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gptkbp:instance_of
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gptkb:challenges
|
|
gptkbp:aims_to
|
detect breast cancer
|
|
gptkbp:aims_to_improve
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diagnostic accuracy
|
|
gptkbp:contributes_to
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gptkb:AI_technology
|
|
gptkbp:encourages
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machine learning solutions
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|
gptkbp:has_a_focus_on
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gptkb:medical_imaging
|
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gptkbp:has_a_website
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gptkb:camelyonchallenge.org
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|
gptkbp:has_multiple_phases
|
training and testing
|
|
gptkbp:has_participants
|
around the world
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|
https://www.w3.org/2000/01/rdf-schema#label
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The Camelyon Challenge
|
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gptkbp:involves
|
pathology images
|
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gptkbp:is_associated_with
|
breast cancer research
|
|
gptkbp:is_evaluated_by
|
performance metrics
|
|
gptkbp:is_organized_by
|
gptkb:University_Medical_Center_Utrecht
|
|
gptkbp:is_part_of
|
gptkb:health_services
medical competitions
|
|
gptkbp:is_recognized_by
|
gptkb:scientific_community
|
|
gptkbp:is_supported_by
|
various institutions
|
|
gptkbp:launched_in
|
gptkb:2016
|
|
gptkbp:promotes
|
collaboration among researchers
|
|
gptkbp:provides
|
benchmark datasets
|
|
gptkbp:result
|
gptkb:academic_journals
|
|
gptkbp:utilizes
|
deep learning techniques
|
|
gptkbp:bfsParent
|
gptkb:Camelyon16
|
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gptkbp:bfsLayer
|
6
|