Fig. 1: RMSE prediction errors for different model permutations, model size d = 4284. | npj Digital Medicine

Fig. 1: RMSE prediction errors for different model permutations, model size d = 4284.

From: 3D convolutional deep learning for nonlinear estimation of body composition from whole body morphology

Fig. 1

Every column represents exactly one modification to the pipeline parameters from the previous column (i.e., OLS to GPR) excluding the baseline column. RMSE is shown in kg except for PFAT, which is expressed as a percentage. Baseline is GPR prediction from known priors [height, weight, age] only. PCA_OLS is linear regression from linear PCA features. This result was identical to OLS from the 3DO vertex coordinates. PCA_GPR is nonlinear GPR prediction from linear PCA features. Mesh_GPR is GPR prediction from 3DO vertex coordinates with no feature extraction. 3DAE_GPR_X is the most accurate GPR model trained from any feature layer of the 3DAE network with the feature dimension indicated as X.

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