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

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, Parelsnoer Neurodegenerative Diseases study group

*Corresponding author for this work

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

Abstract

Machine learning methods have shown large potential for the automatic early diagnosis of Alzheimer’s Disease (AD). However, some machine learning methods based on imaging data have poor interpretability because it is usually unclear how they make their decisions. Explainable Boosting Machines (EBMs) are interpretable machine learning models based on the statistical framework of generalized additive modeling, but have so far only been used for tabular data. Therefore, we propose a framework that combines the strength of EBM with high-dimensional imaging data using deep learning-based feature extraction. The proposed framework is interpretable because it provides the importance of each feature. We validated the proposed framework on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, achieving accuracy of 0.883 and area-under-the-curve (AUC) of 0.970 on AD and control classification. Furthermore, we validated the proposed framework on an external testing set, achieving accuracy of 0.778 and AUC of 0.887 on AD and subjective cognitive decline (SCD) classification. The proposed framework significantly outperformed an EBM model using volume biomarkers instead of deep learning-based features, as well as an end-to-end convolutional neural network (CNN) with optimized architecture.
Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops - ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Proceedings
EditorsM. Emre Celebi, Md Sirajus Salekin, Hyunwoo Kim, Shadi Albarqouni
PublisherSpringer Verlag
Pages69-78
Number of pages10
Volume14393
ISBN (Print)9783031474002
DOIs
Publication statusPublished - 1 Jan 2023
Event26th International Conference on Medical Image Computing and Computer Assisted Intervention - Vancouver, Canada
Duration: 8 Oct 202312 Oct 2023
Conference number: 26
https://conferences.miccai.org/2023/en/

Publication series

SeriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14393
ISSN0302-9743

Conference

Conference26th International Conference on Medical Image Computing and Computer Assisted Intervention
Abbreviated titleMICCAI 2023
Country/TerritoryCanada
CityVancouver
Period8/10/2312/10/23
Internet address

Keywords

  • Alzheimer’s disease
  • Convolutional neural network
  • Explainable boosting machine
  • Interpretable AI
  • MRI

Fingerprint

Dive into the research topics of 'An Interpretable Machine Learning Model with Deep Learning-Based Imaging Biomarkers for Diagnosis of Alzheimer’s Disease'. Together they form a unique fingerprint.

Cite this