","journal-title":"30th Ieee Conference on Computer Vision and Pattern Recognition (Cvpr 2017)"},{"key":"ref24","doi-asserted-by":"publisher","first-page":"2818","DOI":"10.1109\/Cvpr.2016.308","article-title":"Rethinking the Inception Architecture for Computer Vision","author":"C Szegedy","year":"2016","journal-title":"2016 Ieee Conference on Computer Vision and Pattern Recognition (Cvpr)"},{"issue":"12","key":"ref25","doi-asserted-by":"publisher","first-page":"1645","DOI":"10.1016\/S1470-2045(19)30637-0","article-title":"Real-time artificial intelligence for detection of upper gastrointestinal cancer by endoscopy: a multicentre, case-control, diagnostic study","volume":"20","author":"H Luo","year":"2019","journal-title":"Lancet Oncol"},{"issue":"05","key":"ref26","doi-asserted-by":"publisher","first-page":"566","DOI":"10.1016\/j.dld.2019.12.146","article-title":"Diagnosing chronic atrophic gastritis by gastroscopy using artificial intelligence","volume":"52","author":"Y Zhang","year":"2020","journal-title":"Dig Liver Dis"},{"key":"ref27","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1016\/j.ebiom.2017.10.014","article-title":"Application of Convolutional Neural Networks in the Diagnosis of Helicobacter pylori Infection Based on Endoscopic Images","volume":"25","author":"S Shichijo","year":"2017","journal-title":"EBioMedicine"}],"container-title":["Zeitschrift f\u00fcr Gastroenterologie"],"original-title":[],"language":"de","link":[{"URL":"http:\/\/www.thieme-connect.de\/products\/ejournals\/pdf\/10.1055\/a-1828-1441.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,10]],"date-time":"2023-02-10T12:35:29Z","timestamp":1676032529000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.thieme-connect.de\/DOI\/DOI?10.1055\/a-1828-1441"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,13]]},"references-count":26,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2022,12,8]]},"published-print":{"date-parts":[[2022,12]]}},"URL":"https:\/\/doi.org\/10.1055\/a-1828-1441","archive":["Portico","CLOCKSS"],"relation":{},"ISSN":["0044-2771","1439-7803"],"issn-type":[{"type":"print","value":"0044-2771"},{"type":"electronic","value":"1439-7803"}],"subject":[],"published":{"date-parts":[[2022,6,13]]}}}