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Improving Automated Pediatric Bone Age Estimation Using Ensembles of Models from the 2017 RSNA Machine Learning Challenge
55
Zitationen
5
Autoren
2019
Jahr
Abstract
Combining less-correlated, high-performing models resulted in better performance than naively combining the top-performing models. Machine learning competitions within radiology should be encouraged to spur development of heterogeneous models whose predictions can be combined to achieve optimal performance.© RSNA, 2019 <i>Supplemental material is available for this article.</i> See also the commentary by Siegel in this issue.
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