Interpretable Analysis of Melanoma in Whole Slide Imaging: Detection, Virtual Staining, and Diagnostic Insights

Abstract

Whole slide imaging (WSI) has transformed digital pathology, offering extensive details in skin biopsies used for melanoma diagnosis. However, clinical assessments remain challenging, with diagnostic accuracy and efficiency limited by the inherent complexity and variability of these images. While computer-aided diagnosis (CAD) systems can analyze WSIs using deep learning approaches, they often treat the images as pure data inputs, lacking the clinical understanding essential for nuanced assessment. Developing an accurate and reliable CAD model therefore requires not only detecting diagnostically relevant structures but also capturing the clinical context in which these structures are assessed. This dissertation aims to address these needs by introducing a novel diagnosis model that integrates both the key diagnostic structures and the interpretive processes pathologists use to evaluate WSIs. The initial focus of the work is on detecting and segmenting diagnostically relevant structures within WSIs, beginning with a method for identifying melanocytic proliferations using sparse and noisy annotations to highlight suspicious regions that guide diagnostic reasoning. To further investigate cellular entities, VSGD-Net was developed to accurately detect melanocytes in H&E-stained slides, a crucial step for analyzing melanocyte distribution and growth patterns in melanoma. Additionally, VSGD-Net enables virtual synthesis of IHC-stained images from standard H&E WSIs, facilitating further insights without the need for additional staining procedures. This method is extended by CC-WSI-Net, which enables seamless synthesis across entire slides rather than isolated patches, enhancing contextual coherence at the whole-slide level. To support pathologists' diagnostic workflow, the Semantics-Aware Attention Guidance (SAG) framework is introduced, integrating semantic information to guide model's attention toward regions with high diagnostic relevance. Finally, a Multi-level Region-of-Interest Attending Network (MiRA) is developed to emulate how pathologists diagnose WSIs by integrating information from both low-resolution whole slides and high-resolution regions of interest. This dual-level approach improves diagnostic efficiency and aligns the model's behavior with clinical workflows, making it both effective and interpretable for pathologists. In summary, this dissertation presents deep learning methods for interpretable melanoma diagnosis, integrating key diagnostic structures with clinical reasoning. These advancements aim to improve the reliability and consistency of melanoma diagnoses, supporting more efficient clinical workflows and better patient outcomes.