{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T17:25:20Z","timestamp":1791393920883,"version":"4.3.3"},"reference-count":111,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T00:00:00Z","timestamp":1727654400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T00:00:00Z","timestamp":1727654400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62233016"],"award-info":[{"award-number":["62233016"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017599","name":"Science and Technology Program of Zhejiang Province","doi-asserted-by":"publisher","award":["2024C04023"],"award-info":[{"award-number":["2024C04023"]}],"id":[{"id":"10.13039\/501100017599","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004731","name":"Natural Science Foundation of Zhejiang Province","doi-asserted-by":"publisher","award":["LQ23F010024"],"award-info":[{"award-number":["LQ23F010024"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2025,2]]},"DOI":"10.1007\/s11517-024-03203-y","type":"journal-article","created":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T03:17:08Z","timestamp":1727666228000},"page":"293-320","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["A review of deep learning methods for gastrointestinal diseases classification applied in computer-aided diagnosis system"],"prefix":"10.1007","volume":"63","author":[{"given":"Qianru","family":"Jiang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yulin","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yipei","family":"Ren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sheng","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiongxiong","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,9,30]]},"reference":[{"key":"3203_CR1","doi-asserted-by":"publisher","unstructured":"Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F (2021) Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: Cancer J Clin 71(3):209\u2013249. https:\/\/doi.org\/10.3322\/caac.21660","DOI":"10.3322\/caac.21660"},{"issue":"1","key":"3203_CR2","first-page":"11","volume":"26","author":"KS Yao","year":"2013","unstructured":"Yao KS (2013) The endoscopic diagnosis of early gastric cancer. Annals of gastroenterology: quarterly publication of the hellenic soc gastroenterol 26(1):11\u201322","journal-title":"Annals of gastroenterology: quarterly publication of the hellenic soc gastroenterol"},{"issue":"1","key":"3203_CR3","doi-asserted-by":"publisher","first-page":"7497","DOI":"10.1038\/s41598-018-25842-6","volume":"8","author":"H Takiyama","year":"2018","unstructured":"Takiyama H, Ozawa T, Ishihara S, Fujishiro M, Shichijo S, Nomura S, Miura M, Tada T (2018) Automatic anatomical classification of esophagogastroduodenoscopy images using deep convolutional neural networks. Sci Rep 8(1):7497. https:\/\/doi.org\/10.1038\/s41598-018-25842-6","journal-title":"Sci Rep"},{"key":"3203_CR4","doi-asserted-by":"publisher","unstructured":"Zou Y, Li L, Wang Y, Yu J, Li Y, Deng W (2015) Classifying digestive organs in wireless capsule endoscopy images based on deep convolutional neural network. In: 2015 IEEE international conference on Digital Signal Processing (DSP). IEEE, Singapore, pp 1274\u20131278. https:\/\/doi.org\/10.1109\/ICDSP.2015.7252086","DOI":"10.1109\/ICDSP.2015.7252086"},{"key":"3203_CR5","doi-asserted-by":"publisher","unstructured":"Yu J, Chen J, Xiang Z, Zou Y (2015) A hybrid convolutional neural networks with extreme learning machine for WCE image classification. In: 2015 IEEE international conference on Robotics and Biomimetics (ROBIO). IEEE, Zhuhai, pp 1822\u20131827. https:\/\/doi.org\/10.1109\/ROBIO.2015.7419037","DOI":"10.1109\/ROBIO.2015.7419037"},{"key":"3203_CR6","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1016\/j.neucom.2016.06.077","volume":"229","author":"H Chen","year":"2017","unstructured":"Chen H, Wu X, Tao G, Peng Q (2017) Automatic content understanding with cascaded spatial-temporal deep framework for capsule endoscopy videos. Neurocomputing 229:77\u201387. https:\/\/doi.org\/10.1016\/j.neucom.2016.06.077","journal-title":"Neurocomputing"},{"key":"3203_CR7","doi-asserted-by":"publisher","unstructured":"Saito H, Tanimoto T, Ozawa T, Ishihara S, Fujishiro M, Shichijo S, Hirasawa D, Matsuda T, Endo Y, Tada T (2021) Automatic anatomical classification of colonoscopic images using deep convolutional neural networks. Gastroenterology report 9(3):226\u2013233. https:\/\/doi.org\/10.1093\/gastro\/goaa078","DOI":"10.1093\/gastro\/goaa078"},{"key":"3203_CR8","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1007\/s00464-020-08236-6","volume":"36","author":"SJ Choi","year":"2022","unstructured":"Choi SJ, Khan MA, Choi HS, Choo J, Lee JM, Kwon S, Keum B, Chun HJ (2022) Development of artificial intelligence system for quality control of photo documentation in esophagogastroduodenoscopy. Surg Endosc 36:57\u201365. https:\/\/doi.org\/10.1007\/s00464-020-08236-6","journal-title":"Surg Endosc"},{"key":"3203_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.105911","volume":"90","author":"K Zhang","year":"2024","unstructured":"Zhang K, Zhang Y, Ding Y, Wang M, Bai P, Wang X, Qian Z, Zhang X, Ma T, Hu F, Feng L, Wei W, Li P (2024) Anatomical sites identification in both ordinary and capsule gastroduodenoscopy via deep learning. Biomed Signal Process Control 90:105911. https:\/\/doi.org\/10.1016\/j.bspc.2023.105911","journal-title":"Biomed Signal Process Control"},{"key":"3203_CR10","doi-asserted-by":"publisher","unstructured":"Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C, Berg AC(2016) SSD: single shot multibox detector. In: Computer vision\u2013ECCV 2016: 14th European conference. Springer, Amsterdam, pp 21\u201337. https:\/\/doi.org\/10.1007\/978-3-319-46448-0_2","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"3203_CR11","doi-asserted-by":"publisher","unstructured":"Girshick R (2015) Fast R-CNN. In: Presented at the proceedings of the IEEE international conference on computer vision. IEEE, Columbus, pp 1440\u20131448. https:\/\/doi.org\/10.1109\/ICCV.2015.169","DOI":"10.1109\/ICCV.2015.169"},{"key":"3203_CR12","doi-asserted-by":"publisher","unstructured":"Ren