[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122406-en":3,"doc-seo-122406-105":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},122406,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","IMPLICATION AND APPLICATIONS OF MACHINE LEARNING ON BIOMEDICAL IMAGES - Doctoral Dissertation","Medical imaging includes modalities such as computed tomography, X-ray, digital microscopy, and macro-focus dermoscopy, with the work centered on the latter two. Clinical evaluation starts with labor-intensive expert segmentation to localize a region of interest (ROI), followed by physician assessment of biological markers to distinguish benign from malignant lesions. A similar ROI localization process applies to digital microscopy after specimen slicing and staining, enabling diagnostic scoring based on subtle ROI differences. The research addresses challenges in training data availability and privacy constraints for building large medical datasets, proposing machine learning and image-processing methods to help improve screening and evaluation.","Scholars' Mine  \n\n| Doctoral Dissertations | Student Theses and Dissertations |\n| --- | --- |\n| Summer 2025\u003Cbr>Implication and Applications of Machine Learning on Biomedical Images\u003Cbr>Jason Hagerty\u003Cbr>Missouri University of Science and Technology\u003Cbr>Follow this and additional works at: [https://scholarsmine.mst.edu/doctoral_dissertations](https://scholarsmine.mst.edu/doctoral_dissertations)\u003Cbr> Part of the Computer Engineering Commons\u003Cbr>Department: Electrical and Computer Engineering |  |\n\nRecommended Citation  \nHagerty, Jason, \"Implication and Applications of Machine Learning on Biomedical Images\" (2025) . Doctoral Dissertations. 3404.  \n[https://scholarsmine.mst.edu/doctoral_dissertations/3404](https://scholarsmine.mst.edu/doctoral_dissertations/3404)  \nThis thesis is brought to you by Scholars' Mine, a service of the Missouri S&T Library and Learning Resources. This work is protected by U. S. Copyright Law. Unauthorized use including reproduction for redistribution requires the permission of the copyright holder. For more information, please contact [scholarsmine@mst.edu](scholarsmine@mst.edu).  \nIMPLICATION AND APPLICATIONS OF MACHINE LEARNING ON  \nBIOMEDICAL IMAGES  \nby  \nJASON RICHARD HAGERTY  \nA DISSERTATION Presented to the Graduate Faculty of the MISSOURI UNIVERSITY OF SCIENCE AND TECHNOLOGY In Partial Fulfillment of the Requirements for the Degree DOCTOR OF PHILOSOPHY  \nin  \nCOMPUTER ENGINEERING  \n2025  \nApproved by:  \nDr. R. Joe Stanley, Advisor  \nDr. William Van Stoecker  \nDr. Randy H. Moss  \nDr. Donald Wunsch  \nDr. Chang-Soo Kim  \n􀂔 2025 Jason Richard Hagerty All Rights Reserved  \niii  \nPUBLICATION DISSERTATION OPTION  \nThis dissertation consists of the following three articles, formatted in the style used by the Missouri University of Science and Technology:  \nPaper I, found on pages 4–29, has been published in the IEEE Journal of Biomedical and Health Informatics, in July 2019.  \nPaper II, found on pages 30–59, has been published in the Journal of Pathology Informatics, in March 2020.  \nPaper III, found on pages 60–74, has been published in Proceedings ofthe 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, in February 2017.  \niv  \nABSTRACT  \nMedical imaging ranges in modality including computer tomography imaging, xray imaging, digital microscopy, and macro-focus dermoscopy images. The latter two modalities are the focus of the presented work.  \nTo perform a diagnostic evaluation on the captured dermoscopy image, it begins with what is usually a labor-intensive operation that requires an expert to perform the initial segmentation for localizing a region of interest (ROI) . Once that ROI is obtained, a physician with years of training and experience will observe biological markers that can be used to visually differentiate whether a lesion is benign or malignant. A similar process is used for digital microscopy images. Once a sample has been prepared by slicing and staining a specimen, the ROI of the captured image is also localized and again a physician then judges the subtle difference in the ROI to deduce a diagnostic score.  \nAll this relies on having image data to use for machine learning as well as creating image-processing algorithms. Research is a way to infer how much data is needed given the current state-of-the-art methodologies. Data collection is hampered by the fact that, traditionally, medical data is treated differently than other forms of images and metadata in that a patient is entitled to privacy thus making assembling a large medical dataset difficult as well as limiting its release via open source.  \nThe presented work demonstrates methods that machine learning and image processing researchers could leverage to overcome the previously mentioned difficulties with the goal being to aid the physician in an effort to increase the number of cases that can be screened and/or evaluated.  \nv  \nACKNOWLEDGMENTS  \nTo those who saw my potenti","cbCaib0fdawDp8Jx","https://ap.wps.com/l/cbCaib0fdawDp8Jx","pdf",3921306,1,90,"English","en",105,"# Publication Dissertation Option\n# Abstract\n# Acknowledgments\n# List of Illustrations\n# List of Tables\n# Section 1. Introduction\n# Paper I. Deep Learning and Handcrafted Method Fusion: Higher Diagnostic Accuracy for Melanoma Dermoscopy Images\n## Abstract\n## 1. Introduction\n## 2. Handcrafted Feature Detection\n## 2.1. Pre-Processing\n## 2.2. Median Color Split Algorithm","[{\"question\":\"What imaging modalities does the thesis focus on?\",\"answer\":\"The thesis focuses on macro-focus dermoscopy and digital microscopy, while also situating them within broader medical imaging modalities such as CT and X-ray.\"},{\"question\":\"How does the diagnostic workflow use ROIs for evaluation?\",\"answer\":\"The workflow localizes a region of interest (ROI) using initial segmentation, then relies on physician observation of biological markers in dermoscopy or subtle ROI differences in microscopy to produce a diagnostic score.\"},{\"question\":\"What data and access challenges does the work address?\",\"answer\":\"Data collection is hindered by privacy requirements that make assembling large medical datasets difficult and limit release through open sources, affecting how much data can be used for machine learning.\"}]","IMPLICATION AND APPLICATIONS OF MACHINE LEARNING ON BIOMEDICAL IMAGES - Doctoral Dissertation | 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