Name
Abstract | ||
|---|---|---|
•A novel convolutional neural network termed ‘Super U-Net” for medical image segmentation.•A fusion upsampling module that recalibrates feature maps prior to concatenation.•A dynamic receptive field module that allows the network to determine the correct kernel size for the current segmentation task.•Comparative experiments were performed on the super U-Net, seven U-Net variants, and two non-U-Net segmentation architectures on the DRIVE, CHASE DB1, Kvasir-SEG, and ISIC 2017 datasets. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.patcog.2022.108669 | Pattern Recognition |
Keywords | DocType | Volume |
Image segmentation,U-Net,Dynamic receptive field,Fusion upsampling | Journal | 128 |
ISSN | Citations | PageRank |
0031-3203 | 0 | 0.34 |
References | Authors | |
3 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cameron A. Beeche | 1 | 0 | 0.34 |
| Jatin P. Singh | 2 | 0 | 0.34 |
| Joseph K. Leader | 3 | 0 | 0.34 |
| Naciye S. Gezer | 4 | 0 | 0.34 |
| Amechi P. Oruwari | 5 | 0 | 0.34 |
| Kunal K. Dansingani | 6 | 0 | 0.34 |
| Jay Chhablani | 7 | 0 | 0.34 |
| Jiantao Pu | 8 | 277 | 23.12 |