Name
Abstract | ||
|---|---|---|
Lung cancer is the leading cause of cancer deaths. Low-dose computed tomography (CT)screening has been shown to significantly reduce lung cancer mortality but suffers from a high false positive rate that leads to unnecessary diagnostic procedures. The development of deep learning techniques has the potential to help improve lung cancer screening technology. Here we present the algorithm, DeepScree... |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TCBB.2020.3027744 | IEEE/ACM Transactions on Computational Biology and Bioinformatics |
Keywords | DocType | Volume |
Cancer,Computed tomography,Lung,Biological system modeling,Training,Testing,Machine learning | Journal | 19 |
Issue | ISSN | Citations |
2 | 1545-5963 | 0 |
PageRank | References | Authors |
0.34 | 0 | 10 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jason L Causey | 1 | 0 | 0.34 |
| Keyu Li | 2 | 4 | 5.22 |
| Xianghao Chen | 3 | 0 | 0.34 |
| Dong Wei | 4 | 10 | 2.94 |
| Karl Walker | 5 | 0 | 1.01 |
| Jake A Qualls | 6 | 0 | 0.34 |
| Jonathan Stubblefield | 7 | 0 | 0.34 |
| Jason H. Moore | 8 | 1223 | 159.43 |
| Yuanfang Guan | 9 | 0 | 0.34 |
| Xiuzhen Huang | 10 | 442 | 26.16 |