{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T05:05:30Z","timestamp":1750309530359,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":64,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,11,8]],"date-time":"2024-11-08T00:00:00Z","timestamp":1731024000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Develop ment Fund of Macau","award":["0026\/2022\/A"],"award-info":[{"award-number":["0026\/2022\/A"]}]},{"name":"Guangdong Basic and Applied Basic Research Fund, Shenzhen Joint Fund (Guangdong Shenzhen Joint Fund), Guangdong-Hong Kong-Macau Research Team Project","award":["2021B1515130003"],"award-info":[{"award-number":["2021B1515130003"]}]},{"name":"Key Research and Development Plan of Hubei Province","award":["2022BCE034"],"award-info":[{"award-number":["2022BCE034"]}]},{"DOI":"10.13039\/501100003819","name":"Natural Science Foundation of Hubei Province","doi-asserted-by":"publisher","award":["2024AFB1054"],"award-info":[{"award-number":["2024AFB1054"]}],"id":[{"id":"10.13039\/501100003819","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,11,8]]},"DOI":"10.1145\/3707127.3707129","type":"proceedings-article","created":{"date-parts":[[2025,2,6]],"date-time":"2025-02-06T12:34:06Z","timestamp":1738845246000},"page":"10-17","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi-modal Machine Learning in Gastrointestinal Endoscopy: A Review"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7015-4510","authenticated-orcid":false,"given":"In Neng","family":"Chan","sequence":"first","affiliation":[{"name":"Department of Electromechanical Engineering, University of Macau, Macau, Macao"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7623-6904","authenticated-orcid":false,"given":"Pak Kin","family":"Wong","sequence":"additional","affiliation":[{"name":"Department of Electromechanical Engineering, University of Macau, Macau, Macao"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8929-015X","authenticated-orcid":false,"given":"Tao","family":"Yan","sequence":"additional","affiliation":[{"name":"Department of Electromechanical Engineering, University of Macau, Macau, Macao and School of Mechanical Engineering, Hubei University of Arts and Science, Xiangyang, Hubei, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-4275-6040","authenticated-orcid":false,"given":"Yanyan","family":"Hu","sequence":"additional","affiliation":[{"name":"Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-0999-5028","authenticated-orcid":false,"given":"Chon In","family":"Chan","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, Kiang Wu Hospital, Macau, Macao"}]}],"member":"320","published-online":{"date-parts":[[2025,2,6]]},"reference":[{"key":"e_1_3_3_1_2_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/978-3-030-74786-2","volume-title":"Handbook of Medical and Health Sciences in Developing Countries: Education, Practice, and Research","author":"Al-Worafi Yaser\u00a0Mohammed","year":"2023","unstructured":"Yaser\u00a0Mohammed Al-Worafi. 2023. Epidemiology and Burden of Digestive Diseases in Developing Countries. In Handbook of Medical and Health Sciences in Developing Countries: Education, Practice, and Research. Springer, 1\u201324."},{"key":"e_1_3_3_1_3_2","doi-asserted-by":"crossref","unstructured":"Tadas Baltru\u0161aitis Chaitanya Ahuja and Louis-Philippe Morency. 2018. Multimodal Machine Learning: A Survey and Taxonomy. IEEE Transactions on Pattern Analysis and Machine Intelligence 41 2 (2018) 423\u2013443.","DOI":"10.1109\/TPAMI.2018.2798607"},{"key":"e_1_3_3_1_4_2","doi-asserted-by":"crossref","unstructured":"Daphn\u00e9e Beaulieu Alan\u00a0N Barkun Catherine Dub\u00e9 Jill Tinmouth Pierre Hall\u00e9 and Myriam Martel. 