Name
Title | ||
|---|---|---|
Hierarchical combinatorial deep learning architecture for pancreas segmentation of medical computed tomography cancer images. |
Abstract | ||
|---|---|---|
The results of our experiments show that our advanced model works better than previous networks in our dataset. On the other words, it has better ability in catching detailed contexture information. Therefore, our new single object segmentation model has practical meaning in computational automatic diagnosis. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1186/s12918-018-0572-z | BMC Systems Biology |
Keywords | Field | DocType |
Multi-layer up-sampling structure,Pancreas segmentation,Single object segmentation | Architecture,Pattern recognition,Biology,Segmentation,Systems biology,Computed tomography,Artificial intelligence,Bioinformatics,Deep learning,Pancreas,Cancer | Journal |
Volume | Issue | Citations |
12 | 4 | 2 |
PageRank | References | Authors |
0.36 | 17 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Min Fu | 1 | 2 | 0.36 |
| Wenming Wu | 2 | 2 | 0.36 |
| Xiafei Hong | 3 | 2 | 0.36 |
| Qiuhua Liu | 4 | 2 | 0.36 |
| Jialin Jiang | 5 | 2 | 0.69 |
| Yaobin Ou | 6 | 2 | 0.36 |
| Yupei Zhao | 7 | 2 | 0.69 |
| Xinqi Gong | 8 | 6 | 2.80 |