Name
Title | ||
|---|---|---|
Deep Learning for Lung Cancer Detection: Tackling the Kaggle Data Science Bowl 2017 Challenge. |
Abstract | ||
|---|---|---|
We present a deep learning framework for computer-aided lung cancer diagnosis. Our multi-stage framework detects nodules in 3D lung CAT scans, determines if each nodule is malignant, and finally assigns a cancer probability based on these results. We discuss the challenges and advantages of our framework. In the Kaggle Data Science Bowl 2017, our framework ranked 41st out of 1972 teams. |
Year | Venue | Field |
|---|---|---|
2017 | arXiv: Computer Vision and Pattern Recognition | Data science,Lung cancer,Ranking,Computer science,Artificial intelligence,Deep learning,Cancer,Machine learning |
DocType | Volume | Citations |
Journal | abs/1705.09435 | 4 |
PageRank | References | Authors |
0.49 | 14 | 10 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kingsley Kuan | 1 | 21 | 1.77 |
| Mathieu Ravaut | 2 | 5 | 1.85 |
| Gaurav Manek | 3 | 23 | 2.52 |
| Huiling Chen | 4 | 10 | 2.02 |
| Jie Lin | 5 | 51 | 4.73 |
| Babar Nazir | 6 | 4 | 0.49 |
| Cen Chen | 7 | 6 | 1.20 |
| Tse Chiang Howe | 8 | 4 | 0.49 |
| Zeng Zeng | 9 | 164 | 30.44 |
| Vijay Chandrasekhar | 10 | 191 | 22.83 |