Reproducibility in machine learning for medical imaging

• URL: http://arxiv.org/abs/2209.05097v1
• Date: Mon, 12 Sep 2022 09:00:04 GMT
• Title: Reproducibility in machine learning for medical imaging
• Authors: Olivier Colliot, Elina Thibeau-Sutre, Ninon Burgos
• Abstract summary: This chapter intends at being an introduction to for researchers in the field of machine learning for medical imaging. 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 didactic and with a plea for a non-dogmatic approach to this concept and its implementation in research practice.
• Score: 3.1390096961027076
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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