Pathology specimens depicting cell morphology, tissue architecture and tumour–immune system at the microscopic scale. With the increasing prevalence of digital scanning technology---whole slide imaging (WSI), and as well as artificial intelligence (AI), AI-based computational pathology for interpreting digital images of slides has resulted in an explosion of interest in detection, diagnosis, and prognosis of several cancer subtypes. However, WSIs have tremendously large size, in the range of 1–10GB per image, leading to computational burden, and complexity of cancer characteristics hampers model performance for clinical routinely applications. In this project, we aim to develop AI-based methods for segmentation and classification of pathology images to build a clinical decision support tool for precision medicine. Tasks we focus on include: standard and normalization, image segmentation (e.g., delineation of tumor area, cells and cell nuclei), spatial pattern feature extraction to identify disease phenotypes, and deep learning to develop models that predict response with therapy and prognosis.