Distributed learning: a reliable privacy-preserving strategy to change multicenter collaborations using AI-Reference-Cited by-同舟云学术

Distributed learning: a reliable privacy-preserving strategy to change multicenter collaborations using AI

Published:2021-04-13 Issue:12 Volume:48 Page:3791-3804
ISSN:1619-7070
Container-title:European Journal of Nuclear Medicine and Molecular Imaging
language:en
Short-container-title:Eur J Nucl Med Mol Imaging

Author:

Kirienko Margarita^ORCID,Sollini Martina^ORCID,Ninatti Gaia^ORCID,Loiacono Daniele^ORCID,Giacomello Edoardo^ORCID,Gozzi Noemi^ORCID,Amigoni Francesco^ORCID,Mainardi Luca^ORCID,Lanzi Pier Luca^ORCID,Chiti Arturo^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Radiology Nuclear Medicine and imaging,General Medicine,Radiology Nuclear Medicine and imaging,General Medicine

Link

https://link.springer.com/content/pdf/10.1007/s00259-021-05339-7.pdf

Reference59 articles.

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3. Sollini M, Berchiolli R, Delgado Bolton RC, Rossi A, Kirienko M, Boni R, et al. The “3M” approach to cardiovascular infections: multimodality, multitracers, and multidisciplinary. Semin Nucl Med. 2018;48:199–224.

4. Kirienko M, Ninatti G, Cozzi L, Voulaz E, Gennaro N, Barajon I, et al. Computed tomography (CT)-derived radiomic features differentiate prevascular mediastinum masses as thymic neoplasms versus lymphomas. Radiol Med. 2020;125:951–60.

5. Sollini M, Cozzi L, Ninatti G, Antunovic L, Cavinato L, Chiti A, et al. PET/CT radiomics in breast cancer: mind the step. Methods. 2020;S1046–2023:30263–4.

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