FedER: Federated Learning through Experience Replay and privacy-preserving data synthesis-Reference-Cited by-同舟云学术

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FedER: Federated Learning through Experience Replay and privacy-preserving data synthesis

Published:2024-01 Issue: Volume:238 Page:103882
ISSN:1077-3142
Container-title:Computer Vision and Image Understanding
language:en
Short-container-title:Computer Vision and Image Understanding

Author:

Pennisi Matteo^ORCID,Proietto Salanitri Federica^ORCID,Bellitto Giovanni^ORCID,Casella Bruno^ORCID,Aldinucci Marco^ORCID,Palazzo Simone^ORCID,Spampinato Concetto^ORCID

Funder

Ministero dell’Istruzione, dell’Università e della Ricerca

Publisher

Elsevier BV

Subject

Computer Vision and Pattern Recognition,Signal Processing,Software

Reference44 articles.

1. Dark experience for general continual learning: a strong, simple baseline;Buzzega,2020

2. Lung segmentation in chest radiographs using anatomical atlases with nonrigid registration;Candemir;IEEE TMI,2013

3. Skin lesion analysis toward melanoma detection;Codella,2018

4. Covid-19 image data collection: Prospective predictions are the future;Cohen,2020

5. Bcn20000: Dermoscopic lesions in the wild;Combalia,2019

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1. Enhancing skin lesion diagnosis with data augmentation techniques: a review of the state-of-the-art;Multimedia Tools and Applications;2024-08-30

2. Evaluating Generative Models in Medical Imaging;2024 IEEE 12th International Conference on Healthcare Informatics (ICHI);2024-06-03

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