Cross-Cohort Generalizability of Deep and Conventional Machine Learning for MRI-based Diagnosis and Prediction of Alzheimer's Disease. - Esther E. Bron; Stefan Klein 001; Janne M. Papma; Lize C. Jiskoot; Vikram Venkatraghavan; Jara Linders; Pauline Aalten; Peter Paul De Deyn; Geert Jan Biessels; Jurgen A. H. R. Claassen; Huub A. M. Middelkoop; Marion Smits; et al. (CoRR, 2020)

An Interpretable Machine Learning Model with Deep Learning-Based Imaging Biomarkers for Diagnosis of Alzheimer's Disease. - Wenjie Kang; Bo Li 088; Janne M. Papma; Lize C. Jiskoot; Peter Paul De Deyn; Geert Jan Biessels; Jurgen A. H. R. Claassen; Huub A. M. Middelkoop; Wiesje M. van der Flier; Inez H. G. B. Ramakers; Stefan Klein 001; Esther E. Bron (ISIC/Care-AI/MedAGI/DeCaF@MICCAI, 2023)

Identifying latent disease factors differently expressed in patient subgroups using group factor analysis. - Fabio S. Ferreira; John Ashburner; Arabella Bouzigues; Chatrin Suksasilp; Lucy L. Russell; Phoebe H. Foster; Eve Ferry-Bolder; John C. van Swieten; Lize C. Jiskoot; Harro Seelaar; Raquel Sánchez-Valle; Robert Laforce; et al. (CoRR, 2024)

GL-ICNN: An End-To-End Interpretable Convolutional Neural Network for the Diagnosis and Prediction of Alzheimer's Disease. - Wenjie Kang; Lize C. Jiskoot; Peter Paul De Deyn; Geert Jan Biessels; Huiberdina Koek; Jurgen A. H. R. Claassen; Huub A. M. Middelkoop; Wiesje M. van der Flier; Willemijn Jansen; Stefan Klein 001; Esther Bron (ISBI, 2025)