Transformative AI for Automating Histopathology Workflows

Histopathology plays a pivotal role in the diagnosis of various diseases, particularly cancers, through microscopic examination of tissue samples. However, traditional pathology workflows are labor intensive, prone to variability, and require significant expertise. This thesis addresses these challenges by leveraging Transformative Artificial Intelligence (AI) to automate and enhance the histopathology workflow. Research focuses on developing advanced AI models and tools for artifact classification, cancer diagnosis, whole slide image (WSI) management, and tissue segmentation. Key contributions include the development of a Transformer Convolutional Neural Network (TCNN) to classify seven artifact types in diverse tissue types, ensuring clean data sets for subsequent analyses. Furthermore, a Multi-Attention Vision Transformer (MAViT) underpins the HistoCancer-CAD Network, enabling accurate diagnosis of ten prevalent cancers in hematoxylin-and eosinstained slides. Efficient data handling is achieved through the High-Resolution Data Mapping function (HDM-f), compressing WSIs without compromising diagnostic fidelity. Moreover, the Histo-Cell model, utilizing a Multi-Level Vision Transformer (L-ViT), delivers precise contextual segmentation of tissue components, including tumors, fat, and stroma. The systematic integration of these models improves accuracy, efficiency, and scalability in Digital Pathology workflows. This work bridges the gap between advances in artificial intelligence and clinical pathology applications, providing open-source tools to foster innovation and adoption. The findings demonstrate significant implications, including the diagnosis of 10 of the most lethal cancers with over 94% diagnostic sensitivity, optimizing storage in whole slide imaging by up to 92.36%, segmentation of histological tissue components with over 91% segmentation performance, removal of 7 common types of WSI artifacts from 22 tissue types with 96.90% accuracy and paving the way for future exploration of AI in histopathology.