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Variability and reproducibility in deep learning for medical image segmentation.

Abstract：

: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.

journal_name

Sci Rep

journal_title

Scientific reports

authors

Renard F,Guedria S,Palma N,Vuillerme N

doi

10.1038/s41598-020-69920-0

subject

Has Abstract

pub_date

2020-08-13 00:00:00

pages

13724

issue

1

issn

2045-2322

pii

10.1038/s41598-020-69920-0

journal_volume

10

pub_type

杂志文章

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