Details

Autor(en) / Beteiligte

• Renard, Félix
• Guedria, Soulaimane
• Palma, Noel De
• Vuillerme, Nicolas

Titel

Variability and reproducibility in deep learning for medical image segmentation

Ist Teil von

• Scientific reports, 2020-08, Vol.10 (1), p.13724-13724, Article 13724

Ort / Verlag

England: Nature Publishing Group

Erscheinungsjahr

2020

Link zum Volltext

Quelle

MEDLINE

Beschreibungen/Notizen

• Medical image segmentation is an important tool for current clinical applications. It is the backbone of numerous clinical diagnosis methods, oncological treatments and computer-integrated surgeries. A new class of machine learning algorithm, deep learning algorithms, outperforms the results of classical segmentation in terms of accuracy. However, these techniques are complex and can have a high range of variability, calling the reproducibility of the results into question. In this article, through a literature review, we propose an original overview of the sources of variability to better understand the challenges and issues of reproducibility related to deep learning for medical image segmentation. Finally, we propose 3 main recommendations to address these potential issues: (1) an adequate description of the framework of deep learning, (2) a suitable analysis of the different sources of variability in the framework of deep learning, and (3) an efficient system for evaluating the segmentation results.