S, He K, Girshick R, Sun J (2016) Faster R-CNN: towards real-time object detection with region proposal networks. Adv Neural Inf Process Syst 39(6):1137\u20131149. https:\/\/doi.org\/10.1109\/TPAMI.2016.2577031","DOI":"10.1109\/TPAMI.2016.2577031"},{"key":"3203_CR13","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1007\/s11263-013-0620-5","volume":"104","author":"JR Uijlings","year":"2013","unstructured":"Uijlings JR, Sande KE, Gevers T, Smeulders AW (2013) Selective search for object recognition. International Journal of Computer Vision. Int J Comput Vis 104:154\u2013171. https:\/\/doi.org\/10.1007\/s11263-013-0620-5","journal-title":"Int J Comput Vis"},{"issue":"1","key":"3203_CR14","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.gie.2018.07.037","volume":"89","author":"Y Horie","year":"2019","unstructured":"Horie Y, Yoshio T, Aoyama K, Yoshimizu S, Horiuchi Y, Ishiyama A, Hirasawa T, Tomohiro T, Ozawa T, Ishihara S, Kumagai Y, Fujishiro M, Maetani I, Fujisaki J, Tada T (2019) Diagnostic outcomes of esophageal cancer by artificial intelligence using convolutional neural networks. Gastrointest Endosc 89(1):25\u201332. https:\/\/doi.org\/10.1016\/j.gie.2018.07.037","journal-title":"Gastrointest Endosc"},{"key":"3203_CR15","doi-asserted-by":"publisher","unstructured":"Qadir HA, Balasingham I, Solhusvik J, Bergsland J, Aabakken L, Shin Y (2019) Improving automatic polyp detection using CNN by exploiting temporal dependency in colonoscopy video. IEEE J Biomed Health Inf 24(1):180\u2013193. https:\/\/doi.org\/10.1109\/JBHI.2019.2907434","DOI":"10.1109\/JBHI.2019.2907434"},{"key":"3203_CR16","doi-asserted-by":"publisher","first-page":"106531","DOI":"10.1016\/j.compeleceng.2019.106531","volume":"81","author":"ES Nadimi","year":"2020","unstructured":"Nadimi ES, Buijs MM, Herp J, Kroijer R, Kobaek-Larsen M, Nielsen E, Pedersen CD, Blanes-Vidal V, Baatrup G (2020) Application of deep learning for autonomous detection and localization of colorectal polyps in wireless colon capsule endoscopy. Comput & Elect Eng 81:106531. https:\/\/doi.org\/10.1016\/j.compeleceng.2019.106531","journal-title":"Comput & Elect Eng"},{"issue":"11","key":"3203_CR17","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proceed of the IEEE 86(11):2278\u20132324. https:\/\/doi.org\/10.1109\/5.726791","journal-title":"Proceed of the IEEE"},{"key":"3203_CR18","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: Proceedings of the 26th conference on Neural Information Processing Systems (NIPS), pp 1106\u20131114"},{"key":"3203_CR19","doi-asserted-by":"publisher","unstructured":"Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: Proceedings of the 3rd international conference on learning representations. https:\/\/doi.org\/10.48550\/arXiv.1409.1556","DOI":"10.48550\/arXiv.1409.1556"},{"key":"3203_CR20","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. Advances in neural information processing systems, pp 2672\u20132680"},{"key":"3203_CR21","doi-asserted-by":"publisher","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Presented at the proceedings of the IEEE conference on computer vision and pattern recognition. IEEE. Boston, pp 1\u20139. https:\/\/doi.org\/10.1109\/CVPR.2015.7298594","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"3203_CR22","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Presented at the proceedings of the IEEE conference on computer vision and pattern recognition. IEEE, Las Vegas, pp 770\u2013778. https:\/\/doi.org\/10.1109\/CVPR2016.90","DOI":"10.1109\/CVPR2016.90"},{"key":"3203_CR23","doi-asserted-by":"publisher","unstructured":"Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Presented at the proceedings of the IEEE conference on computer vision and pattern recognition. IEEE, Honolulu, pp 4700\u20134708. https:\/\/doi.org\/10.1109\/CVPR.2017.243","DOI":"10.1109\/CVPR.2017.243"},{"issue":"16","key":"3203_CR24","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/aad51c","volume":"63","author":"S Fan","year":"2018","unstructured":"Fan S, Xu L, Fan Y, Wei K, Li L (2018) Computer-aided detection of small intestinal ulcer and erosion in wireless capsule endoscopy images. Phys Med Biol 63(16):165001. https:\/\/doi.org\/10.1088\/1361-6560\/aad51c","journal-title":"Phys Med Biol"},{"issue":"4","key":"3203_CR25","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1016\/j.irbm.2021.10.003","volume":"43","author":"DMN Mubarak","year":"2022","unstructured":"Mubarak DMN (2022) Classification of early stages of esophageal cancer using transfer learning. Irbm 43(4):251\u2013258. https:\/\/doi.org\/10.1016\/j.irbm.2021.10.003","journal-title":"Irbm"},{"key":"3203_CR26","doi-asserted-by":"publisher","unstructured":"Xing X, Yuan Y, Jia X, Meng MQH (2019) A saliency-aware hybrid dense network for bleeding detection in wireless capsule endoscopy images. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). IEEE, Venice, pp 104\u2013107. https:\/\/doi.org\/10.1109\/ISBI.2019.8759401","DOI":"10.1109\/ISBI.2019.8759401"},{"issue":"3","key":"3203_CR27","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0214133","volume":"14","author":"X Zhang","year":"2019","unstructured":"Zhang X, Chen F, Yu T, An J, Huang ZX, Liu JQ, Hu WL, Wang LJ, Duan HL, Si JM (2019) Real-time gastric polyp detection using convolutional neural networks. PloS one 14(3):e0214133. https:\/\/doi.org\/10.1371\/journal.pone.0214133","journal-title":"PloS one"},{"issue":"3","key":"3203_CR28","doi-asserted-by":"publisher","first-page":"765","DOI":"10.3390\/cancers15030765","volume":"15","author":"M Sheikh","year":"2023","unstructured":"Sheikh M, Roshandel G, McCormack V, Malekzadeh R (2023) Current status and future prospects for esophageal cancer. Cancers 15(3):765. https:\/\/doi.org\/10.3390\/cancers15030765","journal-title":"Cancers"},{"key":"3203_CR29","doi-asserted-by":"publisher","unstructured":"Riel VS, Van Der Sommen F, Zinger S, Schoon EJ, de With PH (2018) Automatic detection of early esophageal cancer with CNNS using transfer learning. In: 2018 25th IEEE international