Name
Title | ||
|---|---|---|
Selecting radiomic features from FDG-PET images for cancer treatment outcome prediction. |
Abstract | ||
|---|---|---|
•A stable system for cancer treatment outcome prediction is proposed.•Radiomic features extracted from FDG-PET images are used to construct the system.•Input features are selected by a robust method based on Dempster-Shafer theory.•A data balancing procedure is included to tackle imbalanced learning problem.•Prior knowledge is specified to improve the reliability of selected feature subsets. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.media.2016.05.007 | Medical Image Analysis |
Keywords | Field | DocType |
Dempster–Shafer theory,Feature selection,Imbalanced learning,Outcome prediction,Cancer,PET images | Data mining,Expression (mathematics),Feature selection,Radiation treatment planning,Cancer therapy,Artificial intelligence,Classifier (linguistics),Prediction system,Pattern recognition,Subspace topology,Dempster–Shafer theory,Mathematics,Machine learning | Journal |
Volume | ISSN | Citations |
32 | 1361-8415 | 10 |
PageRank | References | Authors |
0.50 | 23 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chunfeng Lian | 1 | 132 | 22.61 |
| Ruan Su | 2 | 559 | 53.00 |
| Thierry Denoeux | 3 | 815 | 74.98 |
| Fabrice Jardin | 4 | 19 | 2.34 |
| Pierre Vera | 5 | 59 | 10.15 |