Name
Abstract | ||
|---|---|---|
Aim: Early detection and correct diagnosis of lung cancer are the most important steps in improving patient outcome. This study aims to assess which deep learning models perform best in lung cancer diagnosis. Methods: Non-small cell lung carcinoma and small cell lung carcinoma biopsy specimens were consecutively obtained and stained. The specimen slides were diagnosed by two experienced pathologists (over 20 years). Several deep learning models were trained to discriminate cancer and non-cancer biopsies. Result: Deep learning models give reasonable AUC from 0.8810 to 0.9119. Conclusion: The deep learning analysis could help to speed up the detection process for the whole-slide image (WSI) and keep the comparable detection rate with human observer. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Computer Vision and Pattern Recognition | Lung cancer,Lung,Computer science,Computer-aided diagnosis,Biopsy,Artificial intelligence,Small Cell Lung Carcinoma,Deep learning,Radiology,Cancer,Carcinoma,Machine learning |
DocType | Volume | Citations |
Journal | abs/1803.05471 | 0 |
PageRank | References | Authors |
0.34 | 12 | 13 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhang Li | 1 | 0 | 0.68 |
| Zheyu Hu | 2 | 0 | 0.34 |
| Jiaolong Xu | 3 | 0 | 0.34 |
| Tao Tan | 4 | 46 | 10.25 |
| Hui Chen | 5 | 37 | 8.46 |
| Zhi Duan | 6 | 0 | 0.34 |
| Ping Liu | 7 | 14 | 7.39 |
| Jun Tang | 8 | 106 | 21.31 |
| G.-C. Guo | 9 | 0 | 2.37 |
| Quchang Ouyang | 10 | 0 | 0.34 |
| Yuling Tang | 11 | 1 | 0.69 |
| Geert Litjens | 12 | 996 | 50.79 |
| Qiang Li | 13 | 84 | 19.63 |