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For: de Lange T, Halvorsen P, Riegler M. Methodology to develop machine learning algorithms to improve performance in gastrointestinal endoscopy. World J Gastroenterol 2018; 24(45): 5057-5062 [PMID: 30568383 DOI: 10.3748/wjg.v24.i45.5057]
URL: https://www.wjgnet.com/1007-9327/full/v24/i45/5057.htm
Number Citing Articles
1
Vajira Thambawita, Debesh Jha, Hugo Lewi Hammer, Håvard D. Johansen, Dag Johansen, Pål Halvorsen, Michael A. Riegler. An Extensive Study on Cross-Dataset Bias and Evaluation Metrics Interpretation for Machine Learning Applied to Gastrointestinal Tract Abnormality ClassificationACM Transactions on Computing for Healthcare 2020; 1(3): 1 doi: 10.1145/3386295
2
Hanna Borgli, Vajira Thambawita, Pia H. Smedsrud, Steven Hicks, Debesh Jha, Sigrun L. Eskeland, Kristin Ranheim Randel, Konstantin Pogorelov, Mathias Lux, Duc Tien Dang Nguyen, Dag Johansen, Carsten Griwodz, Håkon K. Stensland, Enrique Garcia-Ceja, Peter T. Schmidt, Hugo L. Hammer, Michael A. Riegler, Pål Halvorsen, Thomas de Lange. HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopyScientific Data 2020; 7(1) doi: 10.1038/s41597-020-00622-y
3
Nicholas Hoerter, Seth A. Gross, Peter S. Liang. Artificial Intelligence and Polyp DetectionCurrent Treatment Options in Gastroenterology 2020; 18(1): 120 doi: 10.1007/s11938-020-00274-2
4
Muhammad Ramzan, Mudassar Raza, Muhammad Sharif, Muhammad Attique Khan, Yunyoung Nam. Gastrointestinal Tract Infections Classification Using Deep LearningComputers, Materials & Continua 2021; 69(3): 3239 doi: 10.32604/cmc.2021.015920
5
Maryam Kausar Khan, Muhammad Siddique, Naeem Aslam, Muntazir Hussain Khan, Sara Mukhtar, Bushra Syed. Improvement for Diagnosis of Gastric Cancer from Endoscopic Images using Machine LearningVFAST Transactions on Software Engineering 2022; 10(3): 10 doi: 10.21015/vtse.v10i3.1054
6
Dev Gupta, Gaurish Anand, Prince Kirar, Priyanka Meel. Classification of Endoscopic Images and Identification of Gastrointestinal diseases2022 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COM-IT-CON) 2022; : 231 doi: 10.1109/COM-IT-CON54601.2022.9850571
7
Hung Leng Kaan, Khek Yu Ho. Clinical adoption of robotics in endoscopy: Challenges and solutionsJGH Open 2020; 4(5): 790 doi: 10.1002/jgh3.12412
8
Puneet Sharma, Upma Vaid. Emerging role of artificial intelligence in waste management practicesIOP Conference Series: Earth and Environmental Science 2021; 889(1): 012047 doi: 10.1088/1755-1315/889/1/012047
9
G. Singh. Artificial intelligence in colorectal cancer: a reviewSiberian journal of oncology 2023; 22(3): 99 doi: 10.21294/1814-4861-2023-22-3-99-107
10
Mohamed A. Elmagzoub, Swapandeep Kaur, Sheifali Gupta, Adel Rajab, Khairan D. Rajab, Mana Saleh Al Reshan, Hani Alshahrani, Asadullah Shaikh. Improving Endoscopic Image Analysis: Attention Mechanism Integration in Grid Search Fine-Tuned Transfer Learning Model for Multi-Class Gastrointestinal Disease ClassificationIEEE Access 2024; 12: 80345 doi: 10.1109/ACCESS.2024.3408224
11
Yu-Hang Zhang, Lin-Jie Guo, Xiang-Lei Yuan, Bing Hu. Artificial intelligence-assisted esophageal cancer management: Now and futureWorld Journal of Gastroenterology 2020; 26(35): 5256-5271 doi: 10.3748/wjg.v26.i35.5256
12
Junbo Gao, Yuanhao Guo, Yingxue Sun, Guoqiang Qu. Application of Deep Learning for Early Screening of Colorectal Precancerous Lesions under White Light EndoscopyComputational and Mathematical Methods in Medicine 2020; 2020: 1 doi: 10.1155/2020/8374317
13
Yu-Hsi Hsieh, Felix W. Leung. An overview of deep learning algorithms and water exchange in colonoscopy in improving adenoma detectionExpert Review of Gastroenterology & Hepatology 2019; 13(12): 1153 doi: 10.1080/17474124.2019.1694903
14
Danhao Wang, Lanqing Wang, Daogang Peng, Erjiang Qi. Research on Appearance Defect Detection of Power Equipment Based on Improved Faster-RCNN2021 6th International Conference on Power and Renewable Energy (ICPRE) 2021; : 290 doi: 10.1109/ICPRE52634.2021.9635270
