GL-ICNN: An End-To-End Interpretable Convolutional Neural Network for the Diagnosis and Prediction of Alzheimer's Disease

Wenjie Kang*, Lize Jiskoot, Peter De Deyn, Geert Biessels, Huiberdina Koek, Jurgen Claassen, Huub Middelkoop, Wiesje Flier, Willemijn J. Jansen, Stefan Klein, Esther Bron

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

Abstract

Deep learning methods based on Convolutional Neural Networks (CNNs) have shown large potential to improve early and accurate diagnosis of Alzheimer's disease (AD) dementia based on imaging data. However, these methods have yet to be widely adopted in clinical practice, possibly due to the limited interpretability of deep learning models. The Explainable Boosting Machine (EBM) is a glass-box model but cannot learn features directly from input imaging data. In this study, we propose a novel interpretable model that combines CNNs and EBMs for the diagnosis and prediction of AD. We develop an innovative training strategy that alternatingly trains the CNN component as a feature extractor and the EBM component as the output block to form an end-to-end model. The model takes imaging data as input and provides both predictions and interpretable feature importance measures. We validated the proposed model on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and the Health-RI Parelsnoer Neurode- generative Diseases Biobank (PND) as an external testing set. The proposed model achieved an area-under-the-curve (AUC) of 0.956 for AD and control classification, and 0.694 for the prediction of conversion of mild cognitive impairment (MCI) to AD on the ADNI cohort. The proposed model is a glass- box model that achieves a comparable performance with other state-of-the-art black-box models. Our code is available at: https://anonymous.4open.science/r/GL-ICNN.
Original languageEnglish
Title of host publicationISBI 2025 - 2025 IEEE 22nd International Symposium on Biomedical Imaging, Proceedings
PublisherIEEE Computer Society
ISBN (Electronic)9798331520526
DOIs
Publication statusPublished - 1 Jan 2025
Event22nd IEEE International Symposium on Biomedical Imaging - Houston, United States
Duration: 14 Apr 202517 Apr 2025
Conference number: 22
https://biomedicalimaging.org/2025/

Publication series

SeriesProceedings - International Symposium on Biomedical Imaging
ISSN1945-7928

Conference

Conference22nd IEEE International Symposium on Biomedical Imaging
Abbreviated titleISBI 2025
Country/TerritoryUnited States
CityHouston
Period14/04/2517/04/25
Internet address

Keywords

  • Alzheimer's disease
  • Convolutional neural network
  • Deep learning
  • Explainable artificial intelligence
  • Explainable boosting machine
  • MRI

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