We'll introduce how to develop big image-omics data analytics algorithms with GPU computing tools for clinical outcome prediction from pathological images and cell profiling data of cancer patients. Recent technological innovations are enabling scientists to capture image-omics data at increasing speed and resolution, where the image-omics refers to both image data (pathology images or radiology images) and omics data (genomics, proteomics, or metabolomics) captured from the same patient. This is generating a deluge of heterogeneous data from different views. Thus, a compelling need exists to develop novel data analytics tools to foster and fuel the next generation of scientific discovery in image-omics data-related research. However, the major computational challenges are due to the unprecedented scale and complexity of heterogeneous image-omics data analytics. There is a critical need for large-scale modeling and mining strategies to bridge the gap and facilitate knowledge discovery from complex image-omics data. We'll introduce our recent work on developing novel deep learning methods to detect cells in the terapixel histopathological images with 10,000+ speedup and automatically discovering biomarkers for clinical outcome prediction.