Reproducibility in machine learning for medical imaging

TL;DR

This paper introduces the concept of reproducibility in machine learning for medical imaging, emphasizing its importance, types, requirements, and benefits to improve research reliability in the field.

Contribution

It provides an introductory overview of reproducibility, defining its types, discussing requirements, and advocating for a balanced approach in medical imaging machine learning research.

Findings

Different types of reproducibility are distinguished and defined.

Guidelines and requirements for achieving reproducibility are discussed.

The benefits of reproducibility and a balanced approach are emphasized.

Abstract

Reproducibility is a cornerstone of science, as the replication of findings is the process through which they become knowledge. It is widely considered that many fields of science are undergoing a reproducibility crisis. This has led to the publications of various guidelines in order to improve research reproducibility. This didactic chapter intends at being an introduction to reproducibility for researchers in the field of machine learning for medical imaging. We first distinguish between different types of reproducibility. For each of them, we aim at defining it, at describing the requirements to achieve it and at discussing its utility. The chapter ends with a discussion on the benefits of reproducibility and with a plea for a non-dogmatic approach to this concept and its implementation in research practice.