TY - GEN
T1 - An Interpretable Machine Learning Model with Deep Learning-Based Imaging Biomarkers for Diagnosis of Alzheimer’s Disease
AU - Kang, Wenjie
AU - Li, Bo
AU - Papma, Janne M.
AU - Jiskoot, Lize C.
AU - Deyn, Peter Paul De
AU - Biessels, Geert Jan
AU - Claassen, Jurgen A.H.R.
AU - Middelkoop, Huub A.M.
AU - Flier, Wiesje M.van der
AU - Ramakers, Inez H.G.B.
AU - Klein, Stefan
AU - Bron, Esther E.
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Alzheimer’s disease
KW - Convolutional neural network
KW - Explainable boosting machine
KW - Interpretable AI
KW - MRI
UR - http://www.scopus.com/inward/record.url?scp=85180620813&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-47401-9_7
DO - 10.1007/978-3-031-47401-9_7
M3 - Conference contribution
AN - SCOPUS:85180620813
SN - 9783031474002
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 69
EP - 78
BT - Medical 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
A2 - Celebi, M. Emre
A2 - Salekin, Md Sirajus
A2 - Kim, Hyunwoo
A2 - Albarqouni, Shadi
PB - Springer Science and Business Media Deutschland GmbH
T2 - 26th International Conference on Medical Image Computing and Computer-Assisted Intervention , MICCAI 2023
Y2 - 8 October 2023 through 12 October 2023
ER -