[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120686-en":3,"doc-seo-120686-105":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":20,"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},120686,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Computational Analysis of Tissue Images in Cancer Diagnosis and Prognosis - Machine Learning-Based Methods for the Next Generation of Computational Pathology","The focus of this work is to develop machine learning systems for tissue image analysis in cancer diagnosis and prognosis. The resulting models aim to identify new prognostic markers and function as standalone clinical prediction rules by leveraging non-linear, multivariate patterns beyond conventional clinical gold standards. The research addresses major challenges created by extreme image resolution, heterogeneous microenvironments, noise and artifacts, and limited granularity of ground truth. The study evaluates handcrafted-feature approaches for prognosis, then explores deep learning methods on whole slide images, assessing performance and interpretability across colorectal and muscle-invasive bladder cancer cohorts.","COMPUTATIONAL ANALYSIS OF TISSUE IMAGES IN CANCER DIAGNOSIS AND PROGNOSIS: MACHINE LEARNINGBASED METHODS FOR THE NEXT GENERATION OF COMPUTATIONAL PATHOLOGY  \nNeofytos Dimitriou  \nA Thesis Submitted for the Degree of PhD  \nat the  \nUniversity of St Andrews  \n2023  \nFull metadata for this thesis is available in St Andrews Research Repository at:  \n[http://research-repository.st-andrews.ac.uk/](http://research-repository.st-andrews.ac.uk/)  \n[Identifiers to use to cite or link to this thesis:](Identifiers to use to cite or link to this thesis:)  \nDOI: [https://doi.org/10.17630/sta/336](https://doi.org/10.17630/sta/336)[ ](https://doi.org/10.17630/sta/336)[http://hdl.handle.net/10023/27139](http://hdl.handle.net/10023/27139)  \nThis item is protected by original copyright  \nThis item is licensed under a Creative Commons License  \n[https://creativecommons.org/licenses/by-nc/4.0](https://creativecommons.org/licenses/by-nc/4.0)  \nComputational analysis of tissue images in cancer diagnosis and prognosis: machine learningbased methods for the next generation of computational pathology  \nNeofytos Dimitriou  \nThis thesis is submitted in partial fulfilment for the degree of Doctor of Philosophy (PhD) at the University of St Andrews  \nApril 2022  \nCandidate's declaration  \nI, Neofytos Dimitriou, do hereby certify that this thesis, submitted for the degree of PhD, which is approximately 24,787 words in length, has been written by me, and that it is the record of work carried out by me, or principally by myself in collaboration with others as acknowledged, and that it has not been submitted in any previous application for any degree. I confirm that any appendices included in my thesis contain only material permitted by the 'Assessment of Postgraduate Research Students' policy.  \nI was admitted as a research student at the University of St Andrews in September 2017.  \nI received funding from an organisation or institution and have acknowledged the funder(s) in the full text of my thesis.  \nDate 30/01/2023 Signature of candidate  \nSupervisor's declaration  \nI hereby certify that the candidate has fulfilled the conditions of the Resolution and Regulations appropriate for the degree of PhD in the University of St Andrews and that the candidate is qualified to submit this thesis in application for that degree. I confirm that any appendices included in the thesis contain only material permitted by the 'Assessment of Postgraduate Research Students' policy.  \nDate 30/01/2023 Signature of supervisor  \nPermission for publication  \nIn submitting this thesis to the University of St Andrews we understand that we are giving permission for it to be made available for use in accordance with the regulations of the University Library for the time being in force, subject to any copyright vested in the work not being affected thereby. We also understand, unless exempt by an award of an embargo as requested below, that the title and the abstract will be published, and that a copy of the work may be made and supplied to any bona fide library or research worker, that this thesis will be electronically accessible for personal or research use and that the library has the right to migrate this thesis into new electronic forms as required to ensure continued access to the thesis.  \nI, Neofytos Dimitriou, confirm that my thesis does not contain any third-party material that requires copyright clearance.  \nThe following is an agreed request by candidate and supervisor regarding the publication of this thesis:  \nPrinted copy  \nNo embargo on print copy.  \nElectronic copy  \nNo embargo on electronic copy.  \nDate 30/01/2023 Signature of candidate  \nDate 30/01/2023 Signature of supervisor  \nUnderpinning Research Data or Digital Outputs  \nCandidate's declaration  \nI, Neofytos Dimitriou, hereby certify that no requirements to deposit original research data or digital outputs apply to this thesis and that, where appropriate, secondary data used have been referenced in the full te","cbCaieo3ZNE2CfFI","https://ap.wps.com/l/cbCaieo3ZNE2CfFI","pdf",52401037,1,164,"English","en",105,"# Thesis Information\n## Candidate and Supervisor Declarations\n## Publication Permissions and Embargo Policy\n## Research Data and Digital Output Requirements\n# Core Study Focus\n## Machine Learning for Tissue Image Analysis\n## Challenges: Resolution, Heterogeneity, Noise, and Ground Truth\n## Handcrafted Feature Pipelines for Prognosis\n## Deep Learning Approaches and Pipeline Motivation\n## Evaluation on Cancer Cohorts","[{\"question\":\"What is the main goal of the thesis?\",\"answer\":\"To develop machine learning systems that analyze tissue images to support cancer diagnosis and prognosis, including discovery of prognostic markers and clinical prediction rules.\"},{\"question\":\"Why is tissue-image based prognosis considered challenging in this research?\",\"answer\":\"Because images have extremely high resolution, the tumor microenvironment is highly heterogeneous, data contain multiple noise/artifact sources, and ground-truth labels are low in granularity.\"},{\"question\":\"How does the thesis compare handcrafted features with deep learning?\",\"answer\":\"It investigates handcrafted feature extraction with machine learning for prognosis, and then motivates deep learning to analyze images directly, reducing labor-intensive and bias-prone feature-pipeline design.\"}]","Computational Analysis of Tissue Images in Cancer Diagnosis and Prognosis - Machine Learning-Based Methods for the Next Generation of Computational Pathology | PDF",1785731493,413,{"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},"computational-analysis-of-tissue-images-in-cancer-diagnosis-and-prognosis-machine-learning-based-methods-for-the-next-generation-of-computational-pathology","",{"@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/computational-analysis-of-tissue-images-in-cancer-diagnosis-and-prognosis-machine-learning-based-methods-for-the-next-generation-of-computational-pathology/120686/",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":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of the thesis?","Question",{"text":75,"@type":76},"To develop machine learning systems that analyze tissue images to support cancer diagnosis and prognosis, including discovery of prognostic markers and clinical prediction rules.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why is tissue-image based prognosis considered challenging in this research?",{"text":80,"@type":76},"Because images have extremely high resolution, the tumor microenvironment is highly heterogeneous, data contain multiple noise/artifact sources, and ground-truth labels are low in granularity.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the thesis compare handcrafted features with deep learning?",{"text":84,"@type":76},"It investigates handcrafted feature extraction with machine learning for prognosis, and then motivates deep learning to analyze images directly, reducing labor-intensive and bias-prone feature-pipeline design.","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"]