[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118547-en":3,"doc-seo-118547-105":30,"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":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},118547,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",8,"Research & Report","TOPOLOGICAL MACHINE LEARNING IN MEDICAL IMAGE ANALYSIS","Medical image analysis supports detection, diagnosis, and treatment across modern healthcare. This thesis studies topological data analysis combined with machine learning, using persistent and cubical homology to extract topological features from 2D and 3D medical images. Through filtration that tracks the birth and death of features and a vector representation of persistence diagrams, topological feature vectors improve interpretability and learning performance. Experiments on histopathology, chest X-rays, retinal images, and MEDMNIST demonstrate strong results, including efficient and interpretable Topo-ML, Topo-CXR, and retinal models.","TOPOLOGICAL MACHINE LEARNING IN  \nMEDICAL IMAGE ANALYSIS  \nby  \nFaisal Ahmed  \nAPPROVED BY SUPERVISORY COMMITTEE:  \n\n| Baris Coskunuzer, Chair |\n| --- |\n| Yulia Gel |\n| Yan Cao |\n\nQiwei Li  \nCopyright © 2023 Faisal Ahmed  \nAll rights reserved  \nThis thesis is dedicated to my parents, whose unwavering support and sacrifices in my childhood  \npaved the way for my fulfilling life.  \nI am also deeply grateful to my advisor, Professor Baris Coskunuzer, for his invaluable guidance and support throughout my enriching journey in pursuit of my PhD.  \nTOPOLOGICAL MACHINE LEARNING IN  \nMEDICAL IMAGE ANALYSIS  \nby  \nFAISAL AHMED, BS, MS  \nDISSERTATION  \nPresented to the Faculty of The University of Texas at Dallas in Partial Fulfillment  \nof the Requirements for the Degree of  \nDOCTOR OF PHILOSOPHY IN  \nMATHEMATICS  \nTHE UNIVERSITY OF TEXAS AT DALLAS  \nDecember 2023  \nACKNOWLEDGMENTS  \nI would like to extend my heartfelt gratitude to the following individuals and entities who have played a pivotal role in the successful completion of my thesis. Without their unwavering support, guidance, and encouragement, this journey would have been far more challenging.  \nFirst and foremost, I want to express my deepest appreciation to my supervisor, Professor Baris Coskunuzer. Your guidance, patience, and support throughout the lifetime of this thesis have been invaluable. I consider myself incredibly fortunate to have had you as my PhD advisor during the final moments of my doctoral journey. Your mentorship transformed my vague ideas into practical implementations. Thank you, Professor Baris Coskunuzer, for everything you have done, and may our collaboration continue for many more years.  \nI am indebted to my mother, Rahena Begum, for her unwavering mental support during my PhD journey. Her encouragement was a pillar of strength, without which I would not have been able to complete this arduous journey. To my beloved wife, Tasmia Jahan, your love, companionship, and unwavering support gave me the strength and determination to persevere in the face of challenges. Your belief in me was a constant motivator.  \nI wish to express my gratitude to Professor Mieczyslaw Dabkowski, Professor Yan Cao, and Professor Viswanath Ramakrishna for generously sharing their time and offering invaluable advice during my early days in this field. Your guidance and insights will forever be etched in my memory.  \nProfessor Mohammad Akbar, I am deeply thankful for the mental support you provided during the early stages of my PhD journey. Your encouragement and guidance were instrumental.  \nI also want to acknowledge the entire staff of the Department of Mathematical Sciences for their  \ncontinuous help and support. Your contributions were instrumental in the success of this endeavor.  \nLastly, but certainly not least, I would like to thank my family, including my parents, brothers, and uncles, for their constant love and support throughout this journey. Your unwavering belief in me has been a source of inspiration.  \nNovember 2023  \nTOPOLOGICAL MACHINE LEARNING IN  \nMEDICAL IMAGE ANALYSIS  \nFaisal Ahmed, PhD  \nThe University of Texas at Dallas, 2023  \nSupervising Professor: Baris Coskunuzer  \nMedical image analysis is a critical component of modern healthcare, aiding in the detection, diagnosis, and treatment of various diseases. In recent years, the fusion of topological data analysis (TDA) and machine learning has brought about a transformative approach to understanding and leveraging the complex patterns inherent in medical images. This thesis explores the application of TDA, with a focus on persistent and cubical homology, as a powerful tool for extracting topological features from 2D and 3D medical images. By capturing the birth and death of these features through a filtration process and representing them as feature vectors, we enhance the interpretability and performance of machine learning models.  \nOur journey begins with the transformation of color images into gray","cbCaiqXIdJxmum6q","https://ap.wps.com/l/cbCaiqXIdJxmum6q","pdf",5913771,1,129,"English","en",105,"# Acknowledgments\n# Medical Image Analysis and Motivation\n# Topological Feature Extraction Pipeline\n## Filtration, Persistence Diagrams, and Vectorization\n# Experimental Datasets and Applications\n## Cancer Histopathology\n## Chest X-rays\n## Retinal Images and MEDMNIST\n# Overall Evaluation and Findings","[{\"question\":\"What problem does the thesis address in medical image analysis?\",\"answer\":\"It addresses how to detect and analyze complex patterns in medical images using topological features that are difficult to capture with conventional techniques.\"},{\"question\":\"How does the method extract features from medical images?\",\"answer\":\"It applies topological data analysis with persistent and cubical homology, generating persistence diagrams via filtration and converting them into feature vectors for machine learning.\"},{\"question\":\"Which medical imaging tasks and datasets are evaluated?\",\"answer\":\"The thesis evaluates cancer histopathology, chest X-rays, retinal images, and the MEDMNIST dataset, demonstrating effectiveness for classification and abnormality detection.\"}]","TOPOLOGICAL MACHINE LEARNING IN MEDICAL IMAGE ANALYSIS | 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problem does the thesis address in medical image analysis?","Question",{"text":76,"@type":77},"It addresses how to detect and analyze complex patterns in medical images using topological features that are difficult to capture with conventional techniques.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the method extract features from medical images?",{"text":81,"@type":77},"It applies topological data analysis with persistent and cubical homology, generating persistence diagrams via filtration and converting them into feature vectors for machine learning.",{"name":83,"@type":74,"acceptedAnswer":84},"Which medical imaging tasks and datasets are evaluated?",{"text":85,"@type":77},"The thesis evaluates cancer histopathology, chest X-rays, retinal images, and the MEDMNIST dataset, demonstrating effectiveness for classification and abnormality 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