Abstract

Viral pneumonia remains a critical global health concern due to its potential for rapid transmission and widespread outbreaks, as highlighted by the COVID-19 pandemic. Timely and accurate diagnosis, severity assessment, and prognosis are essential for effective patient care and public health management. Chest imaging modalities such as chest X-rays (CXRs) and computed tomography (CT) scans play a central role in diagnosing and managing viral pneumonia. However, interpreting these images is often resource-intensive and subject to inter-observer variability, especially during surges in clinical demand. Deep learning (DL) holds promise for automating image analysis, detecting subtle radiologic features, and enhancing clinical decision-making. Nevertheless, real-world deployment faces persistent challenges, including limited access to large-scale well-annotated
datasets, domain shifts, and the heterogeneous presentation of disease. To address these challenges, this thesis advances DL-based medical image analysis for the diagnosis and severity assessment of viral pneumonia, with a specific focus on COVID-19. Key contributions include: (i) Employing game-theoretic data valuation (Data Shapley) to identify mislabeled samples in chest image training datasets; (ii) Introducing a domain-adaptive strategy guided by model prediction confidence to improve the generalization of DL segmentation models; (iii) Developing robust DL frameworks for precise segmentation of COVID-19 lesions in chest CT scans, accounting for their diverse visual
characteristics and spatial variability; and (iv) Designing a DL-based diagnostic and severity scoring system for early-stage COVID-19 using baseline CXRs, with performance comparable to expert radiologists. Together, these contributions advance DL-assisted tools for the diagnosis and severity assessment of viral pneumonia in clinical practice.