Federated disentangled representation learning for unsupervised brain anomaly detection-Reference-Cited by-同舟云学术

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Federated disentangled representation learning for unsupervised brain anomaly detection

Published:2022-08-25 Issue:8 Volume:4 Page:685-695
ISSN:2522-5839
Container-title:Nature Machine Intelligence
language:en
Short-container-title:Nat Mach Intell

Author:

Bercea Cosmin I.^ORCID,Wiestler Benedikt,Rueckert Daniel,Albarqouni Shadi^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Networks and Communications,Computer Vision and Pattern Recognition,Human-Computer Interaction,Software

Link

https://www.nature.com/articles/s42256-022-00515-2.pdf

Reference52 articles.

1. Klawiter, E. C. Current and new directions in MRI in multiple sclerosis. Continuum 19, 1058–1073 (2013).

2. Soltaninejad, M. et al. Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI. Int. J. Comput. Assist. Radiol. Surg. 12, 183–203 (2017).

3. Sun, C., Shrivastava, A., Singh, S. & Gupta, A. Revisiting unreasonable effectiveness of data in deep learning era. In Proc. IEEE International Conference on Computer Vision 843–852 (IEEE, 2017).

4. Rieke, N. et al. The future of digital health with federated learning. NPJ Digit. Med. 3, 1–7 (2020).

5. Kaissis, G. A., Makowski, M. R., Rückert, D. & Braren, R. F. Secure, privacy-preserving and federated machine learning in medical imaging. Nat. Mach. Intell. 2, 305–311 (2020).

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