Abstract

With the advance in imaging technology, digital pathology imaging of tumor tissue slides is becoming a routine clinical procedure for cancer diagnosis. This process produces massive imaging data that capture histological details in high resolution. Recent developments in deep-learning methods have enabled us to classify individual regions and cells from digital pathology images on a large scale. Reliable statistical approaches to model the resulting data at the histological and cellular levels are urgently needed, as they can provide new insight into tumor progression and shed light on the biological mechanisms of cancer. In this talk, I will demonstrate how the marriage between Bayesian statistics and AI leads to more explainable and predictable paths from raw imaging data to conclusions, using several case studies of tumor pathology images.