Halic T, De S, Dials J, Gromski MA, Demirel D, Ryason A, Gilmore AC, Al-Haddad MA, Kundumadam S (2020) S1191 task analysis and performance metrics of endoscopic sleeve gastroplasty: preparation for virtual simulation development. Off J Am Coll Gastroenterol ACG 115:S595. https://doi.org/10.14309/01.ajg.0000706812.30100.05

Article  Google Scholar 

Dials J, Demirel D, Halic T, De S, Ryason A, Kundumadam S, Al-Haddad M, Gromski MA (2021) Hierarchical task analysis of endoscopic sleeve gastroplasty. Surg Endosc 1–16

Polese L, Prevedello L, Belluzzi A, Giugliano E, Albanese A, Foletto M (2022) Endoscopic sleeve gastroplasty: results from a single surgical bariatric centre. Updat Surg 74:1971–1975. https://doi.org/10.1007/s13304-022-01385-4

Article  Google Scholar 

Farha J, McGowan C, Hedjoudje A, Itani MI, Abbarh S, Simsek C, Ichkhanian Y, Vulpis T, James TW, Fayad L, Khashab MA, Oberbach A, Badurdeen D, Kumbhari V (2021) Endoscopic sleeve gastroplasty: suturing the gastric fundus does not confer benefit. Endoscopy 53:727–731. https://doi.org/10.1055/a-1236-9347

Article  PubMed  Google Scholar 

Wang J, Perez L (2017) The effectiveness of data augmentation in image classification using deep learning. Convolut Neural Netw Vis Recognit 11:1–8

Google Scholar 

Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16:321–357. https://doi.org/10.1613/jair.953

Article  Google Scholar 

Albawi S, Mohammed TA, Al-Zawi S (2017) Understanding of a convolutional neural network. In: 2017 international conference on engineering and technology (ICET), pp 1–6

Cetinsaya B, Dials J, Demirel D, Halic T, De S, Gromski M, Rex D (2020) Comparison study of deep learning models for colorectal lesions classification. In: Proceedings of the 2020 the 4th international conference on information system and data mining. Association for Computing Machinery, New York, pp 84–88

Hussain Z, Gimenez F, Yi D, Rubin D (2018) Differential data augmentation techniques for medical imaging classification tasks. AMIA Annu Symp Proc 2017:979–984

PubMed  PubMed Central  Google Scholar 

Mikolajczyk A, Grochowski M (2018) Data augmentation for improving deep learning in image classification problem. In: 2018 international interdisciplinary PhD workshop (IIPhDW). IEEE, Swinoujście, pp 117–122

Taylor L, Nitschke G (2018) Improving deep learning with generic data augmentation. In: 2018 IEEE symposium series on computational intelligence (SSCI). IEEE, pp 1542–1547

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 8:7497. https://doi.org/10.1038/s41598-018-25842-6

Article  CAS  PubMed  PubMed Central  Google Scholar 

Li Q, Cai W, Wang X, Zhou Y, Feng DD, Chen M (2014) Medical image classification with convolutional neural network. In: 2014 13th international conference on control automation robotics vision (ICARCV), pp 844–848

Mirza S, Mittal S, Zaman M (2018) Decision support predictive model for prognosis of diabetes using SMOTE and decision tree. Int J Appl Eng Res 13:9277–9282

Google Scholar 

Bellinger C, Drummond C, Japkowicz N (2016) Beyond the boundaries of SMOTE. In: Frasconi P, Landwehr N, Manco G, Vreeken J (eds) Machine learning and knowledge discovery in databases. Springer, Cham, pp 248–263

Chapter  Google Scholar 

Abeysinghe W, Hung C-C, Bechikh S, Wang X, Rattani A (2018) Clustering algorithms on imbalanced data using the SMOTE technique for image segmentation. In: Proceedings of the 2018 conference on research in adaptive and convergent systems. ACM, Honolulu Hawaii, pp 17–22

Dials J, Demirel D, Sanchez-Arias R, Halic T, Kruger U, De S, Gromski MA (2023) Skill-level classification and performance evaluation for endoscopic sleeve gastroplasty. Surg Endosc 1–12