conference on image processing (ICIP). IEEE, Athens, pp 1383\u20131387. https:\/\/doi.org\/10.1109\/ICIP.2018.8451771","DOI":"10.1109\/ICIP.2018.8451771"},{"key":"3203_CR30","doi-asserted-by":"publisher","first-page":"745","DOI":"10.1016\/j.gie.2019.06.044","volume":"90","author":"S Cai","year":"2019","unstructured":"Cai S, Li B, Tan W, Niu X, Yu H, Yao L, Zhou P, Yan B, Zhong Y (2019) Using a deep learning system in endoscopy for screening of early esophageal squamous cell carcinoma (with video). Gastrointest Endosc 90:745-753.e2. https:\/\/doi.org\/10.1016\/j.gie.2019.06.044","journal-title":"Gastrointest Endosc"},{"key":"3203_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.104029","volume":"126","author":"LA de Souza","year":"2020","unstructured":"de Souza LA, Passos LA, Mendel R, Ebigbo A, Probst A, Messmann H, Palm C, Papa JP (2020) Assisting Barrett\u2019s esophagus identification using endoscopic data augmentation based on generative adversarial networks. Comput Biol Med 126:104029. https:\/\/doi.org\/10.1016\/j.compbiomed.2020.104029","journal-title":"Comput Biol Med"},{"issue":"3","key":"3203_CR32","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1007\/s10388-023-00993-2","volume":"20","author":"Y Kitagawa","year":"2023","unstructured":"Kitagawa Y, Ishihara R, Ishikawa H et al (2023) Esophageal cancer practice guidelines 2022 edited by the Japan esophageal society: part 1. Esophagus 20(3):343\u2013372. https:\/\/doi.org\/10.1007\/s10388-023-00993-2","journal-title":"Esophagus"},{"key":"3203_CR33","doi-asserted-by":"publisher","first-page":"1037","DOI":"10.1007\/s00535-020-01716-5","volume":"55","author":"Y Shimamoto","year":"2020","unstructured":"Shimamoto Y, Ishihara R, Kato Y, Shoji A, Inoue T, Matsueda K, Miyake M, Waki K, Kono M, Fukuda H, Matsuura N, Nagaike K, Aoi K, Yamamoto K, Inoue T, Nakahara M, Nishihara A, Tada T (2020) Real-time assessment of video images for esophageal squamous cell carcinoma invasion depth using artificial intelligence. J Gastroenterol 55:1037\u20131045. https:\/\/doi.org\/10.1007\/s00535-020-01716-5","journal-title":"J Gastroenterol"},{"issue":"11","key":"3203_CR34","doi-asserted-by":"publisher","first-page":"8651","DOI":"10.1007\/s00464-022-09353-0","volume":"36","author":"X Yuan","year":"2022","unstructured":"Yuan X, Liu W, Liu Y, Zeng X, Mou Y, Wu C, Ye L, Zhang Y, He L, Feng J, Zhang W, Wang J, Chen X, Hu Y, Zhang K, Hu B (2022) Artificial intelligence for diagnosing microvessels of precancerous lesions and superficial esophageal squamous cell carcinomas: a multicenter study. Surg Endosc 36(11):8651\u20138662. https:\/\/doi.org\/10.1007\/s00464-022-09353-0","journal-title":"Surg Endosc"},{"key":"3203_CR35","doi-asserted-by":"publisher","unstructured":"Liu G, Hua J, Wu Z, Meng T, Sun M, Huang P, He X, Sun W, Li X, Chen Y (2020) Automatic classification of esophageal lesions in endoscopic images using a convolutional neural network. Annals Trans Med 8(7):486. https:\/\/doi.org\/10.21037\/atm.2020.03.24","DOI":"10.21037\/atm.2020.03.24"},{"key":"3203_CR36","doi-asserted-by":"publisher","unstructured":"Gao H, Zhu M, Cao X, Li C, Liu Q, Xu P (2023) A micro- hyperspectral image classification method of gallbladder cancer based on multi-scale fusion attention mechanism. J Image Graph 28(4):1173\u20131185. https:\/\/doi.org\/10.11834\/jig.211201","DOI":"10.11834\/jig.211201"},{"key":"3203_CR37","doi-asserted-by":"publisher","first-page":"101838","DOI":"10.1016\/j.medi.2020.101838","volume":"67","author":"Z Wu","year":"2021","unstructured":"Wu Z, Ge R, Wen M, Liu G, Chen Y, Zhang PZ, He X, Hua J, Luo L, Li S (2021) ELNet: automatic classification and segmentationfor esophageal lesions using convolutional neural network. Med Image Anal 67:101838. https:\/\/doi.org\/10.1016\/j.medi.2020.101838","journal-title":"Med Image Anal"},{"key":"3203_CR38","doi-asserted-by":"publisher","unstructured":"Tang S, Yu X, Cheang C, Ji X, Yu H, Choi IC (2023) CLELNet: a continual learning network for esophageal lesion analysis on endoscopic images. Comput Methods Prog Biomed 231:107399. https:\/\/doi.org\/10.1016\/j.cmpb.2023.107399","DOI":"10.1016\/j.cmpb.2023.107399"},{"key":"3203_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2020.101767","volume":"85","author":"T Rahim","year":"2020","unstructured":"Rahim T, Usman MA, Shin SY (2020) A survey on contemporary computer-aided tumor, polyp, and ulcer detection methods in wireless capsule endoscopy imaging. Comput Med Imaging Graph 85:101767. https:\/\/doi.org\/10.1016\/j.compmedimag.2020.101767","journal-title":"Comput Med Imaging Graph"},{"issue":"2","key":"3203_CR40","doi-asserted-by":"publisher","first-page":"322","DOI":"10.1053\/j.gastro.2019.06.048","volume":"158","author":"M Song","year":"2020","unstructured":"Song M, Chan A, Sun J (2020) Influence of the gut microbiome, diet, and environment on risk of colorectal cancer. Gastroenterology 158(2):322\u2013340. https:\/\/doi.org\/10.1053\/j.gastro.2019.06.048","journal-title":"Gastroenterology"},{"key":"3203_CR41","doi-asserted-by":"publisher","unstructured":"Ugai T, Haruki K, Harrison TA et al (2022) Molecular characteristics of early-onset colorectal cancer according to detailed anatomical locations: comparison with later-onset cases. Official J American College Gastroenterol ACG 118(4):712\u2013726. https:\/\/doi.org\/10.14309\/ajg.0000000000002171","DOI":"10.14309\/ajg.0000000000002171"},{"key":"3203_CR42","doi-asserted-by":"publisher","DOI":"10.1016\/j.canlet.2023.216238","volume":"565","author":"A Sharma","year":"2023","unstructured":"Sharma A, Kumar R, Yadav G, Garg P (2023) Artificial intelligence in intestinal polyp and colorectal cancer prediction. Cancer Lett 565:216238. https:\/\/doi.org\/10.1016\/j.canlet.2023.216238","journal-title":"Cancer Lett"},{"key":"3203_CR43","doi-asserted-by":"publisher","first-page":"721","DOI":"10.1016\/j.neucom.2020.02.123","volume":"423","author":"A Nogueira-Rodr\u00edguez","year":"2021","unstructured":"Nogueira-Rodr\u00edguez A, Dom\u00ednguez-Carbajales R, L\u00f3pez-Fern\u00e1ndez H, Iglesias \u00c1, Cubiella J, Fdez-Riverola