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Bdair, T. ; Navab, N.* ; Albarqouni, S.

FedPerl: Semi-supervised peer learning for skin lesion classification.

In: (24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021, 27 September-01 October 2021, Virtual, Online). Berlin [u.a.]: Springer, 2021. 336-346 (Lect. Notes Comput. Sc. ; 12903 LNCS)
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Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
Skin cancer is one of the most deadly cancers worldwide. Yet, it can be reduced by early detection. Recent deep-learning methods have shown a dermatologist-level performance in skin cancer classification. Yet, this success demands a large amount of centralized data, which is oftentimes not available. Federated learning has been recently introduced to train machine learning models in a privacy-preserved distributed fashion demanding annotated data at the clients, which is usually expensive and not available, especially in the medical field. To this end, we propose FedPerl, a semi-supervised federated learning method that utilizes peer learning from social sciences and ensemble averaging from committee machines to build communities and encourage its members to learn from each other such that they produce more accurate pseudo labels. We also propose the peer anonymization (PA) technique as a core component of FedPerl. PA preserves privacy and reduces the communication cost while maintaining the performance without additional complexity. We validated our method on 38,000 skin lesion images collected from 4 publicly available datasets. FedPerl achieves superior performance over the baselines and state-of-the-art SSFL by 15.8%, and 1.8% respectively. Further, FedPerl shows less sensitivity to noisy clients (https://github.com/tbdair/FedPerlV1.0 ).
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Publikationstyp Artikel: Konferenzbeitrag
Korrespondenzautor
Schlagwörter Semi-supervised Federated Learning ; Skin Cancer
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Konferenztitel 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021
Konferzenzdatum 27 September-01 October 2021
Konferenzort Virtual, Online
Zeitschrift Lecture Notes in Computer Science
Quellenangaben Band: 12903 LNCS, Heft: , Seiten: 336-346 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Nichtpatentliteratur Publikationen
Institut(e) Helmholtz Artifical Intelligence Cooperation Unit (HAICU)