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Computational Pathology (CPath) is an emerging field concerned with the studyof tissue pathology via computational algorithms for the processing andanalysis of digitized high-resolution images of tissue slides. Recent deeplearning based developments in CPath have successfully leveraged sheer volumeof raw pixel data in histology images for predicting target parameters in thedomains of diagnostics, prognostics, treatment sensitivity and patientstratification -- heralding the promise of a new data-driven AI era for bothhistopathology and oncology. With data serving as the fuel and AI as theengine, CPath algorithms are poised to be ready for takeoff and eventual launchinto clinical and pharmaceutical orbits. In this paper, we discuss CPathlimitations and associated challenges to enable the readers distinguish hopefrom hype and provide directions for future research to overcome some of themajor challenges faced by this budding field to enable its launch into the twoorbits.

Towards Launching AI Algorithms for Cellular Pathology into Clinical & Pharmaceutical Orbits