Correction to: Scientific Reports https://doi.org/10.1038/s41598-023-33920-7, published online 27 April 2023
The original version of this Article contained errors in the Materials and methods section, under the subheading ‘Software and hardware specification’, where links to used resources were omitted and the present link was incorrect.
“The sMRI preprocessing was done through the SPM12 v7771 software. All further steps used Python 3.8.5 and Tensorflow 2.4.0. The machine learning experiments were performed on an NVIDIA DGX-2 server, within a Docker virtual machine containing 4 CPUs @2.7Ghz and 1 GPU TESLA V100-SXM3-32 GB. All source codes are available at Github (https://docs.nvidia.com/deeplearning/frameworks/tensorflow-release-notes/rel_21-03.html).”
now reads:
“The sMRI preprocessing was done through the SPM12 v7771 software (https://www.fil.ion.ucl.ac.uk/spm/software/spm12/). All further steps used Python 3.8.5 and Tensorflow 2.4.0 (https://docs.nvidia.com/deeplearning/frameworks/tensorflow-release-notes/rel_21-03.html). The machine learning experiments were performed on an NVIDIA DGX-2 server, within a Docker virtual machine containing 4 CPUs @2.7Ghz and 1 GPU TESLA V100-SXM3-32 GB. All source codes are available at Github (https://github.com/SergioLeonardoMendes/3dcnn_smri_generalization).”
The original Article has been corrected.
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Mendes, S.L., Pinaya, W.H.L., Pan, P.M. et al. Publisher Correction: Generalizability of 3D CNN models for age estimation in diverse youth populations using structural MRI. Sci Rep 13, 8025 (2023). https://doi.org/10.1038/s41598-023-35101-y
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DOI: https://doi.org/10.1038/s41598-023-35101-y
