Whole Slide Pathological Image

Whole slide pathological images (WSIs) are gigapixel-resolution digital representations of tissue samples, enabling computational analysis for improved disease diagnosis and prognosis. Current research focuses on developing robust machine learning models, particularly multiple instance learning (MIL) and attention-based architectures, to effectively analyze these massive datasets, often incorporating multi-scale features and addressing challenges like stain variation and limited annotated data. These advancements aim to improve diagnostic accuracy, automate workflows, and facilitate personalized medicine by integrating WSI analysis with genomic data and other clinical information. The ultimate goal is to enhance the efficiency and objectivity of pathology, leading to better patient care.