Statements (74)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:challenges
|
| gptkbp:aims_to_improve |
diagnostic accuracy in pathology
|
| gptkbp:challenges |
data variability
|
| gptkbp:collaboration |
international
multidisciplinary teams |
| gptkbp:community_outreach |
to medical professionals
|
| gptkbp:encourages |
collaboration between academia and industry
|
| gptkbp:events |
gptkb:Camelyon17
|
| gptkbp:feedback |
participants
|
| gptkbp:has_a_focus_on |
digital pathology solutions
cancer diagnostics AI applications in healthcare |
| gptkbp:has_awards |
prizes for top performers
|
| gptkbp:has_collaborations_with |
various universities
|
| gptkbp:has_evaluation_metric |
area under the ROC curve (AUC)
|
| gptkbp:has_impact_on |
gptkb:medical_imaging
patient outcomes research funding cancer research clinical workflows |
| gptkbp:has_participants |
researchers and data scientists
|
| gptkbp:has_publications |
Camelyon16 paper
|
| gptkbp:has_website |
gptkb:camelyonchallenge.org
|
| gptkbp:held_annually |
gptkb:true
|
| gptkbp:hosted_by |
Kaggle platform
|
| https://www.w3.org/2000/01/rdf-schema#label |
Camelyon Challenge
|
| gptkbp:includes |
training and test datasets
|
| gptkbp:involved_technology |
gptkb:tools
|
| gptkbp:is_analyzed_in |
deep learning techniques
|
| gptkbp:is_associated_with |
gptkb:AI_technology
|
| gptkbp:is_collaborative_with |
gptkb:true
|
| gptkbp:is_documented_in |
gptkb:academic_journals
case studies technical reports white papers |
| gptkbp:is_evaluated_by |
performance metrics
clinical trials peer review process cross-validation machine learning metrics expert pathologists |
| gptkbp:is_influenced_by |
advancements in AI technology
|
| gptkbp:is_organized_by |
gptkb:University_Medical_Center_Utrecht
|
| gptkbp:is_part_of |
gptkb:health_services
AI competitions Grand Challenge in Medical Image Analysis |
| gptkbp:is_promoted_by |
gptkb:Publications
research grants scientific conferences |
| gptkbp:is_promoted_through |
social media
workshops |
| gptkbp:is_recognized_by |
medical imaging community
|
| gptkbp:is_related_to |
gptkb:machine_learning
computer vision data science pathology education |
| gptkbp:is_supported_by |
non-profit organizations
government grants NWO (Netherlands Organization for Scientific Research) academic societies industry sponsors |
| gptkbp:is_utilized_by |
research institutions
healthcare institutions |
| gptkbp:is_utilized_for |
training algorithms
algorithm benchmarking |
| gptkbp:objective |
evaluate algorithms for detecting metastases in lymph node sections
|
| gptkbp:outcome |
improved detection methods
|
| gptkbp:provides |
benchmark for algorithm performance
|
| gptkbp:provides_information_on |
Camelyon16 dataset
|
| gptkbp:reported_by |
gptkb:conference
|
| gptkbp:sponsorship |
various academic and industry partners
|
| gptkbp:started_in |
gptkb:2016
|
| gptkbp:bfsParent |
gptkb:camelyonchallenge.org
|
| gptkbp:bfsLayer |
8
|