[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125686-en":3,"doc-seo-125686-105":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},125686,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Hierarchical, Informed and Robust Machine Learning for Surgical Tool Management","The thesis develops a computer vision and deep learning system for intelligent surgical tool management. It builds a new dataset and designs state-of-the-art methods to handle volume, variety, and vision challenges, enabling reliable recognition across tools with subtle shape differences under changing illumination and backgrounds. The approach includes an image dataset and a surgical tool attribute matrix/knowledge base, addressing the lack of large public datasets and established textual annotations. A hierarchical model generates multi-level predictions across speciality, pack, set, and tool; robustness is enhanced using synthetic data and knowledge infusion to improve predictive performance.","[http://researchcommons.waikato.ac.nz/](http://researchcommons.waikato.ac.nz/)  \nResearch Commons at the University of Waikato  \nCopyright Statement:  \nThe digital copy of this thesis is protected by the Copyright Act 1994 (New Zealand) .  \nThe thesis may be consulted by you, provided you comply with the provisions of the Act and the following conditions of use:  \n􀁸 Any use you make of these documents or images must be for research or private study purposes only, and you may not make them available to any other person.  \n􀁸 Authors control the copyright of their thesis. You will recognise the author’s right to be identified as the author of the thesis, and due acknowledgement will be made to the author where appropriate.  \n􀁸 You will obtain the author’s permission before publishing any material from the thesis.  \nHierarchical, Informed and Robust Machine Learning for Surgical Tool  \nManagement  \nA thesis submitted in fulfillment of the requirements for the degree of  \nDoctor of Philosophy  \nin the  \nDepartment of Computer Science The University of Waikato  \nby  \nMARK WILLIAM RODRIGUES  \nii  \nAbstract  \nHierarchical, Informed and Robust Machine Learning for Surgical Tool  \nManagement  \nby MARK WILLIAM RODRIGUES  \nThis thesis focuses on the development of a computer vision and deep learning based system for the intelligent management of surgical tools. The work accomplished included the development of a new dataset, creation of state of the art techniques to cope with volume, variety and vision problems, and designing or adapting algorithms to address specific surgical tool recognition issues. The system was trained to cope with a wide variety of tools, with very subtle differences in shapes, and was designed to work with high volumes, as well as varying illuminations and backgrounds. Methodology that was adopted in this thesis included the creation of a surgical tool image dataset and development of a surgical tool attribute matrix or knowledge-base. This was significant because there are no large scale publicly available surgical tool datasets, nor are there established annotations or datasets of textual descriptions of surgical tools that can be used for machine learning. The work resulted in the development of a new hierarchical architecture for multi-level predictions at surgical speciality, pack, set and tool level. Additional work evaluated the use of synthetic data to improve robustness of the CNN, and the infusion of knowledge to improve predictive performance.  \niii  \nAcknowledgements  \nThe most important aspect of any PhD is the Supervisory Team, and I was blessed and fortunate to get a fantastic team of Supervisors. This Team unfailingly provided me with incredible support, direction and insights. Dr Tony Smith was incredibly helpful in his comments and insights into what could be done to improve the work; Iam eternally grateful for his assistance and for his strong and unwavering support. Dr Michael Mayo is without doubt the best Supervisor/Manager I have ever worked with; he always responded promptly to submissions and queries with insightful comments and definitive directions. His wealth of experience, generosity in providing time and sharing knowledge, and willingness to read up new material that could contribute to improving the research was a revelation and a blessing. Dr Panos Patros provided structure, organisation and attention to detail in all my work. He ensured that my research always maintained high standards, and invariably provided cheerful and happy inputs to my work. He provided invaluable professional direction and career support, and I could not have hoped for a better mentor and guide.  \nI am grateful for Dale Fletcher for putting me onto the Computer Science path, and for the constant encouragement and “stress-relief” hitting on the tennis court. The academic staff who managed and delivered the PGCertInfoTech program – John Thompson, Geoff Holmes, David Bainbridge, Alvin Yeo – thank you for a magn","cbCaivlfKCI3OUR2","https://ap.wps.com/l/cbCaivlfKCI3OUR2","pdf",29202177,1,142,"English","en",105,"# Introduction\n## The Problem\n## The Solution\n## Surgical Tool Recognition\n## Research Questions\n## Thesis Contribution, Scope and Limitations\n## Publications\n## Thesis Organisation\n# Datasets Survey\n## Introduction\n## Survey Methodology\n## Dataset Review\n## Challenge Datasets\n## Other Surgical Tool Datasets\n## Algorithm Review\n## Tool Presence Detection Research\n## Tool Localisation Research\n## Tool Tracking Research\n## Tool Segmentation Research\n## Tool Pose Estimation Research\n## Open Research Questions","[{\"question\":\"What is the core goal of the thesis?\",\"answer\":\"To develop a computer vision and deep learning system that manages surgical tools intelligently, focusing on accurate recognition and robust performance in real imaging conditions.\"},{\"question\":\"How does the thesis address the lack of public surgical tool datasets?\",\"answer\":\"It develops a new surgical tool image dataset and an attribute matrix/knowledge base, noting that large public datasets and established textual annotations are not available.\"},{\"question\":\"What techniques improve robustness and predictive performance?\",\"answer\":\"The work evaluates synthetic data to strengthen CNN robustness and infuses knowledge to improve predictive performance in surgical tool recognition.\"}]","Hierarchical, Informed and Robust Machine Learning for Surgical Tool Management | 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