Name
Title | ||
|---|---|---|
RefineU-Net: Improved U-Net with Progressive Global Feedbacks and Residual Attention Guided Local Refinement for Medical Image Segmentation |
Abstract | ||
|---|---|---|
•A novel FCN architecture called RefineU-Net is proposed to improve the performance of U-Net on medical image segmentation.•A global refinement module is proposed to generate intermediate layers in skip connections to alleviate semantic gap problems.•A local refinement module is proposed using a residual attention gate to generate discriminative attentive features.•The proposed RefineU-Net outperforms multiple U-Net based methods on four public datasets of medical segmentation. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.patrec.2020.07.013 | Pattern Recognition Letters |
Keywords | DocType | Volume |
U-Net,Medical image segmentation,Progressive global feedbacks,Local refinement,Residual attention gate | Journal | 138 |
ISSN | Citations | PageRank |
0167-8655 | 1 | 0.36 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dongyun Lin | 1 | 1 | 3.06 |
| Yiqun Li | 2 | 236 | 16.27 |
| Tin Lay Nwe | 3 | 1 | 2.72 |
| Sheng Dong | 4 | 1 | 2.72 |
| Zaw Min Oo | 5 | 1 | 0.36 |