V-Net

GPTKB entity

Statements (54)
Predicate Object
gptkbp:instance_of gptkb:microprocessor
gptkbp:adapted_into different modalities
gptkbp:applies_to 3 D convolutions
gptkbp:architectural_style encoder-decoder
gptkbp:based_on fully convolutional networks
gptkbp:collaborated_with research institutions
gptkbp:competes_with gptkb:Alumni_Association
gptkbp:developed_by gptkb:Microsoft_Research
gptkbp:has_achievements state-of-the-art results
gptkbp:has_programs neurology
oncology
radiology
gptkbp:has_variants gptkb:V-Net++
https://www.w3.org/2000/01/rdf-schema#label V-Net
gptkbp:improves medical image analysis
gptkbp:input_output 3 D volumetric data
segmentation maps
gptkbp:introduced gptkb:2016
gptkbp:is_cited_in research articles
gptkbp:is_documented_in academic papers
gptkbp:is_enhanced_by post-processing techniques
gptkbp:is_evaluated_by traditional methods
benchmark datasets
clinical applications
medical imaging datasets
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 other AI systems
gptkbp:is_known_for high accuracy
robustness to noise
gptkbp:is_optimized_for GPU processing
gptkbp:is_part_of deep learning frameworks
medical imaging pipelines
gptkbp:is_popular_in computer vision community
gptkbp:is_recognized_for innovation in AI
gptkbp:is_scalable large datasets
gptkbp:is_supported_by open-source community
gptkbp:is_tested_for synthetic data
real-world data
gptkbp:is_used_for 3 D image segmentation
gptkbp:is_used_in surgical planning
image-guided therapy
automated diagnosis
gptkbp:is_utilized_in gptkb:Insurance_Company
gptkbp:performance other segmentation networks
gptkbp:requires large annotated datasets
gptkbp:suitable_for real-time applications
gptkbp:training transfer learning
data augmentation techniques
gptkbp:utilizes skip connections
gptkbp:bfsParent gptkb:3_DU-Net
gptkbp:bfsLayer 4