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Wenjun (Meredith) Wu

Graduated: June 7, 2024

Thesis/Dissertation Title:

Transformative Diagnostics: Applying Transformer Networks and Semantic Guidance to Whole Slide Images

This dissertation advances digital pathology by developing deep learning techniques for more accurate and efficient analysis of skin and breast cancer from whole slide images. It introduces innovative approaches like VSGD-Net and a two-stage segmentation method, along with transformer-based models such as HatNet and ScatNet, which leverage self-attention to understand contextual relationships within the images. A key innovation is the Semantics-Aware Attention Guidance framework that enhances diagnostic precision and interpretability by focusing on critical areas, significantly outperforming existing models. These advancements provide pathologists with powerful tools, bridging the gap between computational models and clinical applications, thereby improving early detection, diagnosis, and treatment of cancer.