A Bayesian hidden Potts mixture model for analyzing lung cancer pathology images.

ABSTRACT: Digital pathology imaging of tumor tissues, which captures histological details in high resolution, is fast becoming a routine clinical procedure. Recent developments in deep-learning methods have enabled the identification, characterization, and classification of individual cells from pathology images analysis at a large scale. This creates new opportunities to study the spatial patterns of and interactions among different types of cells. Reliable statistical approaches to modeling such spatial patterns and interactions can provide insight into tumor progression and shed light on the biological mechanisms of cancer. In this article, we consider the problem of modeling a pathology image with irregular locations of three different types of cells: lymphocyte, stromal, and tumor cells. We pro

SUBMITTER: Li Q 

PROVIDER: S-EPMC6797059 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature