  Computational Pathology (CPath) is an emerging field concerned with the study
of tissue pathology via computational algorithms for the processing and
analysis of digitized high-resolution images of tissue slides. Recent deep
learning based developments in CPath have successfully leveraged sheer volume
of raw pixel data in histology images for predicting target parameters in the
domains of diagnostics, prognostics, treatment sensitivity and patient
stratification -- heralding the promise of a new data-driven AI era for both
histopathology and oncology. With data serving as the fuel and AI as the
engine, CPath algorithms are poised to be ready for takeoff and eventual launch
into clinical and pharmaceutical orbits. In this paper, we discuss CPath
limitations and associated challenges to enable the readers distinguish hope
from hype and provide directions for future research to overcome some of the
major challenges faced by this budding field to enable its launch into the two
orbits.
