Generalizable and explainable deep learning for brain MRI: a multi-cohort evaluation of 3D architectures for age and sex prediction.

Radhika Juglan¹, Marta Ligero¹, Zunamys I Carrero¹, Asier Rabasco Meneghetti¹, Tim Lenz¹, Leo Misera^1,2, Gregory Patrick Veldhuizen¹, Paul Kuntke³, Hagen H Kitzler³, Sven Nebelung^1,4, Daniel Truhn⁴, Jakob Nikolas Kather^5,6,7,8

¹Else Kroener Fresenius Center for Digital Health, Faculty of Medicine, TUD Dresden University of Technology, Dresden, Germany.

²Institute and Polyclinic for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Carl Gustav Carus Dresden, Technical University Dresden, Dresden, Germany.

³Institute of Diagnostic and Interventional Neuroradiology, Faculty of Medicine and Carl Gustav Carus University Hospital, Technische Universität Dresden, Dresden, Germany.

⁴Department of Diagnostic and Interventional Radiology, University Hospital Aachen, Aachen, Germany.

⁵Else Kroener Fresenius Center for Digital Health, Faculty of Medicine, TUD Dresden University of Technology, Dresden, Germany. kather.jn@tu-dresden.de.

⁶Department of Medicine I, Faculty of Medicine, TUD Dresden University of Technology, Dresden, Germany. kather.jn@tu-dresden.de.

⁷Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany. kather.jn@tu-dresden.de.

⁸Pathology & Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK. kather.jn@tu-dresden.de.

PubMed PMC DOI PDF

Abstract

Deep learning (DL) methods increasingly outperform classical approaches in brain MRI analysis, yet their generalizability across independent imaging cohorts remains insufficiently evaluated. Because age and sex are fundamental neurobiological factors influencing brain structure and disease risk, this study systematically compares three three-dimensional architectures-Simple Fully Convolutional Net…

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