F, Riboiro-Jato M, Glez-Pena D (2021) Deep neural networks approaches for detecting and classifying colorectal polyps. Neurocomputing 423:721\u2013734. https:\/\/doi.org\/10.1016\/j.neucom.2020.02.123","journal-title":"Neurocomputing"},{"issue":"1","key":"3203_CR44","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1109\/JPROC.2019.2950506","volume":"108","author":"X Jia","year":"2020","unstructured":"Jia X, Xing X, Yuan Y, Meng MQH (2020) Wireless capsule endoscopy: a new tool for cancer screening in the colon with deep-learning-based polyp recognition. Proc IEEE 108(1):178\u2013197. https:\/\/doi.org\/10.1109\/JPROC.2019.2950506","journal-title":"Proc IEEE"},{"issue":"12","key":"3203_CR45","doi-asserted-by":"publisher","first-page":"2404","DOI":"10.3390\/app9122404","volume":"9","author":"S Sornapudi","year":"2019","unstructured":"Sornapudi S, Meng F, Yi S (2019) Region-based automated localization of colonoscopy and wireless capsule endoscopy polyps. Appl Sci 9(12):2404. https:\/\/doi.org\/10.3390\/app9122404","journal-title":"Appl Sci"},{"key":"3203_CR46","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.103465","volume":"73","author":"JS Nisha","year":"2022","unstructured":"Nisha JS, Gopi VP, Palanisamy P (2022) Automated colorectal polyp detection based on image enhancement and dual-path CNN architecture. Biomed Signal Process Control 73:103465. https:\/\/doi.org\/10.1016\/j.bspc.2021.103465","journal-title":"Biomed Signal Process Control"},{"key":"3203_CR47","doi-asserted-by":"publisher","first-page":"106787","DOI":"10.1016\/j.compbiomed.2023.106787","volume":"158","author":"R Gong","year":"2023","unstructured":"Gong R, He S, Tian T, Chen J, Hao Y, Qiao C (2023) FRCNN-AA-CIF: an automatic detection model of colon polyps based on attention awareness and context information fusion. Comp Biol Mea 158:106787. https:\/\/doi.org\/10.1016\/j.compbiomed.2023.106787","journal-title":"Comp Biol Mea"},{"issue":"2","key":"3203_CR48","doi-asserted-by":"publisher","first-page":"574","DOI":"10.1109\/TASE.2019.2936645","volume":"17","author":"Y Yuan","year":"2019","unstructured":"Yuan Y, Qin W, Ibragimov B, Zhang G, Han B, Meng MQH, Xing L (2019) Densely connected neural network with unbalanced discriminant and category sensitive constraints for polyp recognition. IEEE Trans Autom Sci Eng 17(2):574\u2013583. https:\/\/doi.org\/10.1109\/TASE.2019.2936645","journal-title":"IEEE Trans Autom Sci Eng"},{"key":"3203_CR49","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2020.101794","volume":"86","author":"P Laiz","year":"2020","unstructured":"Laiz P, Vitri\u00e1 J, Wenzek H, Malagelada C, Azpiroz F, Segu\u00ed S (2020) WCE polyp detection with triplet based embeddings. Comput Med Imaging Graph 86:101794. https:\/\/doi.org\/10.1016\/j.compmedimag.2020.101794","journal-title":"Comput Med Imaging Graph"},{"key":"3203_CR50","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2023.3264047","volume":"72","author":"G Yue","year":"2023","unstructured":"Yue G, Wei P, Liu Y, Luo Y, Du J, Wang T (2023) Automated endoscopic image classification via deep neural network with class imbalance loss. IEEE Trans Instrum Meas 72:1\u201311. https:\/\/doi.org\/10.1109\/TIM.2023.3264047","journal-title":"IEEE Trans Instrum Meas"},{"issue":"2","key":"3203_CR51","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1007\/s00779-021-01660-y","volume":"27","author":"A Ellahyani","year":"2023","unstructured":"Ellahyani A, Jaafari IE, Charfi S, Ansari ME (2023) Fine-tuned deep neural networks for polyp detection in colonoscopy images. Pers Ubiquit Comput 27(2):235\u2013247. https:\/\/doi.org\/10.1007\/s00779-021-01660-y","journal-title":"Pers Ubiquit Comput"},{"issue":"1","key":"3203_CR52","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1186\/s12938-023-01130-x","volume":"22","author":"Y Xie","year":"2023","unstructured":"Xie Y, Yu Y, Liao M, Sun C (2023) Gastric polyp detection module based on improved attentional feature fusion. BioMed Eng OnLine 22(1):72. https:\/\/doi.org\/10.1186\/s12938-023-01130-x","journal-title":"BioMed Eng OnLine"},{"issue":"1","key":"3203_CR53","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1109\/JBHI.2016.2637004","volume":"21","author":"L Yu","year":"2016","unstructured":"Yu L, Chen H, Dou Q, Qin J, Heng P (2016) Integrating online and offline three-dimensional deep learning for automated polyp detection in colonoscopy videos. IEEE J Biomed Health Inf 21(1):65\u201375. https:\/\/doi.org\/10.1109\/JBHI.2016.2637004","journal-title":"IEEE J Biomed Health Inf"},{"issue":"4","key":"3203_CR54","doi-asserted-by":"publisher","first-page":"1379","DOI":"10.1002\/mp.12147","volume":"44","author":"Y Yuan","year":"2017","unstructured":"Yuan Y, Meng MQH (2017) Deep learning for polyp recognition in wireless capsule endoscopy images. Med Phys 44(4):1379\u20131389. https:\/\/doi.org\/10.1002\/mp.12147","journal-title":"Med Phys"},{"key":"3203_CR55","doi-asserted-by":"publisher","unstructured":"Yuan Y, Qin W, Ibragimov B, Han B, Xing L (2018) RIIS-DenseNet: rotation-invariant and image similarity constrained densely connected convolutional network for polyp detection. Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018. Springer, Granda, pp 620\u2013628. https:\/\/doi.org\/10.1007\/978-3-030-00934-2_69","DOI":"10.1007\/978-3-030-00934-2_69"},{"key":"3203_CR56","doi-asserted-by":"publisher","unstructured":"Zhang X, Hu W, Chen F, Liu J, Yang Y, Wang L, Duan H, Si J (2017) Gastric precancerous diseases classification using CNN with a concise model. Plos One 12(9):e0185508. https:\/\/doi.org\/10.1371\/journal.pone.0185508","DOI":"10.1371\/journal.pone.0185508"},{"issue":"2","key":"3203_CR57","doi-asserted-by":"publisher","first-page":"2410","DOI":"10.1007\/s10489-022-03689-9","volume":"53","author":"F Younas","year":"2023","unstructured":"Younas F, Usman M, Yan W (2023) A deep ensemble learning method for colorectal polyp classification with optimized network parameters. Appl Intell 53(2):2410\u20132433. https:\/\/doi.org\/10.1007\/s10489-022-03689-9","journal-title":"Appl