Name
Abstract | ||
|---|---|---|
Relative location prediction in computed tomography (CT) scan images is a challenging problem. In this paper, a regression model based on one-dimensional convolutional neural networks is proposed to determine the relative location of a CT scan image both robustly and precisely. A public dataset is employed to validate the performance of the studyu0027s proposed method using a 5-fold cross validation. Experimental results demonstrate an excellent performance of the proposed model when compared with the state-of-the-art techniques, achieving a median absolute error of 1.04 cm and mean absolute error of 1.69 cm. |
Year | Venue | Field |
|---|---|---|
2017 | arXiv: Computer Vision and Pattern Recognition | Computer vision,Pattern recognition,Convolutional neural network,Regression analysis,Computer science,Mean absolute error,Artificial intelligence,Computed tomography,Deep learning,Location prediction,Cross-validation,Approximation error |
DocType | Volume | Citations |
Journal | abs/1711.07624 | 0 |
PageRank | References | Authors |
0.34 | 8 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiajia Guo | 1 | 0 | 0.68 |
| Hongwei Du | 2 | 43 | 7.29 |
| Bensheng Qiu | 3 | 11 | 6.59 |
| Xiao Liang | 4 | 1 | 5.09 |