Name
Abstract | ||
|---|---|---|
•A novel densely connection inception convolutional neural network based on U-Net architecture is proposed for medical image segmentation tasks.•The modified Inception-res module combining inception architecture and residual connection is used to make the proposed network deeper and wider.•The densely connection is used in the network to avoid gradient vanishing or redundant computation during network training.•Apply the proposed network to CT and MRI medical segmentation tasks and make evaluation with other segmentation methods. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.cmpb.2020.105395 | Computer Methods and Programs in Biomedicine |
Keywords | DocType | Volume |
Deep learning,Medical image segmentation,U-net,GoogLeNet,DenseNet | Journal | 192 |
ISSN | Citations | PageRank |
0169-2607 | 3 | 0.39 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ziang Zhang | 1 | 3 | 0.39 |
| Chengdong Wu | 2 | 250 | 46.36 |
| Sonya Coleman | 3 | 216 | 36.84 |
| Dermot Kerr | 4 | 50 | 13.84 |