2013. Endoscopy reporting standards. Canadian Journal of Gastroenterology and Hepatology 27 5 (2013) 286\u2013292.","DOI":"10.1155\/2013\/145894"},{"key":"e_1_3_3_1_5_2","doi-asserted-by":"crossref","unstructured":"Fatemeh Behrad and Mohammad\u00a0Saniee Abadeh. 2022. An overview of deep learning methods for multimodal medical data mining. Expert Systems with Applications 200 (2022) 117006.","DOI":"10.1016\/j.eswa.2022.117006"},{"key":"e_1_3_3_1_6_2","doi-asserted-by":"crossref","unstructured":"Diego Bravo Josue Ruano Mar\u00eda Gonz\u00e1lez Sebastian Medina Martin G\u00f3mez Fabio Gonz\u00e1lez and Eduardo Romero. 2024. Automatic Endoscopy Classification by Fusing Depth Estimations and Image Information.","DOI":"10.1109\/ISBI56570.2024.10635452"},{"key":"e_1_3_3_1_7_2","doi-asserted-by":"crossref","unstructured":"Wenhao Chai and Gaoang Wang. 2022. Deep vision multimodal learning: Methodology benchmark and trend. Applied Sciences 12 13 (2022) 6588.","DOI":"10.3390\/app12136588"},{"key":"e_1_3_3_1_8_2","doi-asserted-by":"crossref","unstructured":"Shuai Ding Shikang Hu Xiaojian Li Youtao Zhang and Desheng\u00a0Dash Wu. 2021. Leveraging multimodal semantic fusion for gastric cancer screening via hierarchical attention mechanism. IEEE Transactions on Systems Man and Cybernetics: Systems 52 7 (2021) 4286\u20134299.","DOI":"10.1109\/TSMC.2021.3096974"},{"key":"e_1_3_3_1_9_2","doi-asserted-by":"crossref","unstructured":"Shuai Ding Hui Huang Zhenmin Li Xiao Liu and Shanlin Yang. 2020. SCNET: A novel UGI cancer screening framework based on semantic-level multimodal data fusion. IEEE Journal of Biomedical and Health Informatics 25 1 (2020) 143\u2013151.","DOI":"10.1109\/JBHI.2020.2983126"},{"key":"e_1_3_3_1_10_2","doi-asserted-by":"crossref","unstructured":"Hongliu Du Zehua Dong Lianlian Wu Yanxia Li Jun Liu Chaijie Luo Xiaoquan Zeng Yunchao Deng Du Cheng Wenxiu Diao et\u00a0al. 2023. A deep-learning based system using multi-modal data for diagnosing gastric neoplasms in real-time (with video). Gastric Cancer 26 2 (2023) 275\u2013285.","DOI":"10.1007\/s10120-022-01358-x"},{"key":"e_1_3_3_1_11_2","doi-asserted-by":"crossref","unstructured":"Junwei Duan Jiaqi Xiong Yinghui Li and Weiping Ding. 2024. Deep learning based multimodal biomedical data fusion: An overview and comparative review. Information Fusion (2024) 102536.","DOI":"10.1016\/j.inffus.2024.102536"},{"key":"e_1_3_3_1_12_2","doi-asserted-by":"crossref","unstructured":"Dayna\u00a0S Early Tamir Ben-Menachem G\u00a0Anton Decker John\u00a0A Evans Robert\u00a0D Fanelli Deborah\u00a0A Fisher Norio Fukami Joo\u00a0Ha Hwang Rajeev Jain Terry\u00a0L Jue et\u00a0al. 2012. Appropriate use of GI endoscopy. Gastrointestinal endoscopy 75 6 (2012) 1127\u20131131.","DOI":"10.1016\/j.gie.2012.01.011"},{"key":"e_1_3_3_1_13_2","doi-asserted-by":"crossref","unstructured":"Ahmad El\u00a0Hajjar and Jean-Fran\u00e7ois Rey. 2020. Artificial intelligence in gastrointestinal endoscopy: general overview. Chinese Medical Journal 133 3 (2020) 326.","DOI":"10.1097\/CM9.0000000000000623"},{"key":"e_1_3_3_1_14_2","doi-asserted-by":"crossref","unstructured":"James\u00a0E Everhart and Constance\u00a0E Ruhl. 2009. Burden of Digestive Diseases in the United States Part I: Overall and Upper Gastrointestinal Diseases. Gastroenterology 136 2 (2009) 