Name
Abstract | ||
|---|---|---|
•New loss for image segmentation that uses spatial information.•Noticeable improvement when combined with regional losses.•Stabilize the training of the neural network.•Trivially extensible to 3D, multi-class and balanced problems. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.media.2020.101851 | Medical Image Analysis |
Keywords | Field | DocType |
Boundary loss,Unbalanced data,Semantic segmentation,Deep learning,CNN | Softmax function,Convolutional neural network,Segmentation,Computer science,Network architecture,Algorithm,Metric (mathematics),Dice,Linear function,Computation | Journal |
Volume | ISSN | Citations |
67 | 1361-8415 | 8 |
PageRank | References | Authors |
0.50 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hoel Kervadec | 1 | 25 | 2.94 |
| Jihene Bouchtiba | 2 | 8 | 0.50 |
| Christian Desrosiers | 3 | 100 | 23.90 |
| E Granger | 4 | 62 | 7.10 |
| Dolz Jose | 5 | 91 | 16.76 |
| Ismail Ben Ayed | 6 | 678 | 52.28 |