Score breakdown

U-Net: Convolutional Networks for Biomedical Image Segmentation

paper-0072 · paper · 2015

Olaf Ronneberger, Philipp Fischer, Thomas Brox

The default segmentation architecture, far beyond medicine.

Academic, score 0.0253

MetricStatusValueNorm.WeightContributionSourceConfidenceProvenance
citation_countpresent88975.00.4005080.50.200254OpenAlexhighlink
library_holdingsmissingrecorded as missing, penalized by rule, never imputed−0.1recorded as missing; penalized by rule, never imputed
readership_persistencepresent15.01.00.050.05OpenAlexmediumlink
syllabus_adoptionsmissingrecorded as missing, penalized by rule, never imputed−0.125recorded as missing; penalized by rule, never imputed

Broad Influence, score 0.2801

MetricStatusValueNorm.WeightContributionSourceConfidenceProvenance
citation_countpresent88975.00.4005080.20.080102OpenAlexhighlink
library_holdingsmissingrecorded as missing, penalized by rule, never imputed−0.125recorded as missing; penalized by rule, never imputed
readership_persistencepresent15.01.00.40.4OpenAlexmediumlink
syllabus_adoptionsmissingrecorded as missing, penalized by rule, never imputed−0.075recorded as missing; penalized by rule, never imputed

Governance Practitioner, score -0.1249

MetricStatusValueNorm.WeightContributionSourceConfidenceProvenance
citation_countpresent88975.00.4005080.250.100127OpenAlexhighlink
library_holdingsmissingrecorded as missing, penalized by rule, never imputed−0.15recorded as missing; penalized by rule, never imputed
readership_persistencepresent15.01.00.10.1OpenAlexmediumlink
syllabus_adoptionsmissingrecorded as missing, penalized by rule, never imputed−0.175recorded as missing; penalized by rule, never imputed

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