An Interpretable Machine Learning Model with Deep Learning-based Imaging Biomarkers for Diagnosis of Alzheimer's Disease

no code implementations • 15 Aug 2023 • Wenjie Kang, Bo Li, 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, Esther E. Bron

However, some machine learning methods based on imaging data have poor interpretability because it is usually unclear how they make their decisions.

3D Object Classification Interpretable Machine Learning

Ten years of image analysis and machine learning competitions in dementia

no code implementations • 15 Dec 2021 • Esther E. Bron, Stefan Klein, Annika Reinke, Janne M. Papma, Lena Maier-Hein, Daniel C. Alexander, Neil P. Oxtoby

Key for increasing impact in this way are larger testing data sizes, which could be reached by sharing algorithms rather than data to exploit data that cannot be shared.

BIG-bench Machine Learning

Cross-Cohort Generalizability of Deep and Conventional Machine Learning for MRI-based Diagnosis and Prediction of Alzheimer's Disease

no code implementations • 16 Dec 2020 • Esther E. Bron, Stefan Klein, 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, Wiro J. Niessen, John C. van Swieten, Wiesje M. van der Flier, Inez H. G. B. Ramakers, Aad van der Lugt

For prediction in MCI, performances decreased for both SVM (0. 665) and CNN (0. 702).

Classification General Classification