Generalizability of prostate MRI deep learning: does one size fit all data?
Author:
Publisher
Springer Science and Business Media LLC
Subject
Radiology, Nuclear Medicine and imaging,General Medicine
Link
https://link.springer.com/content/pdf/10.1007/s00330-023-09886-5.pdf
Reference10 articles.
1. Hughes H, O’Reilly M, McVeigh N, Ryan R (2023) The top 100 most cited articles on artificial intelligence in radiology: a bibliometric analysis. Clin Radiol 78:99–106. https://doi.org/10.1016/j.crad.2022.09.133
2. Litjens G, Kooi T, Bejnordi BE et al (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60–88. https://doi.org/10.1016/j.media.2017.07.005
3. Wright BD, Vo N, Nolan J et al (2020) An analysis of key indicators of reproducibility in radiology. Insights Imaging 11:65. https://doi.org/10.1186/s13244-020-00870-x
4. Kim DW, Jang HY, Ko Y et al (2020) Inconsistency in the use of the term “validation” in studies reporting the performance of deep learning algorithms in providing diagnosis from medical imaging. PLoS One 15:e0238908. https://doi.org/10.1371/journal.pone.0238908
5. Netzer N, Eith C, Bethge O et al (2023) Application of a validated prostate MRI deep learning system to independent same-vendor multi-institutional data: demonstration of transferability. Eur Radiol. https://doi.org/10.1007/s00330-023-09882-9
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