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gptkbp:instance_of
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gptkb:television_channel
|
|
gptkbp:application
|
Biomedical Image Analysis
|
|
gptkbp:architectural_style
|
Encoder-Decoder
|
|
gptkbp:based_on
|
Fully Convolutional Networks
|
|
gptkbp:can_be_used_with
|
gptkb:streaming_service
Data Augmentation
|
|
gptkbp:content_type
|
Convolutional Layers
Pooling Layers
Up-sampling Layers
|
|
gptkbp:developed_by
|
gptkb:Olaf_Ronneberger
|
|
gptkbp:established
|
Re LU
|
|
gptkbp:has_variants
|
gptkb:3_DU-Net
gptkb:Attention_U-Net
gptkb:Nested_U-Net
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
2 DU-Net
|
|
gptkbp:improves
|
Localization of Structures
|
|
gptkbp:input_output
|
Segmentation Maps
2 D Images
|
|
gptkbp:is_adopted_by
|
Research Institutions
Healthcare Industry
Tech Companies
|
|
gptkbp:is_designed_for
|
Image Segmentation
|
|
gptkbp:is_evaluated_by
|
Accuracy
Precision
Recall
Io U (Intersection over Union)
|
|
gptkbp:is_implemented_in
|
gptkb:Graphics_Processing_Unit
gptkb:Py_Torch
|
|
gptkbp:is_influenced_by
|
gptkb:FCN_(Fully_Convolutional_Network)
gptkb:Alumni_Association
gptkb:Seg_Net
|
|
gptkbp:is_known_for
|
High Performance
Flexibility in Architecture
Robustness to Overfitting
|
|
gptkbp:is_part_of
|
Deep Learning Frameworks
|
|
gptkbp:is_popular_in
|
Medical Imaging
|
|
gptkbp:is_supported_by
|
Community Contributions
Open Source Implementations
|
|
gptkbp:is_used_for
|
Cell Segmentation
Organ Segmentation
Tumor Segmentation
|
|
gptkbp:is_used_in
|
Competitions
Research Papers
|
|
gptkbp:losses
|
Dice Loss
Binary Cross-Entropy Loss
|
|
gptkbp:published_year
|
gptkb:2015
|
|
gptkbp:requires
|
GPU for Training
|
|
gptkbp:training
|
Annotated Datasets
Limited Data
|
|
gptkbp:uses
|
Skip Connections
|
|
gptkbp:bfsParent
|
gptkb:3_DU-Net
|
|
gptkbp:bfsLayer
|
4
|