DeepComBat: A Statistically Motivated, Hyperparameter-Robust, Deep Learning Approach to Harmonization of Neuroimaging Data.

Hu, Fengling; Lucas, Alfredo; Chen, Andrew A; Coleman, Kyle; Horng, Hannah; Ng, Raymond W S; Tustison, Nicholas J; Davis, Kathryn A; Shou, Haochang; Li, Mingyao; Shinohara, Russell T

Hu, Fengling; Lucas, Alfredo; Chen, Andrew A; Coleman, Kyle; Horng, Hannah; Ng, Raymond W S; Tustison, Nicholas J; Davis, Kathryn A; Shou, Haochang; Li, Mingyao; Shinohara, Russell T.

Afiliação

Hu F; Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania.
Lucas A; Center for Neuroengineering and Therapeutics, Department of Engineering, University of Pennsylvania.
Chen AA; Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania.
Coleman K; Statistical Center for Single-Cell and Spatial Genomics, Perelman School of Medicine, University of Pennsylvania.
Horng H; Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania.
Ng RWS; Perelman School of Medicine, University of Pennsylvania.
Tustison NJ; Department of Radiology and Medical Imaging, University of Virginia.
Davis KA; Center for Neuroengineering and Therapeutics, Department of Engineering, University of Pennsylvania.
Shou H; Department of Neurology, Perelman School of Medicine, University of Pennsylvania.
Li M; Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania.
Shinohara RT; Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine.

bioRxiv ; 2023 Apr 24.

Article em En | MEDLINE | ID: mdl-37163042

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: BioRxiv Ano de publicação: 2023 Tipo de documento: Article

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