Shapiro, Linda LSWu, Wenjun2024-09-092024-09-092024-09-092024Wu_washington_0250E_27018.pdfhttps://hdl.handle.net/1773/51699Thesis (Ph.D.)--University of Washington, 2024This dissertation advances the field of digital pathology by introducing innovative deep learning approaches to improve the analysis and diagnosis of skin and breast cancers from whole slide images (WSIs). Given the complexity and variability inherent in WSIs, tradi- tional diagnostic methods often struggle with accuracy and efficiency. This work addresses these challenges through a series of projects leveraging advanced segmentation techniques, transformer-based models, and a novel Semantics-Aware Attention Guidance (SAG) framework.The initial focus of the research is on enhancing the detection and segmentation of diag- nostically significant structures within WSIs. The introduction of VSGD-Net and a two-stage segmentation approach demonstrates significant improvements in identifying melanocytes and other critical features with minimal reliance on extensive annotated data. Building on this foundation, the dissertation explores the application of transformer networks, such as HATNet and ScATNet, utilizing self-attention mechanisms to effectively learn contextual relationships across different scales in WSIs. The culmination of this research is the development of the SAG framework, which integrates semantic information into the diagnostic process, guiding attention mechanisms to focus on areas of potential malignancy. This approach not only enhances the accuracy and precision of the models, but also improves their interpretability, a critical factor in clinical settings. Empirical evaluations across multiple cancer datasets demonstrate that the proposed methods outperform existing state-of-the-art models in terms of diagnostic accuracy, robustness, and efficiency. These advancements hold significant promise for transforming cancer diagnosis, providing pathologists with powerful tools to enhance decision-making and potentially improve patient outcomes. By bridging the gap between computational models and clinical applications, this dissertation contributes to the broader goal of utilizing artificial intelligence in medicine to facilitate early detection, accurate diagnosis, and personalized treatment of cancer.application/pdfen-USCC BYComputational PathologyComputer-aided DiagnosisDeep LearningMedical Image AnalysisTransformersWhole slide ImagesComputer sciencePathologyMedical imagingBiomedical and health informaticsThesis