Intell"},{"issue":"1","key":"3203_CR58","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1136\/gutjnl-2017-314547","volume":"68","author":"MF Byrne","year":"2019","unstructured":"Byrne MF, Chapados N, Soudan F, Oertel C, P\u00e9rez ML, Kelly R, Iqbal N, Chandelier F, Rex DK (2019) Real-time differentiation of adenomatous and hyperplastic diminutive colorectal polyps during analysis of unaltered videos of standard colonoscopy using a deep learning model. Gut 68(1):94\u2013100. https:\/\/doi.org\/10.1136\/gutjnl-2017-314547","journal-title":"Gut"},{"key":"3203_CR59","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107568","volume":"234","author":"S Wang","year":"2021","unstructured":"Wang S, Yin Y, Wang D, Lv Z, Wang Y, Jin Y (2021) An interpretable deep neural network for colorectal polyp diagnosis under colonoscopy. Knowl-Based Syst 234:107568. https:\/\/doi.org\/10.1016\/j.knosys.2021.107568","journal-title":"Knowl-Based Syst"},{"issue":"4","key":"3203_CR60","doi-asserted-by":"publisher","first-page":"431","DOI":"10.3969\/j.issn.0258-8021","volume":"41","author":"S Li","year":"2022","unstructured":"Li S, Cao J, Ye S, Dai F, He X (2022) Image classification network of multiple types of intestinal polyps based on marginal prior information. Chin J Biomed Eng 41(4):431\u2013442. https:\/\/doi.org\/10.3969\/j.issn.0258-8021","journal-title":"Chin J Biomed Eng"},{"issue":"9","key":"3203_CR61","doi-asserted-by":"publisher","first-page":"2051","DOI":"10.1109\/TMI.2016.2547947","volume":"35","author":"P Mesejo","year":"2016","unstructured":"Mesejo P, Pizarro D, Abergel A, Rouquette O, Beorchia S, Poincloux L, Bartoli A (2016) Computer-aided classification of gastrointestinal lesions in regular colonoscopy. IEEE Trans Med Imaging 35(9):2051\u20132063. https:\/\/doi.org\/10.1109\/TMI.2016.2547947","journal-title":"IEEE Trans Med Imaging"},{"key":"3203_CR62","doi-asserted-by":"publisher","unstructured":"Jia X, Meng MQH (2017) Gastrointestinal bleeding detection in wireless capsule endoscopy images using handcrafted and CNN features. In: 2017 39th Annual international conference of the ieee engineering in medicine and biology society (EMBC). IEEE, Jeju, pp 3154\u20133157. https:\/\/doi.org\/10.1109\/EMBC.2017.8037526","DOI":"10.1109\/EMBC.2017.8037526"},{"key":"3203_CR63","doi-asserted-by":"publisher","first-page":"404","DOI":"10.1007\/s10278-021-00428-3","volume":"34","author":"T Ghosh","year":"2021","unstructured":"Ghosh T, Chakareski J (2021) Deep transfer learning for automated intestinal bleeding detection in capsule endoscopy imaging. J Digital Imaging 34:404\u2013417. https:\/\/doi.org\/10.1007\/s10278-021-00428-3","journal-title":"J Digital Imaging"},{"key":"3203_CR64","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2020.101852","volume":"88","author":"A Caroppo","year":"2021","unstructured":"Caroppo A, Leone A, Siciliano P (2021) Deep transfer learning approaches for bleeding detection in endoscopy images. Comput Med Imaging Graph 88:101852. https:\/\/doi.org\/10.1016\/j.compmedimag.2020.101852","journal-title":"Comput Med Imaging Graph"},{"key":"3203_CR65","doi-asserted-by":"publisher","unstructured":"Sunitha S, Sujatha SS (2021) An improved bleeding detection method for Wireless Capsule Endoscopy (WCE) images based on AlexNet. In: 2021 3rd International Conference on Signal Processing and Communication (ICPSC). IEEE, Coimbatore, pp 11\u201315. https:\/\/doi.org\/10.1109\/ICSPC51351.2021.9451699","DOI":"10.1109\/ICSPC51351.2021.9451699"},{"key":"3203_CR66","doi-asserted-by":"publisher","first-page":"21081","DOI":"10.1007\/s11042-023-16305-w","volume":"83","author":"S Lafraxo","year":"2023","unstructured":"Lafraxo S, El Ansari M, Koutti L (2023) Computer-aided system for bleeding detection in WCE images based on CNN-GRU network. Multimed Tools Appl 83:21081\u201321106. https:\/\/doi.org\/10.1007\/s11042-023-16305-w","journal-title":"Multimed Tools Appl"},{"issue":"2","key":"3203_CR67","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1016\/j.gie.2018.10.027","volume":"89","author":"T Aoki","year":"2019","unstructured":"Aoki T, Yamada A, Aoyama K, Saito H, Tsuboi A, Nakada A, Niikura R, Fujishiro M, Oka S, Ishihara S, Matsuda T, Tanaka S, Koike K, Tada T (2019) Automatic detection of erosions and ulcerations in wireless capsule endoscopy images based on a deep convolutional neural network. Gastrointest Endosc 89(2):357-363.e2. https:\/\/doi.org\/10.1016\/j.gie.2018.10.027","journal-title":"Gastrointest Endosc"},{"key":"3203_CR68","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ab5086","volume":"64","author":"S Wang","year":"2019","unstructured":"Wang S, Xing Y, Zhang L, Zhang H (2019) A systematic evaluation and optimization of automatic detection of ulcers in wireless capsule endoscopy on a large dataset using deep convolutional neural networks. Phys Med Biol 64:235014. https:\/\/doi.org\/10.1088\/1361-6560\/ab5086","journal-title":"Phys Med Biol"},{"issue":"6","key":"3203_CR69","doi-asserted-by":"publisher","first-page":"1265","DOI":"10.3390\/s19061265","volume":"19","author":"H Alaskar","year":"2019","unstructured":"Alaskar H, Hussain A, Al-Aseem N, Liatsis P, Al-Jumeily D (2019) Application of convolutional neural networks for automated ulcer detection in wireless capsule endoscopy images. Sensors 19(6):1265. https:\/\/doi.org\/10.3390\/s19061265","journal-title":"Sensors"},{"key":"3203_CR70","doi-asserted-by":"publisher","first-page":"186274","DOI":"10.1109\/ACCESS.2020.3029259","volume":"8","author":"C Lee","year":"2020","unstructured":"Lee C, Shin D, Min J, Cha J, Lee S (2020) Decision boundary re-sampling in imbalanced learning for ulcer detection. IEEE Access 8:186274\u2013186278. https:\/\/doi.org\/10.1109\/ACCESS.2020.3029259","journal-title":"IEEE Access"},{"issue":"6","key":"3203_CR71","doi-asserted-by":"publisher","first-page":"3319","DOI":"10.1016\/j.jksuci.2020.09.008","volume":"34","author":"V Vani","year":"2022","unstructured":"Vani V, Prashanth KVM (2022) Ulcer detection in wireless capsule endoscopy images using deep CNN. J King Saud Univ-Comput Inf Sci 34(6):3319\u20133331. https:\/\/doi.org\/10.1016\/j.jksuci.2020.09.008","journal-title":"J