Variability and reproducibility in deep learning for medical image segmentation
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 clas...
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| Published in: | Scientific reports Vol. 10; no. 1; p. 13724 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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Nature Publishing Group UK
13.08.2020
Nature Publishing Group |
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| ISSN: | 2045-2322, 2045-2322 |
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| 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. |
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| AbstractList | 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.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. 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. |
| ArticleNumber | 13724 |
| Author | Vuillerme, Nicolas Guedria, Soulaimane Renard, Félix Palma, Noel De |
| Author_xml | – sequence: 1 givenname: Félix surname: Renard fullname: Renard, Félix email: felix.renard@univ-grenoble-alpes.fr organization: Univ. Grenoble Alpes, CNRS, Grenoble INP, LIG, Univ. Grenoble Alpes, AGEIS – sequence: 2 givenname: Soulaimane surname: Guedria fullname: Guedria, Soulaimane organization: Univ. Grenoble Alpes, CNRS, Grenoble INP, LIG, Univ. Grenoble Alpes, AGEIS – sequence: 3 givenname: Noel De surname: Palma fullname: Palma, Noel De organization: Univ. Grenoble Alpes, CNRS, Grenoble INP, LIG – sequence: 4 givenname: Nicolas surname: Vuillerme fullname: Vuillerme, Nicolas organization: Univ. Grenoble Alpes, AGEIS, Institut Universitaire de France |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32792540$$D View this record in MEDLINE/PubMed https://hal.science/hal-02917117$$DView record in HAL |
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| Title | Variability and reproducibility in deep learning for medical image segmentation |
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