376\u2013386.","DOI":"10.1053\/j.gastro.2008.12.015"},{"key":"e_1_3_3_1_15_2","doi-asserted-by":"crossref","unstructured":"Roger Fonoll\u00e0 Quirine EW\u00a0van\u00a0der Zander Ramon\u00a0M Schreuder Ad\u00a0AM Masclee Erik\u00a0J Schoon Fons van\u00a0der Sommen and Peter\u00a0HN de With. 2020. A CNN CADx system for multimodal classification of colorectal polyps combining WL BLI and LCI modalities. Applied Sciences 10 15 (2020) 5040.","DOI":"10.3390\/app10155040"},{"key":"e_1_3_3_1_16_2","doi-asserted-by":"publisher","unstructured":"Roger Fonoll\u00e0 Quirine\u00a0E.W. van der Zander Ramon\u00a0M. Schreuder Sharmila Subramaniam Pradeep Bhandari Ad\u00a0A.M. Masclee Erik\u00a0J. Schoon Fons van der Sommen and Peter\u00a0H.N. de With. 2021. Automatic image and text-based description for colorectal polyps using BASIC classification. Artificial Intelligence in Medicine 121 (2021) 102178. 10.1016\/j.artmed.2021.102178","DOI":"10.1016\/j.artmed.2021.102178"},{"key":"e_1_3_3_1_17_2","doi-asserted-by":"crossref","unstructured":"Jing Gao Peng Li Zhikui Chen and Jianing Zhang. 2020. A survey on deep learning for multimodal data fusion. Neural Computation 32 5 (2020) 829\u2013864.","DOI":"10.1162\/neco_a_01273"},{"key":"e_1_3_3_1_18_2","doi-asserted-by":"crossref","unstructured":"Xiaohong\u00a0W Gao Stephen Taylor Wei Pang Rui Hui Xin Lu Barbara Braden Oxford\u00a0GI Investigators et\u00a0al. 2023. Fusion of colour contrasted images for early detection of oesophageal squamous cell dysplasia from endoscopic videos in real time. Information Fusion 92 (2023) 64\u201379.","DOI":"10.1016\/j.inffus.2022.11.023"},{"key":"e_1_3_3_1_19_2","doi-asserted-by":"crossref","unstructured":"Donald Garrow and Mark\u00a0H Delegge. 2010. Risk Factors for Gastrointestinal Ulcer Disease in the US Population. Digestive Diseases and Sciences 55 1 (2010) 66.","DOI":"10.1007\/s10620-008-0708-x"},{"key":"e_1_3_3_1_20_2","unstructured":"Google. [n. d.]. Gemini - chat to supercharge your ideas. https:\/\/gemini.google.com\/. (Accessed on 08\/08\/2024)."},{"key":"e_1_3_3_1_21_2","volume-title":"Standardization and Coding of Gastrointestinal Endoscopy Reports","author":"Groenen Marcel","year":"2011","unstructured":"Marcel Groenen. 2011. Standardization and Coding of Gastrointestinal Endoscopy Reports."},{"key":"e_1_3_3_1_22_2","doi-asserted-by":"publisher","unstructured":"Zhongyu He Peng Wang Yuelong Liang Zuoming Fu and Xuesong Ye. 2021. Clinically Available Optical Imaging Technologies in Endoscopic Lesion Detection: Current Status and Future Perspective. Journal of Healthcare Engineering 2021 1 (2021) 7594513. 10.1155\/2021\/7594513 arXiv:https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2021\/7594513","DOI":"10.1155\/2021\/7594513"},{"key":"e_1_3_3_1_23_2","doi-asserted-by":"crossref","unstructured":"Arthur Hoffman Henrik Manner Johannes\u00a0W Rey and Ralf Kiesslich. 2017. A guide to multimodal endoscopy imaging for gastrointestinal malignancy\u2014an early indicator. Nature Reviews Gastroenterology & Hepatology 14 7 (2017) 421\u2013434.","DOI":"10.1038\/nrgastro.2017.46"},{"key":"e_1_3_3_1_24_2","doi-asserted-by":"crossref","unstructured":"Summaira Jabeen Xi Li Muhammad\u00a0Shoib Amin Omar Bourahla Songyuan Li and Abdul Jabbar. 2023. A review on methods and applications in multimodal deep learning. ACM Transactions on Multimedia Computing Communications and Applications 19 2s (2023) 1\u201341.","DOI":"10.1145\/3545572"},{"key":"e_1_3_3_1_25_2","doi-asserted-by":"crossref","unstructured":"Beata Jab\u0142o\u0144ska and S\u0142awomir Mrowiec. 2023. Gastrointestinal disease: new diagnostic and therapeutic approaches. 1420\u00a0pages.","DOI":"10.3390\/biomedicines11051420"},{"key":"e_1_3_3_1_26_2","doi-asserted-by":"crossref","unstructured":"Marjon Kerkhof Herman Van\u00a0Dekken EW Steyerberg GA Meijer AH Mulder Adriaan De\u00a0Bru\u00efne Ann Driessen FJ Ten\u00a0Kate JG Kusters EJ Kuipers et\u00a0al. 2007. Grading of dysplasia in Barrett\u2019s oesophagus: substantial interobserver variation between general and gastrointestinal pathologists. Histopathology 50 7 (2007) 920\u2013927.","DOI":"10.1111\/j.1365-2559.2007.02706.x"},{"key":"e_1_3_3_1_27_2","doi-asserted-by":"crossref","unstructured":"Hee\u00a0Sun Kim Su\u00a0Jung Baik Kyung\u00a0Hee Kim Cho\u00a0Rong Oh Jung\u00a0Hyun Lee Wan\u00a0Jae Jo Hye\u00a0Kyoung Kim Eun\u00a0Young Kim and Min\u00a0Jung Kim. [n. d.]. Prevalence of and Risk Factors for Gastrointestinal Diseases in Korean Americans and Native Koreans Undergoing Screening Endoscopy. Gut and Liver 7 5 ([n. d.]) 539\u2013545.","DOI":"10.5009\/gnl.2013.7.5.539"},{"key":"e_1_3_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-48687-1_5"},{"key":"e_1_3_3_1_29_2","doi-asserted-by":"crossref","unstructured":"Dana Lahat T\u00fclay Adali and Christian Jutten. 2015. Multimodal data fusion: an overview of methods challenges and prospects. Proc. IEEE 103 9 (2015) 1449\u20131477.","DOI":"10.1109\/JPROC.2015.2460697"},{"key":"e_1_3_3_1_30_2","doi-asserted-by":"crossref","unstructured":"Jiajia Li Xue Han Yiming Qin Feng Tan Yulong Chen Zikai Wang Haitao Song Xi Zhou Yuan Zhang Lun Hu et\u00a0al. 2023. Artificial intelligence accelerates multi-modal biomedical process: A Survey. Neurocomputing 558 (2023) 126720.","DOI":"10.1016\/j.neucom.2023.126720"},{"key":"e_1_3_3_1_31_2","unstructured":"Xia Li and Honggang Yu. 2019. Advances in endoscopic diagnosis of early gastric cancer. Journal of Hainan Medical College 25 5 (2019) 392\u2013395."},{"key":"e_1_3_3_1_32_2","doi-asserted-by":"publisher","unstructured":"Jin Liu Yu-Dong Zhang and Hongming Cai. 2024. Editorial: Multi-modal learning and its application for biomedical data. Frontiers in Medicine 10 (2024). 10.3389\/fmed.2023.1342374","DOI":"10.3389\/fmed.2023.1342374"},{"key":"e_1_3_3_1_33_2","unstructured":"Kuan Liu Yanen Li Ning Xu and Prem Natarajan. 2018. Learn to combine modalities in multimodal deep learning. arXiv preprint arXiv:1805.11730 (2018)."},{"key":"e_1_3_3_1_34_2","doi-asserted-by":"crossref","unstructured":"Zihua Lu Youming Xu Liwen Yao Wei Zhou Wei Gong Genhua Yang Mingwen Guo Beiping Zhang Xu Huang Chunping He et\u00a0al. 2022. Real-time automated diagnosis of colorectal cancer invasion depth using a deep learning model with multimodal data (with video). Gastrointestinal Endoscopy 95 6 (2022) 1186\u20131194.","DOI":"10.1016\/j.gie.2021.11.049"},{"key":"e_1_3_3_1_35_2","doi-asserted-by":"publisher","unstructured":"Changzheng Ma Peng Zhang Shiyu Du et\u00a0al. 2024. Prediction of the gastric precancerous risk based on deep learning of multimodal medical images. Research Square (18 July 2024). 10.21203\/rs.3.rs-4747833\/v1PREPRINT (Version 1).","DOI":"10.21203\/rs.3.rs-4747833\/v1"},{"key":"e_1_3_3_1_36_2","doi-asserted-by":"crossref","unstructured":"Dominic\u00a0W Massaro. 2012. Multimodal learning. Encyclopedia of the Sciences of Learning (2012) 2375\u20132378.","DOI":"10.1007\/978-1-4419-1428-6_273"},{"key":"e_1_3_3_1_37_2","doi-asserted-by":"crossref","unstructured":"Alican Mertan Damien\u00a0Jade Duff and Gozde Unal. 2022. Single image depth estimation: An overview. Digital Signal Processing 123 (2022) 103441.","DOI":"10.1016\/j.dsp.2022.103441"},{"key":"e_1_3_3_1_38_2","doi-asserted-by":"crossref","unstructured":"Meta. [n. d.]. Llama 3.1. https:\/\/llama.meta.com\/. (Accessed on 08\/08\/2024).","DOI":"10.55041\/IJSREM34150"},{"key":"e_1_3_3_1_39_2","doi-asserted-by":"crossref","unstructured":"Yue Ming Xuyang Meng Chunxiao Fan and Hui Yu. 2021. Deep learning for monocular depth estimation: A review. Neurocomputing 438 (2021) 14\u201333.","DOI":"10.1016\/j.neucom.2020.12.089"},{"key":"e_1_3_3_1_40_2","first-page":"689","volume-title":"Proceedings of the 28th international conference on machine learning (ICML-11)","author":"Ngiam Jiquan","year":"2011","unstructured":"Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee, and Andrew\u00a0Y Ng. 2011. Multimodal deep learning. In Proceedings of the 28th international conference on machine learning (ICML-11). 689\u2013696."},{"key":"e_1_3_3_1_41_2","doi-asserted-by":"crossref","unstructured":"John\u00a0E Pandolfino Nimish\u00a0B Vakil and Peter\u00a0J Kahrilas. 2002. Comparison of inter-and intraobserver consistency for grading of esophagitis by expert and trainee endoscopists. Gastrointestinal Endoscopy 56 5 (2002) 639\u2013643.","DOI":"10.1016\/S0016-5107(02)70110-7"},{"key":"e_1_3_3_1_42_2","series-title":"Proceedings of Machine Learning Research","first-page":"8748","volume-title":"Proceedings of the 38th International Conference on Machine Learning","volume":"139","author":"Radford Alec","year":"2021","unstructured":"Alec Radford, Jong\u00a0Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. 2021. Learning Transferable Visual Models From Natural Language Supervision. In Proceedings of the 38th International Conference on Machine Learning(Proceedings of Machine Learning Research, Vol.\u00a0139), Marina Meila and Tong Zhang (Eds.). PMLR, 8748\u20138763. https:\/\/proceedings.mlr.press\/v139\/radford21a.html"},{"key":"e_1_3_3_1_43_2","doi-asserted-by":"publisher","unstructured":"Dhanesh Ramachandram and Graham\u00a0W. Taylor. 2017. Deep Multimodal Learning: A Survey on Recent Advances and Trends. IEEE Signal Processing Magazine 34 6 (2017) 96\u2013108. 10.1109\/MSP.2017.2738401","DOI":"10.1109\/MSP.2017.2738401"},{"key":"e_1_3_3_1_44_2","series-title":"Proceedings of Machine Learning Research","first-page":"8821","volume-title":"Proceedings of the 38th International Conference on Machine Learning","volume":"139","author":"Ramesh Aditya","year":"2021","unstructured":"Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever. 2021. Zero-Shot Text-to-Image Generation. In Proceedings of the 38th International Conference on Machine Learning(Proceedings of Machine Learning Research, Vol.\u00a0139), Marina Meila and Tong Zhang (Eds.). PMLR, 8821\u20138831. https:\/\/proceedings.mlr.press\/v139\/ramesh21a.html"},{"key":"e_1_3_3_1_45_2","doi-asserted-by":"publisher","unstructured":"Aaron\u00a0N. Richter and Taghi\u00a0M. Khoshgoftaar. 2018. A review of statistical and machine learning methods for modeling cancer risk using structured clinical data. Artificial Intelligence in Medicine 90 (2018) 1\u201314. 10.1016\/j.artmed.2018.06.002","DOI":"10.1016\/j.artmed.2018.06.002"},{"key":"e_1_3_3_1_46_2","doi-asserted-by":"crossref","unstructured":"Tanith\u00a0C Rose Andy Pennington Chris Kypridemos Tao Chen Moeez Subhani Johanna Hanefeld Luigi Ricciardiello and Ben Barr. 2022. Analysis of the burden and economic impact of digestive diseases and investigation of research gaps and priorities in the field of digestive health in the European Region\u2014White Book 2: Executive summary. United European Gastroenterology Journal 10 7 (2022) 657.","DOI":"10.1002\/ueg2.12298"},{"key":"e_1_3_3_1_47_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCUBEA.2018.8697857"},{"key":"e_1_3_3_1_48_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58721-5_2"},{"key":"e_1_3_3_1_49_2","doi-asserted-by":"crossref","unstructured":"Pieter Sinonquel S\u00e9verine Vermeire Frederik Maes and Raf Bisschops. 2023. Advanced imaging in gastrointestinal endoscopy: A literature review of the current state of the art. GE-Portuguese Journal of Gastroenterology 30 3 (2023) 175\u2013191.","DOI":"10.1159\/000527083"},{"key":"e_1_3_3_1_50_2","doi-asserted-by":"crossref","unstructured":"Qiaosen Su Fengsheng Wang Dong Chen Gang Chen Chao Li and Leyi Wei. 2022. Deep convolutional neural networks with ensemble learning and transfer learning for automated detection of gastrointestinal diseases. Computers in Biology and Medicine 150 (2022) 106054.","DOI":"10.1016\/j.compbiomed.2022.106054"},{"key":"e_1_3_3_1_51_2","doi-asserted-by":"publisher","unstructured":"Maryam Tayefi Phuong Ngo Taridzo Chomutare Hercules Dalianis Elisa Salvi Andrius Budrionis and Fred Godtliebsen. 2021. Challenges and opportunities beyond structured data in analysis of electronic health records. WIREs Computational Statistics 13 6 (2021) e1549. 10.1002\/wics.1549 arXiv:https:\/\/wires.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/wics.1549","DOI":"10.1002\/wics.1549"},{"key":"e_1_3_3_1_52_2","doi-asserted-by":"crossref","unstructured":"Pietro Valdastri Massimiliano Simi and Robert J\u00a0Webster III. 2012. Advanced Technologies for Gastrointestinal Endoscopy. Annual Review of Biomedical Engineering 14 (2012) 397\u2013429.","DOI":"10.1146\/annurev-bioeng-071811-150006"},{"key":"e_1_3_3_1_53_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403234"},{"key":"e_1_3_3_1_54_2","doi-asserted-by":"crossref","unstructured":"Wei Wang Xin Yang and Jinhui Tang. 2023. Vision transformer with hybrid shifted windows for gastrointestinal endoscopy image classification. IEEE Transactions on Circuits and Systems for Video Technology (2023).","DOI":"10.1109\/TCSVT.2023.3277462"},{"key":"e_1_3_3_1_55_2","doi-asserted-by":"crossref","unstructured":"Yu Wang Yu Hong Yue Wang Xin Zhou Xin Gao Chenyan Yu Jiaxi Lin Lu Liu Jingwen Gao Minyue Yin et\u00a0al. 2023. Automated Multimodal Machine Learning for Esophageal Variceal Bleeding Prediction Based on Endoscopy and Structured Data. Journal of Digital Imaging 36 1 (2023) 326\u2013338.","DOI":"10.1007\/s10278-022-00724-6"},{"key":"e_1_3_3_1_56_2","doi-asserted-by":"crossref","unstructured":"Yichen Wang Yuting Huang Robert\u00a0C Chase Tian Li Daryl Ramai Si Li Xiaoquan Huang Samuel\u00a0O Antwi Andrew\u00a0P Keaveny and Maoyin Pang. 2023. Global burden of digestive diseases: A systematic analysis of the global burden of diseases study 1990 to 2019. Gastroenterology 165 3 (2023) 773\u2013783.","DOI":"10.1053\/j.gastro.2023.05.050"},{"key":"e_1_3_3_1_57_2","doi-asserted-by":"crossref","unstructured":"Peng Xu Xiatian Zhu and David\u00a0A Clifton. 2023. Multimodal learning with transformers: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 45 10 (2023) 12113\u201312132.","DOI":"10.1109\/TPAMI.2023.3275156"},{"key":"e_1_3_3_1_58_2","doi-asserted-by":"publisher","unstructured":"Youming Xu Zehua Dong Li Huang Hongliu Du Ting Yang Chaijie Luo Xiao Tao Junxiao Wang Zhifeng Wu Lianlian Wu Rong Lin and Honggang Yu. 2024. Multi-step validation of a Post-Endoscopic Retrograde Cholangiopancreatography Pancreatitis prediction system integrating multi-modal data: A multi-center study. Gastrointestinal Endoscopy (2024). 10.1016\/j.gie.2024.03.033","DOI":"10.1016\/j.gie.2024.03.033"},{"key":"e_1_3_3_1_59_2","doi-asserted-by":"crossref","unstructured":"S Yalamarthi P Witherspoon D McCole and CD Auld. 2004. Missed Diagnoses in Patients with Upper Gastrointestinal Cancers. Endoscopy 36 10 (2004) 874\u2013879.","DOI":"10.1055\/s-2004-825853"},{"key":"e_1_3_3_1_60_2","doi-asserted-by":"publisher","unstructured":"Zhuoyue Yang Junjun Pan Ju Dai Zhen Sun and Yi Xiao. 2024. Self-supervised endoscopy depth estimation framework with CLIP-guidance segmentation. Biomedical Signal Processing and Control 95 (2024) 106410. 10.1016\/j.bspc.2024.106410","DOI":"10.1016\/j.bspc.2024.106410"},{"key":"e_1_3_3_1_61_2","doi-asserted-by":"crossref","unstructured":"Chao Zhang Zichao Yang Xiaodong He and Li Deng. 2020. Multimodal intelligence: Representation learning information fusion and applications. IEEE Journal of Selected Topics in Signal Processing 14 3 (2020) 478\u2013493.","DOI":"10.1109\/JSTSP.2020.2987728"},{"key":"e_1_3_3_1_62_2","unstructured":"Wenfan Zhang Chunlian Qi Zexing Chen Junqing Chen Peifan Zhang Yinchang Zhang and Yazhen Bai. 1984. Discussion on cases of early gastric cancer missed by gastric endoscopic examination. Chinese Journal of Oncology 6 5 (1984) 361\u2013363."},{"key":"e_1_3_3_1_63_2","doi-asserted-by":"crossref","unstructured":"Chaoqiang Zhao Qiyu Sun Chongzhen Zhang Yang Tang and Feng Qian. 2020. Monocular depth estimation based on deep learning: An overview. Science China Technological Sciences 63 9 (2020) 1612\u20131627.","DOI":"10.1007\/s11431-020-1582-8"},{"key":"e_1_3_3_1_64_2","doi-asserted-by":"crossref","unstructured":"Cenyang Zheng Xun Gong Lin Fan and Jiao Li. 2024. Reconstructing Missing Modalities in Multi-Modal Endoscopic Ultrasound Via Cross-Modal Feature Replacement Representation. (2024). Available at SSRN: https:\/\/ssrn.com\/abstract=4877929 or http:\/\/dx.doi.org\/10.2139\/ssrn.4877929.","DOI":"10.2139\/ssrn.4877929"},{"key":"e_1_3_3_1_65_2","doi-asserted-by":"crossref","unstructured":"Tongxue Zhou Pierre Vera St\u00e9phane Canu and Su Ruan. 2022. Missing data imputation via conditional generator and correlation learning for multimodal brain tumor segmentation. Pattern Recognition Letters 158 (2022) 125\u2013132.","DOI":"10.1016\/j.patrec.2022.04.019"}],"event":{"name":"ICBBE 2024: 2024 11th International Conference on Biomedical and Bioinformatics Engineering","acronym":"ICBBE 2024","location":"Osaka Japan"},"container-title":["Proceedings of the 2024 11th International Conference on Biomedical and Bioinformatics Engineering"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3707127.3707129","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3707127.3707129","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:18:13Z","timestamp":1750295893000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3707127.3707129"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,8]]},"references-count":64,"alternative-id":["10.1145\/3707127.3707129","10.1145\/3707127"],"URL":"https:\/\/doi.org\/10.1145\/3707127.3707129","relation":{},"subject":[],"published":{"date-parts":[[2024,11,8]]},"assertion":[{"value":"2025-02-06","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}