King Saud Univ-Comput Inf Sci"},{"issue":"12","key":"3203_CR72","doi-asserted-by":"publisher","first-page":"2898","DOI":"10.3390\/diagnostics12122898","volume":"12","author":"M Alhajlah","year":"2022","unstructured":"Alhajlah M (2022) Robust ulcer classification: contrast and illumination invariant approach. Diagnostics 12(12):2898. https:\/\/doi.org\/10.3390\/diagnostics12122898","journal-title":"Diagnostics"},{"issue":"3","key":"3203_CR73","doi-asserted-by":"publisher","first-page":"719","DOI":"10.1007\/s11517-021-02486-9","volume":"60","author":"J Afonso","year":"2022","unstructured":"Afonso J, Saraiva MM, Ferreira JPS, Cardoso H, Ribeiro T, Andrade P, Parente M, Jorge RN, Macedo G (2022) Automated detection of ulcers and erosions in capsule endoscopy images using a convolutional neural network. Med Biol Eng Comput 60(3):719\u2013725. https:\/\/doi.org\/10.1007\/s11517-021-02486-9","journal-title":"Med Biol Eng Comput"},{"issue":"1","key":"3203_CR74","doi-asserted-by":"publisher","first-page":"15365","DOI":"10.1038\/s41598-022-19639-x","volume":"12","author":"Y Liu","year":"2022","unstructured":"Liu Y, Zhang L, Hao Z, Yang Z, Wang S, Zhou X, Chang Q (2022) An xception model based on residual attention mechanism for the classification of benign and malignant gastric ulcers. Sci Rep 12(1):15365. https:\/\/doi.org\/10.1038\/s41598-022-19639-x","journal-title":"Sci Rep"},{"issue":"2","key":"3203_CR75","doi-asserted-by":"publisher","first-page":"945","DOI":"10.3390\/ijms24020945","volume":"24","author":"MV Machado","year":"2023","unstructured":"Machado MV (2023) New developments in celiac disease treatment. Int J Mol Sci 24(2):945. https:\/\/doi.org\/10.3390\/ijms24020945","journal-title":"Int J Mol Sci"},{"key":"3203_CR76","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.compbiomed.2017.03.031","volume":"85","author":"T Zhou","year":"2017","unstructured":"Zhou T, Han G, Li BN, Lin Z, Ciaccio EJ, Green PH, Qin J (2017) Quantitative analysis of patients with celiac disease by video capsule endoscopy: a deep learning method. Comput Biol Med 85:1\u20136. https:\/\/doi.org\/10.1016\/j.compbiomed.2017.03.031","journal-title":"Comput Biol Med"},{"key":"3203_CR77","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.bbe.2022.12.002","volume":"43","author":"V Jahmunah","year":"2023","unstructured":"Jahmunah V, Koh JEW, Sudarshan VK, Raghavendra U, Gudigar A, Oh SL, Loh HW, Faust O, Ciaccio EJ, Acharya UR (2023) Endoscopy, video capsule endoscopy, and biopsy for automated celiac disease detection: a review. Biocybernetics and Biomedical Engineering 43:82\u2013108. https:\/\/doi.org\/10.1016\/j.bbe.2022.12.002","journal-title":"Biocybernetics and Biomedical Engineering"},{"issue":"17","key":"3203_CR78","doi-asserted-by":"publisher","first-page":"2780","DOI":"10.3390\/diagnostics13172780","volume":"13","author":"A Molder","year":"2023","unstructured":"Molder A, Balaban DV, Molder CC, Jinga M, Robin A (2023) Computer-based diagnosis of celiac disease by quantitative processing of duodenal endoscopy images. Diagnostics 13(17):2780. https:\/\/doi.org\/10.3390\/diagnostics13172780","journal-title":"Diagnostics"},{"issue":"5","key":"3203_CR79","doi-asserted-by":"publisher","first-page":"2379","DOI":"10.1109\/TIP.2018.2801119","volume":"27","author":"J He","year":"2018","unstructured":"He J, Wu X, Jiang Y, Peng Q, Jain R (2018) Hookworm detection in wireless capsule endoscopy images with deep learning. IEEE Trans Image Process 27(5):2379\u20132392. https:\/\/doi.org\/10.1109\/TIP.2018.2801119","journal-title":"IEEE Trans Image Process"},{"key":"3203_CR80","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1146\/annurev-med-042220-020814","volume":"73","author":"YC Lee","year":"2022","unstructured":"Lee YC, Dore MP, Graham DY (2022) Diagnosis and treatment of Helicobacter pylori infection. Annual Rev Med 73:183\u2013195. https:\/\/doi.org\/10.1146\/annurev-med-042220-020814","journal-title":"Annual Rev Med"},{"key":"3203_CR81","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1016\/j.ebiom.2017.10.014","volume":"25","author":"S Shichijo","year":"2017","unstructured":"Shichijo S, Nomura S, Aoyama K (2017) Application of convolutional neural networks in the diagnosis of Helicobacter pylori infection based on endoscopic images. EBioMedicine 25:106\u2013111. https:\/\/doi.org\/10.1016\/j.ebiom.2017.10.014","journal-title":"EBioMedicine"},{"key":"3203_CR82","doi-asserted-by":"publisher","unstructured":"Zheng W, Zhang X, Kim J, Zhu X, Ye G, Ye B, Wang J, Luo S, Li J, Yu T, Liu J, Hu W, Si J (2019) High accuracy of convolutional neural network for evaluation of helicobacter pylori infection based on endoscopic images: preliminary experience. Clin Transl Gastroenterol 10(12):e00109. https:\/\/doi.org\/10.14309\/ctg.0000000000000109","DOI":"10.14309\/ctg.0000000000000109"},{"key":"3203_CR83","doi-asserted-by":"publisher","unstructured":"Seo JY, Hong H, Ryu WS, Kim D, Chun J, Kwak MS (2023) Development and validation of a convolutional neural network model for diagnosing Helicobacter pylori infections with endoscopic images: a multicenter study. Gastrointest Endosc 97(5):880\u2013888. e2. https:\/\/doi.org\/10.1016\/j.gie.2023.01.007","DOI":"10.1016\/j.gie.2023.01.007"},{"issue":"9","key":"3203_CR84","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0185508","volume":"12","author":"X Zhang","year":"2017","unstructured":"Zhang X, Hu W, Chen F, Liu J, Yang Y, Wang L, Duan H, Si J (2017) Gastric precancerous diseases classification using CNN with a concise model. PLOS ONE 12(9):e0185508. https:\/\/doi.org\/10.1371\/journal.pone.0185508","journal-title":"PLOS ONE"},{"key":"3203_CR85","doi-asserted-by":"publisher","first-page":"51847","DOI":"10.1109\/ACCESS.2021.3069747","volume":"9","author":"SA Lee","year":"2021","unstructured":"Lee SA, Cho HC, Cho HC (2021) A novel approach for increased convolutional neural network performance in gastric-cancer classification using endoscopic images. IEEE Access 9:51847\u201351854. https:\/\/doi.org\/10.1109\/ACCESS.2021.3069747","journal-title":"IEEE