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Federated learning, which shares the weights of the neural network across clients, is gaining attention in the healthcare sector as it enables training on a large corpus of decentralized data while maintaining data privacy.
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Federated learning: Strategies for improving communication efficiency
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Practical secure aggregation for privacy-preserving machine learning
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Distributed deep learning networks among institutions for medical imaging
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Clinically applicable deep learning for diagnosis and referral in retinal disease
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Distributed learning of deep neural network over multiple agents
O. Gupta and R. Raskar · 2018
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Federated learning for mobile keyboard prediction
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RSNA Pneumonia Detection Challenge
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Deep learning in medical image registration: a review
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Deep learning for automatic pneumonia detection
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Joint reconstruction and bias field correction for undersampled mr imaging
M. Gaillochet, K. C. Tezcan, and E. Konukoglu · 2020
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End-to-end evaluation of federated learning and split learning for internet of things
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Federated learning: Challenges, methods, and future directions
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Multi-institutional deep learning modeling without sharing patient data: A feasibility study on brain tumor segmentation
M. J. Sheller, G. A. Reina, B. Edwards, J. Martin, and S. Bakas · 2018
Cited alongside, same era.
Automatic instrument segmentation in robot-assisted surgery using deep learning
A. A. Shvets, A. Rakhlin, A. A. Kalinin, and V. I. Iglovikov · 2018
Cited alongside, same era.
Split learning for health: Distributed deep learning without sharing raw patient data
P. Vepakomma, O. Gupta, T. Swedish, and R. Raskar · 2018
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Less is more: Simultaneous view classification and landmark detection for abdominal ultrasound images
Z. Xu, Y. Huo, J. Park, B. Landman, A. Milkowski, S. Grbic, and S. Zhou · 2018
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Probabilistic federated neural matching
M. Yurochkin, M. Agarwal, S. Ghosh, K. Greenewald, N. Hoang, and Y. Khazaeni · 2018
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Variational federated multi-task learning
L. Corinzia, A. Beuret, and J. M. Buhmann · 2019
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Deepharmony: a deep learning approach to contrast harmonization across scanner changes
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N. Tajbakhsh, L. Jeyaseelan, Q. Li, J. N. Chiang, Z. Wu, and X. Ding · 2020
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Deep learning for tomographic image reconstruction
G. Wang, J. C. Ye, and B. De Man · 2020
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Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal
L. Wynants, B. Van Calster, G. S. Collins, R. D. Riley, G. Heinze, E. Schuit, M. M. Bonten, D. L. Dahly, J. A. Damen, T. P. Debray, et al · 2020
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Weakly supervised lesion localization with probabilistic-cam pooling
W. Ye, J. Yao, H. Xue, and Y. Li · 2020
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idlg: Improved deep leakage from gradients
B. Zhao, K. R. Mopuri, and H. Bilen · 2020
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Deep transfer learning artificial intelligence accurately stages COVID-19 lung disease severity on portable chest radiographs
J. Zhu, B. Shen, A. Abbasi, M. Hoshmand-Kochi, H. Li, and T. Q. Duong · 2020
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https://github.com/OpenMined/PySyft
Openmined/pysyft: A library for answering questions using data you cannot see · 2021
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https://www.tensorflow.org/federated
Tensorflow federated · 2021
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https://grpc.io/
grpc · 2021
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Maximizing a deep submodular function optimization with a weighted max-sat problem for trajectory clustering and motion segmentation
K. K. Chandriah and R. V. Naraganahalli · 2021
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Transunet: Transformers make strong encoders for medical image segmentation
J. Chen, Y. Lu, Q. Yu, X. Luo, E. Adeli, Y. Wang, L. Lu, A. L. Yuille, and Y. Zhou · 2021
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Transformers in vision: A survey
S. Khan, M. Naseer, M. Hayat, S. W. Zamir, F. S. Khan, and M. Shah · 2021
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Federated learning: Opportunities and challenges
P. M. Mammen · 2021
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S. Park, G. Kim, Y. Oh, J. B. Seo, S. M. Lee, J. H. Kim, S. Moon, J.-K. Lim, and J. C. Ye · 2021
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Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
M. Roberts, D. Driggs, M. Thorpe, J. Gilbey, M. Yeung, S. Ursprung, A. I. Aviles-Rivero, C. Etmann, C. McCague, L. Beer, et al · 2021
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Bs-net: learning covid-19 pneumonia severity on a large chest x-ray dataset
A. Signoroni, M. Savardi, S. Benini, N. Adami, R. Leonardi, P. Gibellini, F. Vaccher, M. Ravanelli, A. Borghesi, R. Maroldi, and D. Farina · 2021
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Soteria: Provable defense against privacy leakage in federated learning from representation perspective
J. Sun, A. Li, B. Wang, H. Yang, H. Li, and Y. Chen · 2021
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A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises
S. K. Zhou, H. Greenspan, C. Davatzikos, J. S. Duncan, B. van Ginneken, A. Madabhushi, J. L. Prince, D. Rueckert, and R. M. Summers · 2021
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