{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,9]],"date-time":"2025-11-09T11:16:13Z","timestamp":1762686973706,"version":"build-2065373602"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2025,1,17]],"date-time":"2025-01-17T00:00:00Z","timestamp":1737072000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,17]],"date-time":"2025-01-17T00:00:00Z","timestamp":1737072000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2019YFB1311300"],"award-info":[{"award-number":["2019YFB1311300"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging. Inform. med."],"DOI":"10.1007\/s10278-025-01396-8","type":"journal-article","created":{"date-parts":[[2025,1,17]],"date-time":"2025-01-17T09:00:29Z","timestamp":1737104429000},"page":"2841-2850","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Utilizing a Novel Convolutional Neural Network for Diagnosis and Lesion Delineation in Colorectal Cancer Screening"],"prefix":"10.1007","volume":"38","author":[{"given":"Renbo","family":"Li","sequence":"first","affiliation":[]},{"given":"Ruofan","family":"Cao","sequence":"additional","affiliation":[]},{"given":"Qi","family":"Zhao","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7849-814X","authenticated-orcid":false,"given":"Zijian","family":"Zhao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,1,17]]},"reference":[{"key":"1396_CR1","doi-asserted-by":"publisher","first-page":"1553","DOI":"10.1001\/jamaoncol.2018.2706","volume":"4","author":"Collaboration GBoDC","year":"2018","unstructured":"Collaboration GBoDC: Global, Regional, and National Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life-Years for 29 Cancer Groups, 1990 to 2016: A Systematic Analysis for the Global Burden of Disease Study. JAMA Oncology, 4:1553-1568, 2018","journal-title":"JAMA Oncology"},{"key":"1396_CR2","first-page":"130","volume":"58","author":"B Levin","year":"2008","unstructured":"Levin B, Lieberman DA, McFarland B, Smith RA, Brooks D, Andrews KS, Dash C, Giardiello FM, Glick S, Levin TR, Pickhardt P, Rex DK, Thorson A, Winawer SJ, American Cancer Society Colorectal Cancer Advisory G, Force USM-ST, American College of Radiology Colon Cancer C: Screening and surveillance for the early detection of colorectal cancer and adenomatous polyps, 2008: a joint guideline from the American Cancer Society, the US Multi-Society Task Force on Colorectal Cancer, and the American College of Radiology. CA Cancer J Clin, 58:130-160, 2008","journal-title":"CA Cancer J Clin"},{"key":"1396_CR3","doi-asserted-by":"publisher","first-page":"884","DOI":"10.1007\/s10120-020-01071-7","volume":"23","author":"P An","year":"2020","unstructured":"An P, Yang D, Wang J, Wu L, Zhou J, Zeng Z, Huang X, Xiao Y, Hu S, Chen Y, Yao F, Guo M, Wu Q, Yang Y, Yu H: A deep learning method for delineating early gastric cancer resection margin under chromoendoscopy and white light endoscopy. Gastric Cancer, 23:884-892, 2020","journal-title":"Gastric Cancer"},{"key":"1396_CR4","doi-asserted-by":"publisher","first-page":"368","DOI":"10.1093\/jcde\/qwac138","volume":"10","author":"X Pang","year":"2022","unstructured":"Pang X, Zhao Z, Wu Y, Chen Y, Liu J: Computer-aided diagnosis system based on multi-scale feature fusion for screening large-scale gastrointestinal diseases. Journal of Computational Design and Engineering, 10:368-381, 2022","journal-title":"Journal of Computational Design and Engineering"},{"key":"1396_CR5","doi-asserted-by":"publisher","first-page":"104200","DOI":"10.1016\/j.bspc.2022.104200","volume":"79","author":"L Ma","year":"2023","unstructured":"Ma L, Su X, Ma L, Gao X, Sun M: Deep learning for classification and localization of early gastric cancer in endoscopic images. Biomed Signal Process Control, 79:104200, 2023","journal-title":"Biomed Signal Process Control"},{"key":"1396_CR6","doi-asserted-by":"crossref","unstructured":"Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B: Swin Transformer: Hierarchical vision transformer using shifted windows. Proceedings