Access"},{"key":"3203_CR86","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2022.107301","volume":"229","author":"H Zhou","year":"2023","unstructured":"Zhou H, Liu Z, Li T, Chen Y, Huang W, Zhang Z (2023) Classification of precancerous lesions based on fusion of multiple hierarchical features. Comput Methods Progr Biomed 229:107301. https:\/\/doi.org\/10.1016\/j.cmpb.2022.107301","journal-title":"Comput Methods Progr Biomed"},{"key":"3203_CR87","doi-asserted-by":"publisher","unstructured":"Shah SC, Piazuelo MB, Kuipers EJ, Li D (2021) AGA clinical practice update on the diagnosis and management of atrophic gastritis: expert review. Gastroenterology 161(4):1325\u20131332. e7. https:\/\/doi.org\/10.1053\/j.gastro.2021.06.078","DOI":"10.1053\/j.gastro.2021.06.078"},{"issue":"1","key":"3203_CR88","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1136\/gutjnl-2019-319347","volume":"69","author":"P Guimar\u00e3es","year":"2020","unstructured":"Guimar\u00e3es P, Keller A, Fehlmann T, Lammert F, Casper M (2020) Deep-learning based detection of gastric precancerous conditions. Gut 69(1):4\u20136. https:\/\/doi.org\/10.1136\/gutjnl-2019-319347","journal-title":"Gut"},{"key":"3203_CR89","doi-asserted-by":"publisher","unstructured":"Xu M, Zhou W, Wu L, Zhang J, Wang J, Mu G, Huang X, Li Y, Yuan J, Zeng Z, Wang Y, Huang L, Liu J, Yu H (2021) Artificial intelligence in the diagnosis of gastric precancerous conditions by image-enhanced endoscopy: a multicenter, diagnostic study (with video). Gastrointest Endosc 94(3):540\u2013548. e4. https:\/\/doi.org\/10.1016\/j.gie.2021.03.013","DOI":"10.1016\/j.gie.2021.03.013"},{"issue":"4","key":"3203_CR90","doi-asserted-by":"publisher","first-page":"650","DOI":"10.1016\/j.gie.2021.11.050","volume":"95","author":"M Yoshida","year":"2022","unstructured":"Yoshida M, Takizawa K, Hasuike N, Ono H, Boku N, Kadota T, Mizusawa J, Oda I, Yoshida N, Horiuchi Y, Hirasawa K, Morita Y, Yamamoto Y, Muto M (2022) Second gastric cancer after curative endoscopic resection of differentiated-type early gastric cancer: post-hoc analysis of a single-arm confirmatory trial. Gastrointest Endosc 95(4):650\u2013659. https:\/\/doi.org\/10.1016\/j.gie.2021.11.050","journal-title":"Gastrointest Endosc"},{"key":"3203_CR91","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1007\/s10120-019-00992-2","volume":"23","author":"L Li","year":"2020","unstructured":"Li L, Chen Y, Shen Z, Zhang X, Song J, Ding Y, Yang X, Chen M, Chen C, Yu C (2020) Convolutional neural network for the diagnosis of early gastric cancer based on magnifying narrow band imaging. Gastric Cancer 23:126\u2013132. https:\/\/doi.org\/10.1007\/s10120-019-00992-2","journal-title":"Gastric Cancer"},{"issue":"1","key":"3203_CR92","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1111\/den.13688","volume":"33","author":"Y Ikenoyama","year":"2021","unstructured":"Ikenoyama Y, Hirasawa T, Ishioka M, Namikawa K, Yoshimizu S, Horiuchi Y, Ishiyama A, Yoshio T, Tsuchida T, Takeuchi Y, Shichijo S, Katayama N, Fujisaki J, Tada T (2021) Detecting early gastric cancer: comparison between the diagnostic ability of convolutional neural networks and endoscopists. Dig Endosc 33(1):141\u2013150. https:\/\/doi.org\/10.1111\/den.13688","journal-title":"Dig Endosc"},{"key":"3203_CR93","doi-asserted-by":"publisher","unstructured":"He X, Wu L, Dong Z, Gong D, Jiang X, Zhang H, Ai Y, Tong Q, Lv P, Lu B, Wu Q, Yuan J, Xu M, Yu H (2022) Real-time use of artificial intelligence for diagnosing early gastric cancer by magnifying image-enhanced endoscopy: a multicenter diagnostic study (with videos). Gastrointest Endosc 95(4):671\u2013678. e4. https:\/\/doi.org\/10.1016\/j.gie.2021.11.040","DOI":"10.1016\/j.gie.2021.11.040"},{"key":"3203_CR94","doi-asserted-by":"publisher","first-page":"653","DOI":"10.1007\/s10120-018-0793-2","volume":"21","author":"T Hirasawa","year":"2018","unstructured":"Hirasawa T, Aoyama K, Tanimoto T, Ishihara S, Shichijo S, Ozawa T, Ohnishi T, Fujishiro M, Matsuo K, Fujisaki J, Tada T (2018) Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images. Gastric Cancer 21:653\u2013660. https:\/\/doi.org\/10.1007\/s10120-018-0793-2","journal-title":"Gastric Cancer"},{"key":"3203_CR95","doi-asserted-by":"publisher","first-page":"29391","DOI":"10.1109\/ACCESS.2023.3260983","volume":"11","author":"JW Chae","year":"2023","unstructured":"Chae JW, Cho HC (2023) Enhanced classification of gastric lesions and early gastric cancer diagnosis in gastroscopy using multi-filter autoaugment. IEEE Access 11:29391\u201329399. https:\/\/doi.org\/10.1109\/ACCESS.2023.3260983","journal-title":"IEEE Access"},{"key":"3203_CR96","doi-asserted-by":"publisher","first-page":"538","DOI":"10.1016\/j.gassur.2023.12.029","volume":"28","author":"R Wu","year":"2024","unstructured":"Wu R, Qin K, Fang Y, Xu Y, Zhang H, Li W, Luo X, Han Z, Liu S, Li Q (2024) Application of the convolution neural network in determining the depth of invasion of gastrointestinal cancer: a systematic review and meta-analysis. Journal of gastrointestinal surgery 28:538\u2013547. https:\/\/doi.org\/10.1016\/j.gassur.2023.12.029","journal-title":"Journal of gastrointestinal surgery"},{"issue":"4","key":"3203_CR97","doi-asserted-by":"publisher","first-page":"806","DOI":"10.1016\/j.gie.2018.11.011","volume":"89","author":"Y Zhu","year":"2019","unstructured":"Zhu Y, Wang Q, Xu M, Zhang Z, Cheng J, Zhong Y, Zhang Y, Chen W, Yao L, Zhou P, Li Q (2019) Application of convolutional neural network in the diagnosis of the invasion depth of gastric cancer based on conventional endoscopy. Gastrointest Endosc 89(4):806-815.e1. https:\/\/doi.org\/10.1016\/j.gie.2018.11.011","journal-title":"Gastrointest Endosc"},{"issue":"6","key":"3203_CR98","doi-asserted-by":"publisher","first-page":"1858","DOI":"10.3390\/jcm9061858","volume":"9","author":"BJ Cho","year":"2020","unstructured":"Cho BJ, Bang CS, Lee JJ, Seo CW, Kim JH (2020) Prediction of submucosal invasion for gastric neoplasms in endoscopic images using deep-learning. J Clin Med 9(6):1858. https:\/\/doi.org\/10.3390\/jcm9061858","journal-title":"J