The quality of clinical pathology is a critical index for evaluating a nation's healthcare level. Recently developed digital pathology techniques have the capability to transform pathological slides into digital whole slide images (WSI). This transformation facilitates data storage, online transmission, real-time viewing, and remote consultations, significantly elevating clinical diagnosis. The effectiveness and efficiency of digital pathology imaging often hinge on the precision and speed of autofocusing. However, achieving autofocusing of pathological images presents challenges under constraints including uneven focus distribution and limited Depth of Field (DoF). Current autofocusing methods, such as those relying on image stacks, need to use more time and resources for capturing and processing images. Moreover, autofocusing based on reflective hardware systems, despite its efficiency, incurs significant hardware costs and suffers from a lack of system compatibility. Finally, machine learning-based autofocusing can circumvent repetitive mechanical movements and camera shots. However, a simplistic end-to-end implementation that does not account for the imaging process falls short of delivering satisfactory focus prediction and in-focus image restoration. In this thesis, we present three distinct autofocusing techniques for defocus pathology images: (1) Aberration-aware Focal Distance Prediction leverages the asymmetric effects of optical aberrations, making it ideal for focus prediction within focus map scenarios; (2) Dual-shot Deep Autofocusing with a Fixed Offset Prior is designed to merge two images taken at different defocus distances with fixed positions, ensuring heightened accuracy in in-focus image restoration for fast offline situations; (3) Semi-blind Deep Restoration of Defocus Images utilizes multi-task joint prediction guided by PSF, enabling high-efficiency, single-pass scanning for offline in-focus image restoration. / Thesis / Doctor of Philosophy (PhD)

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With cancer being one of the leading causes of death globally, and with oral cancers being among the most common types of cancer, it is of interest to conduct large-scale oral cancer screening among the general population. Deep Learning can be used to make this possible despite the medical expertise required for early detection of oral cancers. A bottleneck of Deep Learning is the large amount of data required to train a good model. This project investigates two topics: certainty calibration, which aims to make a machine learning model produce more reliable predictions, and Active Learning, which aims to reduce the amount of data that needs to be labeled for Deep Learning to be effective. In the investigation of certainty calibration, five different methods are compared, and the best method is found to be Dirichlet calibration. The Active Learning investigation studies a single method, Cost-Effective Active Learning, but it is found to produce poor results with the given experiment setting. These two topics inspire the further development of the cytological annotation tool CytoBrowser, which is designed with oral cancer data labeling in mind. The proposedevolution integrates into the existing tool a Deep Learning-assisted annotation workflow that supports multiple users.