XAI for Lymph Node Histopathology: From Explainability to Immersive Metaverse Applications

This paper presents an integrated framework that combines segmentation, explainable artificial intelligence, and metaverse technologies to improve the histopathological analysis of lymph node tissues. A modified U-Net was designed to enhance the segmentation accuracy of metastatic regions. The Dice score achieved was 0.87, IoU of 0.80, and PPV of 0.90, surpassing the original U-Net. XAI techniques, including Grad-CAM visualizations, were used to provide interpretability so that pathologists can understand and validate the decisions of AI. The feedback from the pathologists was such that XAI outputs aligned with histological principles, thus increasing trust and usability. The framework also included an immersive metaverse environment, allowing pathologists to navigate segmented 3D histopathological slides interactively. Zoomable regions, adjustable segmentation thresholds, and click-to-explain functionalities empowered collaboration in diagnostics and training. A quantitative evaluation reflected a reduction of 30% in the time taken to diagnose and an increase of 15% in the confidence scores. By integrating segmentation, XAI, and immersive technologies, this framework fills the gap between AI developments and clinical usability while allowing for transparent, efficient, and collaborative diagnostic workflows. Future work will be dedicated to the refinement of the framework and the extension of its application to other pathological conditions.