Artificial intelligence is one of the biggest topics being discussed in the realm of Computer Science and it has made incredible breakthroughs possible in so many different industries. One of the largest issues with utilizing computational resources in the health industry historically is centered around the quantity of data, the specificity of conditions for accurate results, and the general risks associated with being incorrect in an analysis. Although these all have been major issues in the past, the application of artificial intelligence has opened up an entirely different realm of possibilities because accessing massive amounts of patient data, is essential for generating an extremely accurate model in machine learning. The goal of this project is to analyze tools and algorithm design techniques used in recent times to accelerate data processing in the realm of healthcare, but one of the most important discoveries is that the standardization of conditioned data being fed into the models is almost more important than the algorithms or tools being used combined.

Electron Multiplying Charge Coupled Device (EMCCD) cameras are widely used for fluorescence microscopy experiments. However, the quantitative determination of biological parameters uniquely depends on characteristics of the unavoidably inhomogenous illumination profile as it gives rise to an image. It is therefore of interest to learn this inhomogenous illumination profiles that can dramatically vary across images alongside the camera parameters though a detailed camera model. In this manuscript we create a detailed model to learn inhomogeneous illumination profiles as well as learn all associated camera parameters. We achieve this within a Bayesian paradigm allowing us to determine full distributions over the unknowns.