U-Net: Convolutional Networks for Biomedical Image Segmentation
GPTKB entity
Statements (23)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
gptkb:academic_journal
|
| gptkbp:application |
medical image analysis
cell segmentation tissue segmentation |
| gptkbp:architecture |
encoder-decoder
|
| gptkbp:author |
gptkb:Thomas_Brox
gptkb:Olaf_Ronneberger gptkb:Philipp_Fischer |
| gptkbp:citation |
highly cited
|
| gptkbp:field |
biomedical image segmentation
|
| https://www.w3.org/2000/01/rdf-schema#label |
U-Net: Convolutional Networks for Biomedical Image Segmentation
|
| gptkbp:impact |
influential in deep learning for medical imaging
|
| gptkbp:language |
English
|
| gptkbp:method |
gptkb:convolutional_neural_network
|
| gptkbp:notableFor |
skip connections
|
| gptkbp:openAccess |
true
|
| gptkbp:proposedBy |
gptkb:U-Net_architecture
|
| gptkbp:publicationYear |
2015
|
| gptkbp:publishedIn |
gptkb:Medical_Image_Computing_and_Computer-Assisted_Intervention_(MICCAI)
|
| gptkbp:trainer |
gptkb:ISBI_cell_tracking_challenge
|
| gptkbp:url |
https://arxiv.org/abs/1505.04597
|
| gptkbp:bfsParent |
gptkb:U-Net
|
| gptkbp:bfsLayer |
6
|