Name
Title | ||
|---|---|---|
Learning From Multiple Datasets With Heterogeneous and Partial Labels for Universal Lesion Detection in CT |
Abstract | ||
|---|---|---|
Large-scale datasets with high-quality labels are desired for training accurate deep learning models. However, due to the annotation cost, datasets in medical imaging are often either partially-labeled or small. For example, DeepLesion is such a large-scale CT image dataset with lesions of various types, but it also has many unlabeled lesions (missing annotations). When training a lesion detector ... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TMI.2020.3047598 | IEEE Transactions on Medical Imaging |
Keywords | DocType | Volume |
Lesions,Annotations,Training,Lenses,Proposals,Computed tomography,Task analysis | Journal | 40 |
Issue | ISSN | Citations |
10 | 0278-0062 | 0 |
PageRank | References | Authors |
0.34 | 0 | 10 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ke Yan | 1 | 42 | 9.14 |
| Jinzheng Cai | 2 | 71 | 10.95 |
| Youjing Zheng | 3 | 0 | 0.68 |
| Adam P. Harrison | 4 | 101 | 17.06 |
| Dakai Jin | 5 | 53 | 11.67 |
| Youbao Tang | 6 | 107 | 12.00 |
| Yu-Xing Tang | 7 | 0 | 1.69 |
| Lingyun Huang | 8 | 0 | 3.04 |
| Jing Xiao | 9 | 7 | 5.78 |
| Le Lu | 10 | 1297 | 86.78 |