Clin Med"},{"key":"3203_CR99","doi-asserted-by":"publisher","unstructured":"Xie F, Zhang K, Li F, Ma G, Ni Y, Zhang W, Wang J, Li Y (2022) Diagnostic accuracy of convolutional neural network\u2013based endoscopic image analysis in diagnosing gastric cancer and predicting its invasion depth: a systematic review and meta-analysis. Gastrointest Endosc 95(4):599\u2013609. e7. https:\/\/doi.org\/10.1016\/j.gie.2021.12.021","DOI":"10.1016\/j.gie.2021.12.021"},{"key":"3203_CR100","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101733","volume":"64","author":"X Guo","year":"2020","unstructured":"Guo X, Yuan Y (2020) Semi-supervised WCE image classification with adaptive aggregated attention. Med Image Anal 64:101733. https:\/\/doi.org\/10.1016\/j.media.2020.101733","journal-title":"Med Image Anal"},{"key":"3203_CR101","doi-asserted-by":"publisher","first-page":"4047","DOI":"10.1109\/TMI.2020.3010102","volume":"39","author":"X Xing","year":"2020","unstructured":"Xing X, Yuan Y, Meng MQH (2020) Zoom in lesions for better diagnosis: attention guided deformation network for WCE image classification. IEEE Trans Med Imaging 39:4047\u20134059. https:\/\/doi.org\/10.1109\/TMI.2020.3010102","journal-title":"IEEE Trans Med Imaging"},{"key":"3203_CR102","doi-asserted-by":"publisher","unstructured":"Li S, Cao J, Yao J, Zhu J, He X (2022a) Adaptive aggregation with self-attention network for gastrointestinal image classification. IET Image Process 16(9):2384\u20132397. https:\/\/doi.org\/10.1049\/ipr2.12495","DOI":"10.1049\/ipr2.12495"},{"key":"3203_CR103","doi-asserted-by":"publisher","unstructured":"Li S, Yao J, Cao J, Kong X, Zhu J (2022b) Effective high-to-low-level feature aggregation network for endoscopic image classification. International J Comput Assist Radiol Surg 17(7):1225\u20131233. https:\/\/doi.org\/10.1007\/s11548-022-02591-6","DOI":"10.1007\/s11548-022-02591-6"},{"key":"3203_CR104","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11760-023-02507-0","volume":"17","author":"S Li","year":"2023","unstructured":"Li S, Zhu B, Guo X, Ye J, Zhuang Y, He X (2023) Multi-scale high and low feature fusion attention network for intestinal image classification. Signal, image and video processing 17:1\u201310. https:\/\/doi.org\/10.1007\/s11760-023-02507-0","journal-title":"Signal, image and video processing"},{"key":"3203_CR105","doi-asserted-by":"publisher","unstructured":"Guo X, Yuan Y (2019) Triple ANet: adaptive abnormal-aware attention network for WCE image classification. In: Medical image computing and computer assisted intervention \u2013 miccai 2019. springer, shenzhen, pp 293\u2013301. https:\/\/doi.org\/10.1007\/978-3-030-32239-7_33","DOI":"10.1007\/978-3-030-32239-7_33"},{"key":"3203_CR106","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104789","volume":"137","author":"S Jain","year":"2021","unstructured":"Jain S, Seal A, Ojha A, Yazidi A, Bures J, Tacheci I, Krejcar O (2021) A deep CNN model for anomaly detection and localization in wireless capsule endoscopy images. Comput Biol Med 137:104789. https:\/\/doi.org\/10.1016\/j.compbiomed.2021.104789","journal-title":"Comput Biol Med"},{"key":"3203_CR107","doi-asserted-by":"publisher","unstructured":"Poudel S, Kim YJ, Vo DM, Lee SW (2020) Colorectal disease classification using efficiently scaled dilation in convolutional neural network. IEEE Access 8:99227\u201399238. https:\/\/doi.org\/10.1109\/ACCESS.2020.2996770","DOI":"10.1109\/ACCESS.2020.2996770"},{"issue":"7","key":"3203_CR108","doi-asserted-by":"publisher","first-page":"1557","DOI":"10.3390\/electronics12071557","volume":"12","author":"M Nouman Noor","year":"2023","unstructured":"Nouman Noor M, Nazir M, Khan SA, Song O-Y, Ashraf I (2023) Efficient gastrointestinal disease classification using pretrained deep convolutional neural network. Electronics 12(7):1557. https:\/\/doi.org\/10.3390\/electronics12071557","journal-title":"Electronics"},{"key":"3203_CR109","doi-asserted-by":"publisher","unstructured":"Cao B, Li L, Ma Y, Ye S, Li S, He X (2023) RAANet: residual aggregation attention network for classification of small intestinal endoscopic images. In: 2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS). IEEE, Xiangtan, pp 1014\u20131019. https:\/\/doi.org\/10.1109\/DDCLS58216.2023.10167165","DOI":"10.1109\/DDCLS58216.2023.10167165"},{"key":"3203_CR110","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2024.106125","volume":"93","author":"D Bajhaiya","year":"2024","unstructured":"Bajhaiya D, Narayanan Unni S (2024) Deep learning-enabled detection and localization of gastrointestinal diseases using wireless-capsule endoscopic images. Biomed Signal Process Control 93:106125. https:\/\/doi.org\/10.1016\/j.bspc.2024.106125","journal-title":"Biomed Signal Process Control"},{"key":"3203_CR111","doi-asserted-by":"publisher","first-page":"664","DOI":"10.3390\/cancers15030664","volume":"15","author":"W Januszewicz","year":"2023","unstructured":"Januszewicz W, Turkot MH, Malfertheiner P, Regula J (2023) A global perspective on gastric cancer screening: which concepts are feasible, and when? Cancers 15:664. https:\/\/doi.org\/10.3390\/cancers15030664","journal-title":"Cancers"}],"container-title":["Medical & Biological Engineering & Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-024-03203-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-024-03203-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-024-03203-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,21]],"date-time":"2025-01-21T06:17:51Z","timestamp":1737440271000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-024-03203-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,30]]},"references-count":111,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,2]]}},"alternative-id":["3203"],"URL":"https:\/\/doi.org\/10.1007\/s11517-024-03203-y","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,30]]},"assertion":[{"value":"22 May 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 September 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 September 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}]}}