Labelling Stomach Anatomical Locations In Upper Gastrointestinal Endoscopic Images Using a CNN

Author:

Vu Hai1,Manh Xuan Huy1,Duc Bach Quan1,Ha Van Kien1,Dao Viet Hang2,Nguyen Phuc Binh3,Hoang Bao Long4,Vu Thanh Hai5

Affiliation:

1. MICA Institute, HUST Hanoi, Vietnam

2. HMU Inst. of Gastroenterology and Hepatology Hanoi, Vietnam

3. Inst. of Gastroenterology and Hepatology Hanoi, Vietnam

4. Inst. of Gastroenterology and Hepatology, Hanoi, Vietnam

5. Viettel Cooperation Hanoi, Vietnam

Publisher

ACM Press

Reference18 articles.

1. Andre Esteva, Brett Kuprel, Roberto A Novoa, Justin Ko, Susan M Swetter, Helen M Blau, and Sebastian Thrun. 2017. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 7639 (2017), 115.

2. Varun Gulshan, Lily Peng, Marc Coram, Martin C Stumpe, Derek Wu, Arunachalam Narayanaswamy, Subhashini Venugopalan, Kasumi Widner, Tom Madams, Jorge Cuadros, et al. 2016. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. Jama 316, 22 (2016), 2402--2410.

3. T. Hirasawa, K. Aoyama, and T. Tanimoto. 2018. Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images. Gastric Cancer 21 (2018), 653--660. Issue 4.

4. Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. 2017. ImageNet classification with deep convolutional neural networks. Communications of the ACM. 60 (2017). Issue 6.

5. Mengyuan Ma, Jie Chen, Wei Liu, and Wei Yang. 2018. Ship Classification and Detection Based on CNN Using GF-3 SAR Images. Remote Sensing 10 (2018), 20--43.

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