of the IEEE\/CVF International Conference on Computer Vision:9992\u201310002, 2021","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"1396_CR7","doi-asserted-by":"publisher","first-page":"757","DOI":"10.1055\/a-1706-6174","volume":"54","author":"L Yao","year":"2022","unstructured":"Yao L, Zhang L, Liu J, Zhou W, He C, Zhang J, Wu L, Wang H, Xu Y, Gong D, Xu M, Li X, Bai Y, Gong R, Sharma P, Yu H: Effect of an artificial intelligence-based quality improvement system on efficacy of a computer-aided detection system in colonoscopy: a four-group parallel study. Endoscopy, 54:757-768, 2022","journal-title":"Endoscopy"},{"key":"1396_CR8","doi-asserted-by":"publisher","first-page":"771","DOI":"10.1055\/a-1731-9535","volume":"54","author":"Z Dong","year":"2022","unstructured":"Dong Z, Wu L, Mu G, Zhou W, Li Y, Shi Z, Tian X, Liu S, Zhu Q, Shang RJE: A deep learning-based system for real-time image reporting during esophagogastroduodenoscopy: a multicenter study. Endoscopy, 54:771-777, 2022","journal-title":"Endoscopy"},{"key":"1396_CR9","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1016\/j.gie.2021.06.033","volume":"95","author":"L Wu","year":"2022","unstructured":"Wu L, Wang J, He X, Zhu Y, Jiang X, Chen Y, Wang Y, Huang L, Shang R, Dong Z, Chen B, Tao X, Wu Q, Yu H: Deep learning system compared with expert endoscopists in predicting early gastric cancer and its invasion depth and differentiation status (with videos). Gastrointest Endosc, 95:92-104.e103, 2022","journal-title":"Gastrointest Endosc"},{"key":"1396_CR10","doi-asserted-by":"publisher","first-page":"856","DOI":"10.1016\/j.gie.2020.04.079","volume":"92","author":"Y Horiuchi","year":"2020","unstructured":"Horiuchi Y, Hirasawa T, Ishizuka N, Tokai Y, Namikawa K, Yoshimizu S, Ishiyama A, Yoshio T, Tsuchida T, Fujisaki J, Tada T: Performance of a computer-aided diagnosis system in diagnosing early gastric cancer using magnifying endoscopy videos with narrow-band imaging (with videos). Gastrointest Endosc, 92:856-865.e851, 2020","journal-title":"Gastrointest Endosc"},{"key":"1396_CR11","doi-asserted-by":"publisher","first-page":"522","DOI":"10.1055\/a-0855-3532","volume":"51","author":"L Wu","year":"2019","unstructured":"Wu L, Zhou W, Wan X, Zhang J, Shen L, Hu S, Ding Q, Mu G, Yin A, Huang X, Liu J, Jiang X, Wang Z, Deng Y, Liu M, Lin R, Ling T, Li P, Wu Q, Jin P, Chen J, Yu H: A deep neural network improves endoscopic detection of early gastric cancer without blind spots. Endoscopy, 51:522-531, 2019","journal-title":"Endoscopy"},{"key":"1396_CR12","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1016\/j.gie.2021.09.017","volume":"95","author":"L Wu","year":"2022","unstructured":"Wu L, Xu M, Jiang X, He X, Zhang H, Ai Y, Tong Q, Lv P, Lu B, Guo M, Huang M, Ye L, Shen L, Yu H: Real-time artificial intelligence for detecting focal lesions and diagnosing neoplasms of the stomach by white-light endoscopy (with videos). Gastrointest Endosc, 95:269-280.e266, 2022","journal-title":"Gastrointest Endosc"},{"key":"1396_CR13","doi-asserted-by":"publisher","first-page":"103146","DOI":"10.1016\/j.ebiom.2020.103146","volume":"62","author":"D Tang","year":"2020","unstructured":"Tang D, Wang L, Ling T, Lv Y, Ni M, Zhan Q, Fu Y, Zhuang D, Guo H, Dou X, Zhang W, Xu G, Zou X: Development and validation of a real-time artificial intelligence-assisted system for detecting early gastric cancer: A multicentre retrospective diagnostic study. eBioMedicine, 62:103146, 2020","journal-title":"eBioMedicine"},{"key":"1396_CR14","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1016\/j.gie.2023.02.025","volume":"98","author":"L Zhang","year":"2023","unstructured":"Zhang L, Lu Z, Yao L, Dong Z, Zhou W, He C, Luo R, Zhang M, Wang J, Li Y, Deng Y, Zhang C, Li X, Shang R, Xu M, Wang J, Zhao Y, Wu L, Yu H: Effect of a deep learning\u2013based automatic upper GI endoscopic\u00a0reporting system: a randomized crossover study (with video). Gastrointest Endosc, 98:181-190.e110, 