Name
Title | ||
|---|---|---|
Quantitative analysis of patients with celiac disease by video capsule endoscopy: A deep learning method. |
Abstract | ||
|---|---|---|
Background. Celiac disease is one of the most common diseases in the world. Capsule endoscopy is an alternative way to visualize the entire small intestine without invasiveness to the patient. It is useful to characterize celiac disease, but hours are need to manually analyze the retrospective data of a single patient. Computer-aided quantitative analysis by a deep learning method helps in alleviating the workload during analysis of the retrospective videos. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.compbiomed.2017.03.031 | Computers in Biology and Medicine |
Keywords | Field | DocType |
Celiac disease,Videocapsule endoscopy,Deep learning,GoogLeNet,Quantitative analysis | Disease,Entire small intestine,Videocapsule Endoscopy,Artificial intelligence,Mucosal lesions,Deep learning,Capsule endoscopy,Surgery,Medicine,CLIPS | Journal |
Volume | ISSN | Citations |
85 | 0010-4825 | 6 |
PageRank | References | Authors |
0.50 | 11 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Teng Zhou | 1 | 6 | 0.50 |
| Guoqiang Han | 2 | 439 | 43.27 |
| Bing Nan Li | 3 | 240 | 18.77 |
| Zhizhe Lin | 4 | 6 | 0.84 |
| Edward J. Ciaccio | 5 | 165 | 30.79 |
| Peter H. R. Green | 6 | 48 | 5.26 |
| Jing Qin | 7 | 1109 | 95.43 |