Machine Learning for Fibre-Bundle Endomicroscopy Systems

While our understanding of Medicine and Physiology is and must ultimately be firmly rooted in the natural sciences, much of the knowledge of Clinicians and their resulting diagnostic efficacy is statistical and experiential in nature. While this kind of implicit or "intuitive" knowledge is often hard to formalize or even articulate, recent advances in applied statistical learning (particularly what is know as "deep learning") allow us to leverage the expertise of human experts and ultimately automate some of the lower-level diagnostic work the routinely perform. In addition to saving time, which is arguably the scarcest resource in most modern clinical environments, these techniques could power novel state-of-the-art engineering solutions to diagnostic problems such as that proposed by the FLIM project, which would be virtually inaccessible to human experts without the aid of artificial intelligence.