2023","journal-title":"Gastrointest Endosc"},{"key":"1396_CR15","first-page":"81","volume":"12","author":"Y Gong","year":"2024","unstructured":"Gong Y, Zhang H, Xu R, Yu Z, Zhang J, Technology: Innovative Deep Learning Methods for Precancerous Lesion Detection. International Journal of Innovative Research in Computer Science, 12:81-86, 2024","journal-title":"International Journal of Innovative Research in Computer Science"},{"key":"1396_CR16","doi-asserted-by":"crossref","unstructured":"Zhong X, Liang LH, Koh AS, Yong YS: Lightweight Relational Embedding in Task-Interpolated Few-Shot Networks for Enhanced Gastrointestinal Disease Classification. Proceedings of the IEEE Conference on Artificial Intelligence:839\u2013844, 2024","DOI":"10.1109\/CAI59869.2024.00157"},{"key":"1396_CR17","doi-asserted-by":"publisher","first-page":"106933","DOI":"10.1016\/j.bspc.2024.106933","volume":"100","author":"X-L Pan","year":"2025","unstructured":"Pan X-L, Hua B, Tong K, Li X, Luo J-L, Yang H, Ding J-R: EL-CNN: An enhanced lightweight classification method for colorectal cancer histopathological images. Biomed Signal Process Control, 100:106933, 2025","journal-title":"Biomed Signal Process Control"},{"key":"1396_CR18","doi-asserted-by":"publisher","first-page":"2248","DOI":"10.3390\/electronics13122248","volume":"13","author":"\u00c1 Gago-Fabero","year":"2024","unstructured":"Gago-Fabero \u00c1, Mu\u00f1oz-Saavedra L, Civit-Masot J, Luna-Perej\u00f3n F, Rodr\u00edguez Corral JM, Dom\u00ednguez-Morales M: Diagnosis Aid System for Colorectal Cancer Using Low Computational Cost Deep Learning Architectures. Electronics, 13:2248, 2024","journal-title":"Electronics"},{"key":"1396_CR19","doi-asserted-by":"publisher","first-page":"1650","DOI":"10.1016\/j.ccell.2023.08.002","volume":"41","author":"SJ Wagner","year":"2023","unstructured":"Wagner SJ, Reisenb\u00fcchler D, West NP, Niehues JM, Zhu J, Foersch S, Veldhuizen GP, Quirke P, Grabsch HI, van den Brandt PA, Hutchins GGA, Richman SD, Yuan T, Langer R, Jenniskens JCA, Offermans K, Mueller W, Gray R, Gruber SB, Greenson JK, Rennert G, Bonner JD, Schmolze D, Jonnagaddala J, Hawkins NJ, Ward RL, Morton D, Seymour M, Magill L, Nowak M, Hay J, Koelzer VH, Church DN, Church D, Domingo E, Edwards J, Glimelius B, Gogenur I, Harkin A, Hay J, Iveson T, Jaeger E, Kelly C, Kerr R, Maka N, Morgan H, Oien K, Orange C, Palles C, Roxburgh C, Sansom O, Saunders M, Tomlinson I, Matek C, Geppert C, Peng C, Zhi C, Ouyang X, James JA, Loughrey MB, Salto-Tellez M, Brenner H, Hoffmeister M, Truhn D, Schnabel JA, Boxberg M, Peng T, Kather JN: Transformer-based biomarker prediction from colorectal cancer histology: A large-scale multicentric study. Cancer Cell, 41:1650-1661.e1654, 2023","journal-title":"Cancer Cell"},{"key":"1396_CR20","doi-asserted-by":"publisher","first-page":"1565","DOI":"10.1007\/s11517-023-02799-x","volume":"61","author":"L Tan","year":"2023","unstructured":"Tan L, Li H, Yu J, Zhou H, Wang Z, Niu Z, Li J, Li Z: Colorectal cancer lymph node metastasis prediction with weakly supervised transformer-based multi-instance learning. Med Biol Eng Comput, 61:1565-1580, 2023","journal-title":"Med Biol Eng Comput"},{"key":"1396_CR21","doi-asserted-by":"publisher","first-page":"324","DOI":"10.3390\/ai5010016","volume":"5","author":"M Leo","year":"2024","unstructured":"Leo M, Carcagn\u00ec P, Signore LD, Corcione F, Benincasa G, Laukkanen MO, Distante C: Convolutional Neural Networks in the Diagnosis of Colon Adenocarcinoma. AI, 5:324-341, 2024","journal-title":"AI"},{"key":"1396_CR22","doi-asserted-by":"crossref","unstructured":"Subedi A, Regmi S, Regmi N, Bhusal B, Bagci U, Jha D: Classification of Endoscopy and Video Capsule Images Using CNN-Transformer Model. Cancer Prevention, Detection, and Intervention, 15199:26\u201336, 2025","DOI":"10.1007\/978-3-031-73376-5_3"},{"key":"1396_CR23","doi-asserted-by":"publisher","first-page":"102872","DOI":"10.1016\/j.displa.2024.102872","volume":"85","author":"X Li","year":"2024","unstructured":"Li X, Liu Q, Li X, Huang T, Lin M, Han X, Zhang W, Chen K, Lin Y: CIFTC-Net: Cross information fusion network with transformer and CNN for polyp segmentation. Displays, 85:102872, 2024","journal-title":"Displays"},{"key":"1396_CR24","doi-asserted-by":"crossref","unstructured":"Ding X, Zhang X, Han J, Ding G: Scaling Up Your Kernels to 31\u00d731: Revisiting Large Kernel Design in CNNs. