Statements (32)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
gptkb:model
gptkb:convolutional_neural_network |
| gptkbp:application |
brain tumor segmentation
liver segmentation lung nodule segmentation |
| gptkbp:author |
gptkb:Thomas_Brox
gptkb:Olaf_Ronneberger gptkb:Özgün_Çiçek gptkb:Ahmed_Abdulkadir gptkb:Soeren_S._Lienkamp |
| gptkbp:basedOn |
gptkb:U-Net
|
| gptkbp:citation |
gptkb:3D_U-Net:_Learning_Dense_Volumetric_Segmentation_from_Sparse_Annotation
10.1007/978-3-319-46723-8_49 |
| gptkbp:designedFor |
volumetric image segmentation
|
| gptkbp:feature |
skip connections
encoder-decoder structure |
| gptkbp:firstPublished |
2016
|
| gptkbp:frameworks |
gptkb:TensorFlow
gptkb:PyTorch |
| https://www.w3.org/2000/01/rdf-schema#label |
3D U-Net
|
| gptkbp:input |
3D images
|
| gptkbp:inputDimension |
3D
|
| gptkbp:openSource |
gptkb:GitHub
|
| gptkbp:output |
segmentation maps
|
| gptkbp:outputDimension |
3D
|
| gptkbp:publishedIn |
gptkb:MICCAI_2016
|
| gptkbp:relatedTo |
gptkb:U-Net
gptkb:V-Net |
| gptkbp:usedIn |
medical image analysis
biomedical imaging |
| gptkbp:bfsParent |
gptkb:U-Net
|
| gptkbp:bfsLayer |
6
|