|
gptkbp:instance_of
|
gptkb:microprocessor
|
|
gptkbp:applies_to
|
3 D volumetric data
|
|
gptkbp:based_on
|
gptkb:Alumni_Association
|
|
gptkbp:can_be
|
tumors
organs
|
|
gptkbp:developed_by
|
gptkb:Ozan_Oktay
|
|
gptkbp:has_variants
|
gptkb:V-Net
gptkb:Attention_U-Net
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
3 DU-Net
|
|
gptkbp:improves
|
segmentation accuracy
|
|
gptkbp:input_output
|
segmentation masks
|
|
gptkbp:introduced
|
gptkb:2018
|
|
gptkbp:is_adopted_by
|
research institutions
healthcare companies
|
|
gptkbp:is_challenged_by
|
data scarcity
computational cost
training time
|
|
gptkbp:is_compared_to
|
gptkb:2_DU-Net
Fully Convolutional Networks
|
|
gptkbp:is_designed_for
|
medical image segmentation
|
|
gptkbp:is_documented_in
|
academic papers
technical reports
|
|
gptkbp:is_enhanced_by
|
hyperparameter tuning
data augmentation
transfer learning
|
|
gptkbp:is_evaluated_by
|
clinical trials
benchmark competitions
3 DCT scans
3 DMRI scans
Dice coefficient
Hausdorff distance
|
|
gptkbp:is_implemented_in
|
gptkb:Graphics_Processing_Unit
gptkb:Py_Torch
|
|
gptkbp:is_influenced_by
|
gptkb:Dense_Net
gptkb:Res_Net
|
|
gptkbp:is_integrated_with
|
clinical workflows
other AI models
|
|
gptkbp:is_optimized_for
|
3 D data processing
|
|
gptkbp:is_part_of
|
deep learning frameworks
|
|
gptkbp:is_popular_in
|
medical imaging community
|
|
gptkbp:is_promoted_by
|
workshops
AI conferences
|
|
gptkbp:is_scalable
|
larger datasets
|
|
gptkbp:is_supported_by
|
community forums
online tutorials
|
|
gptkbp:is_used_for
|
feature extraction
image-to-image translation
|
|
gptkbp:is_used_in
|
oncology
pathology
radiology
|
|
gptkbp:requires
|
GPU for training
|
|
gptkbp:security_features
|
missing data
noisy data
|
|
gptkbp:training
|
labeled datasets
|
|
gptkbp:uses
|
convolutional layers
skip connections
|
|
gptkbp:utilizes
|
upsampling
downsampling
|
|
gptkbp:bfsParent
|
gptkb:Alumni_Association
|
|
gptkbp:bfsLayer
|
3
|