[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120917-en":3,"doc-seo-120917-105":30,"detail-sidebar-cat-0-en-105":83},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120917,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",7,"Healthcare","An Interpretable Machine Learning Model with Deep Learning-Based Imaging Biomarkers for Diagnosis of Alzheimer’s Disease","Machine learning methods hold promise for automatic early diagnosis of Alzheimer’s Disease, yet imaging-based approaches often lack interpretability, leaving the decision rationale unclear. The work introduces a framework that combines Explainable Boosting Machines with deep learning-based feature extraction to handle high-dimensional imaging data while preserving feature-level importance. Validation on the ADNI dataset reports accuracy of 0.883 and AUC of 0.970, and external testing yields accuracy of 0.778 and AUC of 0.887.","University of Groningen  \nAn Interpretable Machine Learning Model with Deep Learning  \nfor the Alzheimer’s Disease Neuroimaging Initiative, on behalf of the Parelsnoer Neurodegenerative Diseases study group; Kang, Wenjie; Li, Bo; Papma, Janne M. ; Jiskoot, Lize C. ; Deyn, Peter Paul De; Biessels, Geert Jan; Claassen, Jurgen A. H. R. ; Middelkoop,  \nHuub A. M. ; Flier, Wiesje M.van der Published in:  \nMedical 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  \nDOI:  \n10. 1007/978-3-031-47401-9_7  \nIMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document version below.  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nPublication date: 2023  \nLink to publication in University of Groningen/UMCG research database  \nCitation for published version (APA):  \nfor the Alzheimer’s Disease Neuroimaging Initiative, on behalf of the Parelsnoer Neurodegenerative Diseases study group, Kang, W. , Li, B. , Papma, J. M. , Jiskoot, L. C. , Deyn, P. P. D. , Biessels, G. J. , Claassen, J. A. H. R. , Middelkoop, H. A. M. , Flier, W. M. V. D. , Ramakers, I. H. G. B. , Klein, S. , & Bron, E.  \nE. (2023) . An Interpretable Machine Learning Model with Deep Learning: Based Imaging Biomarkers for Diagnosis of Alzheimer’s Disease. In M. E. Celebi, M. S. Salekin, H. Kim, & S. Albarqouni (Eds.), 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 (pp. 69- 78) . (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14393) . Springer Science and Business Media Deutschland GmbH. [https://doi.org/10.1007/978-3-031-47401-9_7](https://doi.org/10.1007/978-3-031-47401-9_7)  \nCopyright  \nOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons) .  \nThe publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license. More information can be found on the University of Groningen website: [https://www.rug.nl/library/open-access/self-archiving-pure/taverne](https://www.rug.nl/library/open-access/self-archiving-pure/taverne)amendment.  \nTake-down policy  \nIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.  \nDownloaded from the University of Groningen/U MCG research database (Pure): [http://www.rug. nl/research/portal. For technical reasons the](http://www.rug. nl/research/portal. For technical reasons the)[ ](http://www.rug. nl/research/portal. For technical reasons the)[number of authors shown on this cover page is limited to 10 maximum.](number of authors shown on this cover page is limited to 10 maximum.)  \nDownload date: 02-08-2026  \nAn Interpretable Machine Learning Model with Deep Learning-Based Imaging Biomarkers for Diagnosis of Alzheimer’s Disease  \nWenjie Kang 1(B), Bo Li 1 , Janne M. Papma2 , Lize C. Jiskoot2 , Peter Paul De Deyn3 , Geert Jan Biessels4 , Jurgen A.H. R. Claassen5 , Huub A.M. Middelkoop6,7 , Wiesje M. van der Flier8 , Inez H.G.B. Ramakers9 , Stefan Klein 1 , Esther E. Bron1 ,  \nand for the Alzheimer’s Disease Neuroimaging Initiative, and on behalf of the Parelsnoer Neurodegenerative Diseases study group  \n1 Department of Radiology and Nuclear Medicine, Erasmus MC,  \nRotterdam, The Netherlands  \n[w.kang@erasmusmc.nl](w.kang@erasmusmc.nl)  \n2 Department of Neurology, Erasmus MC, Rotterdam, The Netherlands  \n3 Department of Neurolog","cbCaihdNh6bG8Drp","https://ap.wps.com/l/cbCaihdNh6bG8Drp","pdf",1082221,1,11,"English","en",105,"# Abstract\n## Problem: Interpretability in imaging-based machine learning\n## Proposed approach: EBM with deep learning-based feature extraction\n## Evaluation: ADNI results and external testing performance","[{\"question\":\"What performance was achieved on the ADNI dataset and on an external test set?\",\"answer\":\"On ADNI, accuracy reached 0.883 with AUC 0.970; on an external set, accuracy was 0.778 with AUC 0.887.\"}]","An Interpretable Machine Learning Model with Deep Learning-Based Imaging Biomarkers for Diagnosis of Alzheimer’s Disease | PDF",1785732675,28,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":78,"head_meta":80,"extra_data":82,"updated_unix":28},"an-interpretable-machine-learning-model-with-deep-learning-based-imaging-biomarkers-for-diagnosis-of-alzheimers-disease","",{"@graph":36,"@context":77},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/an-interpretable-machine-learning-model-with-deep-learning-based-imaging-biomarkers-for-diagnosis-of-alzheimers-disease/120917/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71],{"name":72,"@type":73,"acceptedAnswer":74},"What performance was achieved on the ADNI dataset and on an external test set?","Question",{"text":75,"@type":76},"On ADNI, accuracy reached 0.883 with AUC 0.970; on an external set, accuracy was 0.778 with AUC 0.887.","Answer","https://schema.org",{"og:url":52,"og:type":79,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":81,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":84},[85,89,93,97,102,107,110,115,120,123,127],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":86,"show_sort_weight":87,"slug":88},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":90,"show_sort_weight":91,"slug":92},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Exam",70,"exam",{"id":98,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},5,"Comic",60,"comic",{"id":103,"doc_module":4,"doc_module_name":46,"category_name":104,"show_sort_weight":105,"slug":106},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":108,"slug":109},40,"healthcare",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},8,"Research & Report",30,"research-report",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":124,"slug":126},10,"Lifestyle","lifestyle",{"id":128,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":98,"slug":130},19,"General","general"]