Reproducibility in machine learning for medical imaging - ARAMISLAB - Algorithms, models and methods for images and signals of the human brain Access content directly
Book Sections Year : 2023

Reproducibility in machine learning for medical imaging

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.
Fichier principal
Vignette du fichier
Chapter 21 - Final.pdf (352.33 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
licence : CC BY - Attribution

Dates and versions

hal-03957240 , version 1 (26-01-2023)
hal-03957240 , version 2 (20-04-2023)

Licence

Attribution

Identifiers

  • HAL Id : hal-03957240 , version 2

Cite

Olivier Colliot, Elina Thibeau-Sutre, Ninon Burgos. Reproducibility in machine learning for medical imaging. Olivier Colliot. Machine Learning for Brain Disorders, Springer, 2023. ⟨hal-03957240v2⟩
137 View
63 Download

Share

Gmail Facebook X LinkedIn More