http://hdl.handle.net/10453/19978 2026-11-04T19:20:22Z http://hdl.handle.net/10453/197300 Title: Characterisation of Chronic Obstructive Pulmonary Disease Using Advanced Ventilation Imaging Authors: Jeagal, Edward Abstract: Chronic obstructive pulmonary disease (COPD) is a complex and heterogeneous disease. Conventional markers of disease severity such as spirometric parameters and demographics struggle to adequately characterise the disease due to their insensitivity. There have been efforts at COPD phenotyping by utilising complementary lung function tests and imaging techniques. Ventilation single photon computed tomography (VSPECT) may be utilised to quantitatively analyse ventilation defects. Qualitative VSPECT studies have long noted there to be poorer ventilation on patients with more severe disease. Due to advancements in imaging technology and analysis software, quantitative imaging analysis is more accessible than ever. Unfortunately, quantitative VSPECT has seldom been used to investigate COPD and available analysis techniques remain unvalidated. This thesis aims to assess and validate various analysis methods for quantitative VSPECT and investigate the relationships between quantitative VSPECT parameters and various lung function measurements in COPD patients, at baseline, post-bronchodilator, and post-methacholine. Description: University of Technology Sydney. Faculty of Science. 2024-01-01T00:00:00Z http://hdl.handle.net/10453/197298 Title: Deciphering the molecular mechanisms of helminth-host interactions Authors: Chowdhury, Sumaiya Abstract: Parasitic worms (helminths) infect more than 1.5 billion people worldwide and cause major economic losses to the agricultural industry through infection of livestock. Their persistence is driven by their ability to modulate host immune responses, while current control strategies are increasingly limited by drug resistance, lack of vaccines, and poor diagnostics. This thesis investigated the role of parasite-derived microRNAs (miRNAs) in host-parasite interactions using Fasciola hepatica (liver fluke) as a model organism. The study identified distinct host- and parasite-derived circulating miRNA signatures associated with different stages of infection, demonstrating their potential as sensitive, stage-specific biomarkers for early diagnosis of fasciolosis. Importantly, this thesis revealed for the first time that parasite-derived miRNAs transferred to host cells are present as sequence length variants, known as isomiRs. The study demonstrated that these isomiRs can regulate mammalian immune cells and suggests that they are selectively deployed by the parasite to fine-tune host immune responses. The research also showed that these parasite-derived isomiRs remain functional even when key components of the host RNA interference pathway, Ago2 and Dicer, are disrupted, suggesting adaptation of flexible mechanisms to utilise mammalian RNAi pathways. Together, these findings provide new insights into helminth biology, advance understanding of cross-kingdom miRNA-mediated parasite-host communication and support the development of improved diagnostic and therapeutic strategies. Description: University of Technology Sydney. Faculty of Science. 2025-01-01T00:00:00Z http://hdl.handle.net/10453/197297 Title: Deep Learning-Based Metastatic Detection and Localization for Breast Cancer Diagnosis in Whole Slide Histopathology Images Authors: Khaliliboroujeni, Sepideh Abstract: Breast cancer is one of the most prevalent and lethal diseases among women worldwide, and early and accurate detection, particularly of metastasis, is crucial to improving patient survival rates. Deep learning techniques have revolutionized image analysis, yet their application to histopathology Whole Slide Images (WSIs) remains a challenge due to the massive size, complex tissue structures, and limited annotations of WSIs. Although advances such as patch-based classification, multi-scale modeling, and weakly supervised learning have improved accuracy and reduced annotation needs, challenges remain. Metastasis detection and localization approaches still face high computational demands, reliance on extensive annotations, and difficulty capturing global context and fine details. Patch-based methods are widely used as they enable manageable processing of large WSIs by dividing them into smaller regions. However, they often struggle with boundary precision and detecting small metastases, while reliance on high-resolution WSIs increases costs and limits scalability in clinical practice. This thesis addresses the core research question of how deep learning models can be optimized for metastasis detection and localization in large-scale histopathology images while ensuring efficiency, reliability, and clinical deployability in weakly supervised settings. To address this question, this thesis proposes two novel, efficient, weakly supervised deep-learning approaches for the detection and localization of metastases in WSIs. The first approach, detailed in Chapter 3, focuses on maximal computational efficiency, achieving efficient inference for large-scale WSI analysis using low-resolution inputs and minimal annotated data, making it suitable for rapid clinical triage. The second, enhanced framework (Chapter 4) prioritizes spatial accuracy and improved data efficiency by addressing the limitations of coarse patch-based analysis. It improves contextual modeling and boundary-level precision through advanced architectural components, enabling more refined localization of metastatic regions under weak supervision. Both newly proposed methods reduce computational and annotation costs by requiring only a fraction of labeled data. Evaluated on the CAMELYON16 dataset, they outperform state-of-the-art methods and expert pathologists in metastasis detection and localization, demonstrating robustness in large-scale, noisy, and limited-data environments. Description: University of Technology Sydney. Faculty of Engineering and Information Technology. 2026-01-01T00:00:00Z http://hdl.handle.net/10453/197296 Title: Blockchain based Intelligent Identity Management Authors: Alotaibi, Suhair Abstract: Digital identity management remains a critical challenge in the digital era, as traditional centralized models are susceptible to data breaches, impersonation, and reliance on single points of authority. To address these limitations, this research proposes a Blockchain-based Intelligent Identity Management (BIIM) framework that integrates blockchain technology with biometric authentication to deliver a secure, transparent, and user-centric paradigm for identity management. The BIIM framework comprises three core modules: blockchain-enabled biometric identity management, user-driven data ownership, and a federated identity mechanism. Collectively, these modules enable individuals and organizations to securely register, validate, and manage identity records within a decentralized environment. Biometric data serve as a primary authentication factor, while the federated identity feature ensures consistent and verifiable identity utilization across multiple domains, thereby reducing redundancy and enhancing user control. A prototype implementation of BIIM was developed and empirically evaluated in terms of scalability, security, and operational efficiency in managing user identity. The evaluation results demonstrate that BIIM strengthens defenses against impersonation, enhances transparency in verification processes, and empowers users with full control of their identity data. Nonetheless, limitations persist, particularly regarding transaction costs and latency inherent to blockchain-based systems. This study contributes to the academic field of decentralized identity management by introducing a biometrics-integrated, blockchain-driven, and federated framework for secure and autonomous identity governance. The research highlights BIIM’s potential to establish a scalable, privacy-preserving, and user-centric identity management system, while underscoring the need for further optimization and regulatory alignment to enable practical deployment and adoption. Description: University of Technology Sydney. Faculty of Engineering and Information Technology. 2024-01-01T00:00:00Z