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition:11953\u201311965, 2022","DOI":"10.1109\/CVPR52688.2022.01166"},{"key":"1396_CR25","doi-asserted-by":"crossref","unstructured":"Xu Y, Jin T, Xu Y, Shi X, Chen S, Sun W, Xue Y, Wu H: Transformer Image Recognition System Based on Deep Learning. Proceedings of the 6th International Conference on Systems and Informatics:595\u2013599, 2019","DOI":"10.1109\/ICSAI48974.2019.9010489"},{"key":"1396_CR26","unstructured":"Jain RC, Kasturi R, Schunck BG: Machine vision, New York: McGraw-Hill,1995"},{"key":"1396_CR27","doi-asserted-by":"publisher","unstructured":"Hendrycks D, Gimpel K: Gaussian Error Linear Units (GELUs). arXiv: Learning,\u00a0https:\/\/doi.org\/10.48550\/arXiv.1606.08415, 2016","DOI":"10.48550\/arXiv.1606.08415"},{"key":"1396_CR28","unstructured":"Simonyan K, Zisserman A: Very deep convolutional networks for large-scale image recognition. Proceedings of the 3rd International Conference on Learning Representations:1\u201314,2015"},{"key":"1396_CR29","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J: Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition:770\u2013778, 2016","DOI":"10.1109\/CVPR.2016.90"},{"key":"1396_CR30","doi-asserted-by":"crossref","unstructured":"Zhang X, Wei Y, Feng J, Yang Y, Huang T: Adversarial Complementary Learning for Weakly Supervised Object Localization. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition:1325\u20131334, 2018","DOI":"10.1109\/CVPR.2018.00144"},{"key":"1396_CR31","doi-asserted-by":"crossref","unstructured":"Pan X, Ge C, Lu R, Song S, Chen G, Huang Z, Huang G: On the Integration of Self-Attention and Convolution. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition:805\u2013815, 2022","DOI":"10.1109\/CVPR52688.2022.00089"},{"key":"1396_CR32","unstructured":"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. Proceedings of the International Conference on Learning Representations, 2021"},{"key":"1396_CR33","doi-asserted-by":"publisher","first-page":"3229","DOI":"10.1007\/s41870-023-01380-x","volume":"15","author":"SBR Prasad","year":"2023","unstructured":"Prasad SBR, Chandana BS: Mobilenetv3: a deep learning technique for human face expressions identification. International Journal of Information Technology, 15:3229-3243, 2023","journal-title":"International Journal of Information Technology"}],"container-title":["Journal of Imaging Informatics in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-025-01396-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-025-01396-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-025-01396-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T22:48:15Z","timestamp":1761778095000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-025-01396-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,17]]},"references-count":33,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2025,10]]}},"alternative-id":["1396"],"URL":"https:\/\/doi.org\/10.1007\/s10278-025-01396-8","relation":{},"ISSN":["2948-2933"],"issn-type":[{"type":"electronic","value":"2948-2933"}],"subject":[],"published":{"date-parts":[[2025,1,17]]},"assertion":[{"value":"3 October 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 December 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 December 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 January 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The image data collected in this study does not contain any personal information about patients and does not involve ethical issues.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}]}}