[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128125-en":3,"doc-seo-128125-105":31,"detail-sidebar-cat-0-en-105":92},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128125,3985741905716,"Rowan","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Machine Learning Algorithms for Augmenting Diagnosis of Hematological Malignancies - Dissertation","This dissertation examines how modern machine learning methods can strengthen the diagnostic workflow for hematological malignancies, especially in cytogenetics and clinical flow cytometry. Recent progress in ML enables the development, adaptation, and evaluation of algorithms that support hematopathology decision-making. Hematology oncology includes many disease types, and accurate diagnosis depends on rapid interpretation of multiple biological data streams such as morphologic images, cytogenetic micrographs, sequencing data, and high-dimensional multicolor flow cytometry.","MACHINE LEARNING TECHNIQUES FOR AUGMENTING HEMATOLOGICAL  \nMALIGNANCY DIAGNOSIS  \nAPPROVED BY SUPERVISORY COMMITTEE  \n\n| Daehwan Kim, Ph.D. |\n| --- |\n| Satwik Rajaram, Ph.D. |\n| Lily Huang, Ph.D. |\n| Marcel Mettlen, Ph.D. |\n| Gaudenz Danuser, Ph.D. |\n\nDEDICATION  \nTo my wife Emily, and my children Jacob and Molly. You are daily treasures.  \nTo my family, especially my mother and father, for all their love and support.  \nTo Dr. Daehwan Kim foryour mentorship.  \nAnd to all who have kindly offered support throughout my Ph.D. journey.  \nACKNOWLEDGEMENTS  \nThroughout my Ph.D. journey at UT Southwestern, numerous individuals have played a crucial role in my professional and personal development, and I would like to take this opportunity to express my heartfelt gratitude to them.  \nAbove all, I am immensely grateful to my Ph.D. supervisor and mentor, Dr. Daehwan Kim, for his unwavering guidance, patience, and encouragement throughout my Ph.D. journey. Dr. Kim's passion for science, commitment to creating innovative software, and pursuit of excellence in all things have truly inspired me and will continue to shape my scientific career. I feel honored to have been part of the Kim laboratory team and to have had the chance to grow as a scientist under his tutelage.  \nI also deeply appreciate the support and expertise of my dissertation committee members, Dr. Satwik Rajaram, Dr. Lily Huang, Dr. Marcel Mettlen, and Dr. Gaudenz Danuser, who played a critical role in my Ph.D. research. Additionally, I would like to acknowledge the generous support from the Cancer Prevention Research Institute of Texas.  \nI am immensely grateful for my collaboration with the Hematopathology Department at UT Southwestern, and particularly for Rolando García, Franklin Fuda, Weina Chen, and Olga Weinberg. Their insights, guidance, and expertise have been invaluable in my Ph.D. journey.  \nFurthermore, I am grateful to everyone involved in the Computational and Systems Biology Track at UT Southwestern for shaping my research and scientific training. I would also  \nlike to extend my gratitude to the faculty of the Cell and Molecular Biology Department and the Lyda Hill Department of Bioinformatics at UT Southwestern.  \nA special thank you goes out to Dr. Jared Ostmeyer for his continuous support, helpful suggestions, and invaluable mentorship throughout my Ph.D.  \nI sincerely appreciate all members of the Kim laboratory, past and present, for their assistance in experimental troubleshooting, feedback on presentations, and unwavering support. My Ph.D. journey has been enriched by the kindness and encouragement of the entire Kim laboratory family.  \nI also want to extend thanks to past and present members of the Hematopathology/Flow Cytometry division at UT Southwestern for sharing their clinical flow cytometry knowledge with me during my Ph.D. studies.  \nI would like to acknowledge and thank my friends at UT Southwestern that helped me throughout my Ph.D. studies. I would also like to extend a special thank you to the community at Northridge Presbyterian Church, training buddies from The Movement Standard and Cold Plunge Comrades, members of the Thursday night pickleball group, the community at LDU coffee, and other close friends for keeping me grounded and creating memories of fun and enjoyment during the challenges of pursuing a Ph.D.  \nI would like to extend a thank you to my family, but especially my parents, for their unwavering support, encouragement, and belief in me. I would also like to thank my in-laws, and especially my mother-and father-in-law for their immense support of me and my family.  \nFinally, a very special thank you to my wife, Emily. Thank you for your continuous patience, support, and encouragement during these many years of my scientific endeavors. Your steady presence and love have been influential in helping me cross the finish line. Thank you for  \nthe countless hours of listening to me work out my research by bending your ear. I am so ex","cbCaigUDSyyfB9g1","https://ap.wps.com/l/cbCaigUDSyyfB9g1","pdf",2701680,3,1,178,"English","en",105,"# Dedication\n# Acknowledgements\n# Abstract\n# Machine Learning for Diagnostic Augmentation\n## Automated Screening of Chromosomal Abnormalities\n## Clinical Decision Support for Flow Cytometry","[{\"question\":\"What problem does the dissertation address?\",\"answer\":\"It addresses improving diagnostic workflows for hematological malignancies using modern machine learning, with an emphasis on cytogenetics and clinical flow cytometry.\"},{\"question\":\"Which types of data are considered in the diagnostic workflow?\",\"answer\":\"The work highlights morphologic images, cytogenetic micrographs, molecular sequencing data, and high-dimensional multicolor flow cytometry (MFC) data.\"},{\"question\":\"How does the dissertation propose machine learning to help clinicians?\",\"answer\":\"It explores strategies for automated screening of recurring structural chromosomal abnormalities and for building clinical decision support to assist MFC data analysis.\"}]","Machine Learning Algorithms for Augmenting Diagnosis of Hematological Malignancies - 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