[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127643-en":3,"doc-seo-127643-105":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},127643,962084925502,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","A Deep Diagnostic Framework Using Explainable Artificial Intelligence and Clustering - Paper Abstract","An important part of diagnostics is understanding disease-specific properties. Machine learning can help identify such characteristics, yet image-based patient data makes insight extraction much harder, especially when rely-on handcrafted features limits what can be learned. This work introduces a framework that uses deep learning to learn deep patterns, explainable artificial intelligence to visualize influential patterns, and an explanation-weighted clustering method to summarize patterns across patients. Applied to gastrointestinal tract images, including healthy, polyps, and synthetic pathology variants, the approach organizes images by diagnostic reasons with high cluster quality and a rand index near one.","diagnostics  \nArticle  \nA Deep Diagnostic Framework Using Explainable Artificial Intelligence and Clustering  \nHåvard Horgen Thunold 1, Michael A. Riegler 1,2, Anis Yazidi 1 and Hugo L. Hammer 1,2, *  \nCitation: Thunold, H.H.; Riegler, M.A.; Yazidi, A.; Hammer, H.L. A Deep Diagnostic Framework Using Explainable Artificial Intelligence and Clustering. Diagnostics 2023, 13, 3413. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)diagnostics13223413  \nAcademic Editors: Hajime Isomoto and Henk A. Marquering  \nReceived: 18 September 2023  \nRevised: 3 November 2023  \nAccepted: 6 November 2023  \nPublished: 9 November 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Compute Science, Faculty of Technology, Art and Design, Oslo Metropolitan University, 0176 Oslo, Norway; [hhthunold@icloud.com](hhthunold@icloud.com) (H.H.T.); [michael@simula.no](michael@simula.no) (M.A.R.); [anisy@oslomet.no](anisy@oslomet.no) (A.Y.)  \n2 Department of Holistic Systems, SimulaMet, 0176 Oslo, Norway  \n* [Correspondence: hugo.hammer@oslomet.no](Correspondence: hugo.hammer@oslomet.no)  \nAbstract: An important part of diagnostics is to gain insight into properties that characterize a disease. Machine learning has been used for this purpose, for instance, to identify biomarkers in genomics. However, when patient data are presented as images, identifying properties that characterize a disease becomes far more challenging. A common strategy involves extracting features from the images and analyzing their occurrence in healthy versus pathological images. A limitation of this approach is that the ability to gain new insights into the disease from the data is constrained by the information in the extracted features. Typically, these features are manually extracted by humans, which further limits the potential for new insights. To overcome these limitations, in this paper, we propose a novel framework that provides insights into diseases without relying on handcrafted features or human intervention. Our framework is based on deep learning (DL), explainable artificial intelligence (XAI), and clustering. DL is employed to learn deep patterns, enabling efficient differentiation between healthy and pathological images. Explainable artificial intelligence (XAI) visualizes these patterns, and a novel “explanation-weighted” clustering technique is introduced to gain an overview of these patterns across multiple patients. We applied the method to images from the gastrointestinal tract. In addition to real healthy images and real images of polyps, some of the images had synthetic shapes added to represent other types of pathologies than polyps. The results show that our proposed method was capable of organizing the images based on the reasons they were diagnosed as pathological, achieving high cluster quality and a rand index close to or equal to one.  \nKeywords: clustering; deep learning; explainable artificial intelligence; image classification; knowledge discovery  \n1. Introduction  \nGaining new insights into diseases is crucial for diagnostics. Machine learning (ML) has been employed for this purpose. For instance, in genomics, ML has been used extensively to identify biomarkers for various diseases [1] . However, when patient data are presented as images, deriving insights into characteristics associated with a disease becomes more challenging. A common approach involves extracting a set of features from the images or performing image segmentation [2] . To search for new insights, these features can be analyzed using statistical or machine learning techniques. A recent advancement in this area is the use of graph con","cbCaipSCl4pIY7qS","https://ap.wps.com/l/cbCaipSCl4pIY7qS","pdf",11886339,1,18,"English","en",105,"# Introduction\n## Motivation and limitations of feature-based approaches\n## Proposed deep learning + XAI + explanation-weighted clustering framework","[{\"question\":\"Why is diagnosing disease from medical images more challenging than from other data types?\",\"answer\":\"When patient data are presented as images, deriving disease-associated characteristics is harder because information extraction often depends on engineered features or segmentation, which can limit what new insights can be discovered.\"},{\"question\":\"What components make up the proposed diagnostic framework?\",\"answer\":\"The framework combines deep learning to learn deep patterns, explainable artificial intelligence to visualize pattern influence, and an explanation-weighted clustering technique to provide an overview of patterns across multiple patients.\"},{\"question\":\"How was the method evaluated and what were the key results?\",\"answer\":\"The method was applied to gastrointestinal tract images, including healthy images, real polyps images, and images with added synthetic shapes representing other pathologies. Results show it can organize images according to why they were diagnosed as pathological, achieving high cluster quality and a rand index close to or equal to one.\"}]","A Deep Diagnostic Framework Using Explainable Artificial Intelligence and Clustering - Paper Abstract | PDF",1785940479,45,{"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},"a-deep-diagnostic-framework-using-explainable-artificial-intelligence-and-clustering-paper-abstract","",{"@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/a-deep-diagnostic-framework-using-explainable-artificial-intelligence-and-clustering-paper-abstract/127643/",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","2026-08-05",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},"Why is diagnosing disease from medical images more challenging than from other data types?","Question",{"text":75,"@type":76},"When patient data are presented as images, deriving disease-associated characteristics is harder because information extraction often depends on engineered features or segmentation, which can limit what new insights can be discovered.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What components make up the proposed diagnostic framework?",{"text":80,"@type":76},"The framework combines deep learning to learn deep patterns, explainable artificial intelligence to visualize pattern influence, and an explanation-weighted clustering technique to provide an overview of patterns across multiple patients.",{"name":82,"@type":73,"acceptedAnswer":83},"How was the method evaluated and what were the key results?",{"text":84,"@type":76},"The method was applied to gastrointestinal tract images, including healthy images, real polyps images, and images with added synthetic shapes representing other pathologies. 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