[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-116950-en":3,"doc-seo-116950-105":30,"detail-sidebar-cat-0-en-105":91},{"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},116950,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Multimodal Machine Learning in Medical Screenings - MSc(R) Thesis","Healthcare increasingly needs technology-supported medical screening to reduce delays caused by limited resources, overloaded systems, and insufficient accessibility. This MSc(R) thesis investigates multimodal machine learning as a decision-support approach, focusing on mental disorder detection in high-risk clinical contexts. The work conducts a scoping review of high-impact studies, compiling datasets and modalities and proposing an end-to-end pipeline spanning preprocessing, representation, fusion, modelling, and evaluation. It further examines multimodal fusion via “Autofusion,” an autoencoder-infused method leveraging cross-modality interaction for Alzheimer’s disease detection on DementiaBank Pitt corpus. Autofusion achieves 82.47% F1 and outperforms unimodal, early fusion, and late fusion hard-voting, with cross-modality interaction improving performance by 2–3% across metrics.","Nguyen, Thuy Trinh (2023) Multimodal machine learning in medical screenings. MSc(R) thesis.  \n[http://theses.gla.ac.uk/83883/](http://theses.gla.ac.uk/83883/)  \nCopyright and moral rights for this work are retained by the author  \nA copy can be downloaded for personal non-commercial research or study, without prior permission or charge  \nThis work cannot be reproduced or quoted extensively from without first obtaining permission in writing from the author  \nThe content must not be changed in any way or sold commercially in any format or medium without the formal permission of the author  \nWhen referring to this work, full bibliographic details including the author, title, awarding institution and date of the thesis must be given  \nEnlighten: Theses  \n[https://theses.gla.ac.uk/](https://theses.gla.ac.uk/)  \n[research-enlighten@glasgow.ac.uk](research-enlighten@glasgow.ac.uk)  \nMULTIMODAL MACHINE LEARNING IN MEDICAL SCREENINGS  \nTHUY TRINH NGUYEN  \nSUBMITTED IN FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF  \nMaster of Science by Research  \nSCHOOL OF COMPUTING SCIENCE  \nCOLLEGE OF SCIENCE AND ENGINEERING UNIVERSITY OF GLASGOW  \nSEPTEMBER 2023  \nAbstract  \nThe healthcare industry, with its high demand and standards, has long been considered a crucial area for technology-based innovation. However, the medical field often relies on experience-based evaluation. Limited resources, overloading capacity, and a lack of accessibility can hinder timely medical care and diagnosis delivery. In light of these challenges, automated medical screening as a decision-making aid is highly recommended. With the increasing availability of data and the need to explore the complementary effect among modalities, multimodal machine learning has emerged as a potential area of technology. Its impact has been witnessed across a wide range of domains, prompting the question of how far machine learning can be leveraged to automate processes in even more complex and high-risk sectors.  \nThis paper delves into the realm of multimodal machine learning in the field of automated medical screening and evaluates the potential of this area of study in mental disorder detection, a highly important area of healthcare. First, we conduct a scoping review targeted at high-impact papers to highlight the trends and directions of multimodal machine learning in screening prevalent mental disorders such as depression, stress, and bipolar disorder. Thereview provides a comprehensive list of popular datasets and extensively studied modalities. The review also proposes an end-to-end pipeline for multimodal machine learning applications, covering essential steps from preprocessing, representation, and fusion, to modelling and evaluation. While cross-modality interaction has been considered a promising factor to leverage fusion among multimodalities, the number of existing multimodal fusion methods employing this mechanism is rather limited. This study investigates multimodal fusion in more detail through the proposal of Autofusion, an autoencoder-infused fusion technique that harnesses the cross-modality interaction among different modalities. The technique is evaluated on DementiaBank's Pitt corpus to detect Alzheimer's disease, leveraging the power of cross-modality interaction. Autofusion achieves a promising performance of 79.89% inaccuracy, 83.85% in recall, 81.72% in precision, and 82.47% in F1 . The technique consistently outperforms all unimodal methods by an average of 5.24% across all metrics. Our method consistently outperforms early fusion and late fusion. Especially against the late fusion hard-voting technique, our method outperforms by an average of 20% across all met-  \nrics. Further, empirical results show that the cross-modality interaction term enhances the model performance by 2-3% across metrics. This research highlights the promising impact of cross-modality interaction in multimodal machine learning and calls for further research to unlock its full potentia","cbCaivmScQm3PMM3","https://ap.wps.com/l/cbCaivmScQm3PMM3","pdf",2362403,1,101,"English","en",105,"# Abstract\n# Keywords\n# Acknowledgements\n# Declaration\n# Publications\n## Contribution","[{\"question\":\"What problem does the thesis address in medical screening?\",\"answer\":\"The thesis targets delays and limitations in medical evaluation, where healthcare often depends on experience-based assessment and faces resource constraints and accessibility issues. It motivates automated screening as a decision-making aid.\"},{\"question\":\"What scope does the thesis cover regarding automated screening?\",\"answer\":\"It studies multimodal machine learning for automated medical screening with an emphasis on mental disorder detection, covering depression, stress, and bipolar disorder. It also evaluates an Alzheimer’s disease detection task using DementiaBank Pitt corpus.\"},{\"question\":\"What is Autofusion and how does it improve multimodal fusion?\",\"answer\":\"Autofusion is an autoencoder-infused fusion technique designed to leverage cross-modality interaction among different modalities. Empirical results show it enhances model performance compared with unimodal methods, early fusion, and late fusion hard-voting.\"}]","Multimodal Machine Learning in Medical Screenings - MSc(R) Thesis | PDF",1785672777,255,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"multimodal-machine-learning-in-medical-screenings-mscr-thesis","",{"@graph":36,"@context":85},[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/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/multimodal-machine-learning-in-medical-screenings-mscr-thesis/116950/",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","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the thesis address in medical screening?","Question",{"text":75,"@type":76},"The thesis targets delays and limitations in medical evaluation, where healthcare often depends on experience-based assessment and faces resource constraints and accessibility issues. It motivates automated screening as a decision-making aid.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What scope does the thesis cover regarding automated screening?",{"text":80,"@type":76},"It studies multimodal machine learning for automated medical screening with an emphasis on mental disorder detection, covering depression, stress, and bipolar disorder. It also evaluates an Alzheimer’s disease detection task using DementiaBank Pitt corpus.",{"name":82,"@type":73,"acceptedAnswer":83},"What is Autofusion and how does it improve multimodal fusion?",{"text":84,"@type":76},"Autofusion is an autoencoder-infused fusion technique designed to leverage cross-modality interaction among different modalities. Empirical results show it enhances model performance compared with unimodal methods, early fusion, and late fusion hard-voting.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]