Whole Slide

Whole slide imaging (WSI) involves analyzing digitized pathology slides, enabling automated analysis of gigapixel images for improved diagnostic accuracy and efficiency. Current research focuses on developing deep learning models, often employing convolutional neural networks and transformer architectures, to perform tasks such as cancer detection, grading, and survival prediction, often using weakly supervised or self-supervised learning techniques to address data scarcity. These advancements aim to improve the speed and accuracy of diagnoses, potentially leading to better patient outcomes and more efficient workflows in pathology. The field is actively exploring methods to improve model robustness, generalization, and integration with clinical data for more comprehensive analysis.