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Structural magnetic resonance imaging serves as input to a 3D convolutional neural network (3D-CNN) trained with transfer learning, pre-trained on normal control and DAT scans and then re-trained on MCI-to-DAT classification. The approach achieves 82.4% accuracy, visualizes key contributing brain regions via occlusion mapping, and links prediction values to longitudinal changes in clinical assessment scores to support individualized therapeutic planning.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/transfer-learning-for-predicting-conversion-from-mild-cognitive-impairment-to-dementia-of-alzheimers-type-based-on-a-3d-convolutional-neural-network/291610/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/transfer-learning-for-predicting-conversion-from-mild-cognitive-impairment-to-dementia-of-alzheimers-type-based-on-a-3d-convolutional-neural-network/291610.png","ImageObject",300,407,{"name":92,"@type":93},"Fans","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-22","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",12,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What condition does the model aim to predict, and from which baseline group?","Question",{"text":113,"@type":114},"The model predicts conversion to dementia of Alzheimer’s type (DAT) from individuals diagnosed with mild cognitive impairment (MCI).","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How is transfer learning applied in the 3D-CNN training process?",{"text":118,"@type":114},"The 3D-CNN is pre-trained on a source task using normal control and DAT scans, then re-trained on the target task to classify which MCI patients convert to DAT.",{"name":120,"@type":111,"acceptedAnswer":121},"How do the results evaluate performance and interpretability?",{"text":122,"@type":114},"Performance is reported as classification accuracy on the target task, and interpretability is provided by an occlusion-map approach highlighting contributory brain regions linked to prediction.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},291610,1789646135,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":145,"preview_page_count":4},5909892330395,"https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8","Author Manuscript Author Manuscript Author Manuscript Author Manuscript  \n\n| | HHS Public Access\u003Cbr>Author manuscript\u003Cbr>Neurobiol Aging. Author manuscript; available in PMC 2022 March 01. |\n| --- | --- |\n\nPublished in final edited form as:  \nNeurobiol Aging. 2021 March ; 99: 53–64. doi:10.1016/j.neurobiolaging.2020.12.005 .  \nTransfer Learning for Predicting Conversion from Mild Cognitive Impairment to Dementia of Alzheimer’s Type based on a 3DConvolutional Neural Network  \nJinhyeong Baea,* , Jane Stocksb, Ashley Heywoodb, Youngmoon Jungd, Lisanne Jenkinsc, Virginia Hilla, Aggelos Katsaggelose, Karteek Popurif, Howie Roseng, M. Faisal Begf, Lei Wanga,c Alzheimer’s Disease Neuroimaging Initiative**  \na Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, United States  \nb Department of Psychology, Feinberg School of Medicine, Northwestern University, Chicago, United States  \nc Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Chicago, United States  \ndSchool of Engineering, KAIST, Daejeon, South Korea  \neSchool of Engineering, Northwestern University, Evanston, United States fSchool of Engineering Science, Simon Fraser University, Burnaby, Canada gSchool of Medicine, University of California, San Francisco, United States  \nAbstract  \n*Corresponding author at: Psychiatry and Behavioral Sciences, Northwestern University, Chicago, IL, 60611 USA. [jinhyeongbae2017@u.northwestern.edu](jinhyeongbae2017@u.northwestern.edu).  \nJinhyeong Bae: Conceptualization, Methodology, Software, Validation, Formal Analysis, Investigation, Data curation, WritingOriginal Draft, Writing-Review and Editing, Visualization.  \nJane stocks: Formal analysis, Writing – Review and Editing.  \nAshley Heywood: Software, Validation, Formal analysis, Writing – Review and Editing.  \nYoungmoon Jung: Conceptualization, Writing – Review and Editing.  \nLisanne Jenkins: Writing – Review & Editing.  \nVirginia Hill: Validation, Writing – Review and Editing.  \nAggelos Katsaggelos: Conceptualization.  \nKarteek Popuri: Resources, Data Curation.  \nHowie Rosen: Funding acquisition.  \nFasial Beg: Resources, Data curation, Funding acquisition.  \nL*iDWataanguse:dCioncn ppeptalizatrationionof,tWhisringrticl–e  \nReview and Editing, Supervision, Project administration, Funding acquisition.  \nwere obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database  \n([adni.loni.usc.edu](adni.loni.usc.edu)). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing ofADNI investigators can be found at:  \n[http://adni.loni.usc.edu/wpcontent/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf](http://adni.loni.usc.edu/wpcontent/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf)  \nPublisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.  \nDisclosure Statement  \nThis manuscript has nothing to disclose for actual or potential conflict and interest.  \nAuthor Manuscript Author Manuscript Author Manuscript Author Manuscript  \nBae et al. Page 2  \nDementia of Alzheimer’s Type (DAT) is associated with devastating and irreversible cognitive decline. Predicting which patients with mild cognitive impairment (MCI) will progress to DAT isan ongoing challenge in the field. We developed a deep learning model to predict conversion from MCI to DAT. Structural magnetic resonance imaging scans were used as input to a threedimensional convolutiona","cbCaicCGxeQ33GCk","https://ap.wps.com/l/cbCaicCGxeQ33GCk","pdf",1517571,32,"English","# Abstract\n# Keywords\n# Introduction","[{\"question\":\"What condition does the model aim to predict, and from which baseline group?\",\"answer\":\"The model predicts conversion to dementia of Alzheimer’s type (DAT) from individuals diagnosed with mild cognitive impairment (MCI).\"},{\"question\":\"How is transfer learning applied in the 3D-CNN training process?\",\"answer\":\"The 3D-CNN is pre-trained on a source task using normal control and DAT scans, then re-trained on the target task to classify which MCI patients convert to DAT.\"},{\"question\":\"How do the results evaluate performance and interpretability?\",\"answer\":\"Performance is reported as classification accuracy on the target task, and interpretability is provided by an occlusion-map approach highlighting contributory brain regions linked to prediction.\"}]","Transfer Learning for Predicting Conversion from Mild Cognitive Impairment to Dementia of Alzheimer’s Type based on a 3D Convolutional Neural Network | PDF",81]