Exploring the values underlying machine learning research in medical image analysis

Abstract

Machine learning has emerged as a crucial tool for medical image analysis, largely due to recent developments in deep artificial neural networks addressing numerous, diverse clinical problems. As with any conceptual tool, the effective use of machine learning should be predicated on an understanding of its underlying motivations just as much as algorithms or theory — and to do so, we need to explore its philosophical foundations. One of these foundations is the understanding of how values, despite being non-empirical, nevertheless affect scientific research. This article has three goals: to introduce the reader to values in a way that is specific to medical image analysis; to characterise a particular set of technical decisions (what we call the end-to-end vs. separable learning spectrum ) that are fundamental to machine learning for medical image analysis; and to create a simple and structured method to show how these values can be rigorously connected to these technical decisions. This better understanding of how the philosophy of science can clarify fundamental elements of how medical image analysis research is performed and can be improved. • Research values indirectly but fundamentally affect technical decisions in machine learning for medical image analysis. • Machine learning frameworks can be characterised on a spectrum regarding how much they use explicit intermediate representations, i.e. sub-problems. • Structured approaches to values can show what motivates research towards either end of said spectrum.

Topics & Concepts