Name
Title | ||
|---|---|---|
Nabla-net: A Deep Dag-Like Convolutional Architecture for Biomedical Image Segmentation. |
Abstract | ||
|---|---|---|
Biomedical image segmentation requires both voxel-level information and global context. We report on a deep convolutional architecture which combines a fully-convolutional network for local features and an encoder-decoder network in which convolutional layers and max-pooling compute high-level features, which are then upsampled to the resolution of the initial image using further convolutional layers and tied unpooling. We apply the method to segmenting multiple sclerosis lesions and gliomas. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-55524-9_12 | Lecture Notes in Computer Science |
Field | DocType | Volume |
Computer vision,Architecture,Nabla symbol,Scale-space segmentation,Market segmentation,Pattern recognition,Computer science,Convolutional neural network,Image segmentation,Artificial intelligence,Lesion segmentation | Conference | 10154 |
ISSN | Citations | PageRank |
0302-9743 | 1 | 0.35 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Richard McKinley | 1 | 7 | 3.60 |
| Rik Wepfer | 2 | 1 | 0.35 |
| tom gundersen | 3 | 55 | 4.48 |
| Franca Wagner | 4 | 5 | 1.89 |
| Chan, T. | 5 | 3 | 1.77 |
| Roland Wiest | 6 | 344 | 22.73 |
| Mauricio Reyes | 7 | 73 | 13.74 |