15
Debesh Jha, Sharib Ali, Nikhil Kumar Tomar, Havard D. Johansen, Dag Johansen, Jens Rittscher, Michael A. Riegler, Pal Halvorsen. Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep LearningIEEE Access 2021; 9: 40496 doi: 10.1109/ACCESS.2021.3063716
16
Samira Lafraxo, Mohamed El Ansari. GastroNet: Abnormalities Recognition in Gastrointestinal Tract through Endoscopic Imagery using Deep Learning Techniques2020 8th International Conference on Wireless Networks and Mobile Communications (WINCOM) 2020; : 1 doi: 10.1109/WINCOM50532.2020.9272456
17
Zahra Sayyid, Varun Vendra, Kara D. Meister, Catherine D. Krawczeski, Noah J. Speiser, Douglas R. Sidell. Application‐Based Translaryngeal Ultrasound for the Assessment of Vocal Fold Mobility in ChildrenOtolaryngology–Head and Neck Surgery 2019; 161(6): 1031 doi: 10.1177/0194599819877650
18
Feng Liang, Shu Wang, Kai Zhang, Tong-Jun Liu, Jian-Nan Li. Development of artificial intelligence technology in diagnosis, treatment, and prognosis of colorectal cancerWorld Journal of Gastrointestinal Oncology 2022; 14(1): 124-152 doi: 10.4251/wjgo.v14.i1.124
19
Sathishkumar R, Nirmalraj M, Govindarajan M, Jaisree J, Haripriya L, Santhiya M. Convolution Neural Network for Gastrointestinal Cancer Detection and Classification using Deep Learning2023 International Conference on System, Computation, Automation and Networking (ICSCAN) 2023; : 1 doi: 10.1109/ICSCAN58655.2023.10395530
20
Lucas Lacerda de Souza, Felipe Paiva Fonseca, Anna Luiza Damaceno Araújo, Marcio Ajudarte Lopes, Pablo Agustin Vargas, Syed Ali Khurram, Luiz Paulo Kowalski, Harim Tavares dos Santos, Saman Warnakulasuriya, James Dolezal, Alexander T. Pearson, Alan Roger Santos‐Silva. Machine learning for detection and classification of oral potentially malignant disorders: A conceptual reviewJournal of Oral Pathology & Medicine 2023; 52(3): 197 doi: 10.1111/jop.13414
21
Wanderson Gonçalves e Gonçalves, Marcelo Henrique de Paula dos Santos, Fábio Manoel França Lobato, Ândrea Ribeiro-dos-Santos, Gilderlanio Santana de Araújo. Deep learning in gastric tissue diseases: a systematic reviewBMJ Open Gastroenterology 2020; 7(1): e000371 doi: 10.1136/bmjgast-2019-000371
22
Junbo Gao, Qilin Xiong, Chang Yu, Guoqiang Qu, Tao Huang. White-Light Endoscopic Colorectal Lesion Detection Based on Improved YOLOv5Computational and Mathematical Methods in Medicine 2022; 2022: 1 doi: 10.1155/2022/9508004
23
Vasant Rajan, Havish Srinath, Christopher Yii Siang Bong, Alex Cichowski, Christopher J Young, Peter J Hewett. Software Analysis of Colonoscopy Videos Enhances Teaching and Quality MetricsCureus 2022;  doi: 10.7759/cureus.23039
24
Cheng-Si He, Chen-Ji Wang, Jhong-Wei Wang, Yuan-Chen Liu. UY-NET: A Two-Stage Network to Improve the Result of Detection in Colonoscopy ImagesApplied Sciences 2023; 13(19): 10800 doi: 10.3390/app131910800
25
Ken Namikawa, Toshiaki Hirasawa, Kaoru Nakano, Yohei Ikenoyama, Mitsuaki Ishioka, Sho Shiroma, Yoshitaka Tokai, Shoichi Yoshimizu, Yusuke Horiuchi, Akiyoshi Ishiyama, Toshiyuki Yoshio, Tomohiro Tsuchida, Junko Fujisaki, Tomohiro Tada. Artificial intelligence-based diagnostic system classifying gastric cancers and ulcers: comparison between the original and newly developed systemsEndoscopy 2020; 52(12): 1077 doi: 10.1055/a-1194-8771
26
Junlong Li, Junru Liang, Liangqi Ren, Kunpeng Zhang, Harris Wu, Haiwu Li. Colorectal white light filter object detection based on improved YOLOv5International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023) 2024; : 79 doi: 10.1117/12.3026445
27
Shihori Tanabe, Edward J Perkins, Ryuichi Ono, Hiroki Sasaki. Artificial intelligence in gastrointestinal diseasesArtificial Intelligence in Gastroenterology 2021; 2(3): 69-76 doi: 10.35712/aig.v2.i3.69
28
Luigi Manco, Nicola Maffei, Silvia Strolin, Sara Vichi, Luca Bottazzi, Lidia Strigari. Basic of machine learning and deep learning in imaging for medical physicistsPhysica Medica 2021; 83: 194 doi: 10.1016/j.ejmp.2021.03.026