Name
Abstract | ||
|---|---|---|
•We propose an efficient 3D residual neural network for brain tumor segmentation.•We propose a fusion loss function based on Dice and Cross-entropy.•We introduce a concise but effective post-processing method.•The evaluation is performed on the BRATS 2018 dataset.•The results demonstrate that our method outperforms the state-of-the-art approaches. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.eswa.2021.114566 | Expert Systems with Applications |
Keywords | DocType | Volume |
Brain tumor segmentation,3D convolutional neural network,Encoder-decoder,Efficiency,Lightweight,Residual block | Journal | 170 |
ISSN | Citations | PageRank |
0957-4174 | 2 | 0.44 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xinyu Zhou | 1 | 2 | 0.44 |
| Xuanya Li | 2 | 16 | 9.22 |
| Kai Hu | 3 | 46 | 8.62 |
| Yuan Zhang | 4 | 2 | 0.44 |
| Zhineng Chen | 5 | 192 | 25.29 |
| Xieping Gao | 6 | 100 | 24.43 |