The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Variability and reproducibility in deep learning for medical image segmentation
Open accessAbstract
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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Towards a guideline for evaluation metrics in medical image segmentation
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Fuzzy System Based Medical Image Processing for Brain Disease Prediction
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Privacy-first health research with federated learning
Sadilek, A., Liu, L., Nguyen, D., Kamruzzaman, M., Serghiou, S., Rader, B., Ingerman, A., Mellem, S., Kairouz, P., Nsoesie, E. O., et al. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00489-2 · Cited by 226
Melanoma detection using deep learning-based classifications
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Deep Learning in Surgical Workflow Analysis: A Review of Phase and Step Recognition
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Metadata
- Authors
- Renard, F., Guedria, S., Palma, N. D., & Vuillerme, N.
- Venue
- Scientific Reports
- Published
- 2020-08-13
- Volume
- 10
- Issue
- 1
- Pages
- 13724-13724
- ISSN
- 2045-2322 (ISSN-L)
- OpenAlex
- W3048802707
- Cited by
- 229
- References
- 23
- FWCI
- 13.04
- Type
- journal article
- Language
- en
- Open access
- gold
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