Kendall Schmidt, Benjamin Bearce, Ken Chang, Laura Coombs, Keyvan Farahani, Marawan Elbatel, Kaouther Mouheb, Robert Marti, Ruipeng Zhang, Yao Zhang, Yanfeng Wang, Yaojun Hu, Haochao Ying, Yuyang Xu, Conrad Testagrose, Mutlu Demirer, Vikash Gupta, Ünal Akünal, Markus Bujotzek, Klaus H Maier-Hein, Yi Qin, Xiaomeng Li, Jayashree Kalpathy-Cramer, Holger R Roth
Fair evaluation of federated learning algorithms for automated breast density classification: The results of the 2022 ACR-NCI-NVIDIA federated learning challenge
Medical image analysis
executive function ef assessments often involve administration multiple tasks although factor analytic methods routinely used summarize performance across multiple tasks may optimal purpose introduce reliable component analysis rca strategy summarizing ef task performance demonstrate compares traditional methods participants included 259 children 45 sd 06 years old 55 female 41 white 35 black 14 hispanic 1 asian 1 american indian 8 one race kids activity learning study data collection occurred centerbased preschools involved direct child assessments ef motor math skills principal components analysis pca principal axis factor analysis fa rca methods used summarize childrens performance across battery six ef tasks whereas pca fa indicated single composite factor provided best representation ef task data rca indicated three composites justifiable rca composites moderately strongly correlated pca fa scores rs 3979 regression models indicated three approaches combining ef task scores explained proportion variance motor math skills outcomes though contributions individual composite factor scores varied results discussed respect rca differs commonly used tools data reduction psycinfo database record c 2023 apa rights reserved