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Federated learning enables multiple institutions to collaboratively train machine learning models on their local data in a privacy-preserving way.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I. & Hinton, G. E · 2012
Earlier work this paper cites.
Amyloid deposition, hypometabolism, and longitudinal cognitive decline
Landau, S. M. et al · 2012
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The cancer imaging archive (tcia): maintaining and operating a public information repository
Clark, K. et al · 2013
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Experiments on parallel training of deep neural network using model averaging
Su, H. & Chen, H · 2015
Earlier work this paper cites.
Communication-efficient learning of deep networks from decentralized data
McMahan, H. B., Moore, E., Ramage, D., Hampson, S. et al · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S. & Sun, J · 2016
Earlier work this paper cites.
Radiomics: images are more than pictures, they are data
Gillies, R. J., Kinahan, P. E. & Hricak, H · 2016
Earlier work this paper cites.
Deep gradient compression: Reducing the communication bandwidth for distributed training
Lin, Y., Han, S., Mao, H., Wang, Y. & Dally, W. J · 2017
Earlier work this paper cites.
Diabetic retinopathy detection
Kaggle · 2017
Cited alongside, same era.
Radiomics: the bridge between medical imaging and personalized medicine
Lambin, P. et al · 2017
Cited alongside, same era.
Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning
Coudray, N. et al · 2018
Cited alongside, same era.
Predicting cancer outcomes from histology and genomics using convolutional networks
Mobadersany, P. et al · 2018
Cited alongside, same era.
Distributed deep learning networks among institutions for medical imaging
Chang, K. et al · 2018
Cited alongside, same era.
Split learning for health: Distributed deep learning without sharing raw patient data
Wavelet-based semi-supervised adversarial learning for synthesizing realistic 7t from 3t mri
Qu, L., Wang, S., Yap, P.-T. & Shen, D · 2019
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Measuring the effects of non-identical data distribution for federated visual classification
Hsu, T.-M. H., Qi, H. & Brown, M · 2019
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The non-iid data quagmire of decentralized machine learning
Hsieh, K., Phanishayee, A., Mutlu, O. & Gibbons, P. B · 2019
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Synthesized 7t mri from 3t mri via deep learning in spatial and wavelet domains
Qu, L., Zhang, Y., Wang, S., Yap, P.-T. & Shen, D · 2020
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Deep learning for tomographic image reconstruction
Wang, G., Ye, J. C. & De Man, B · 2020
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Vepakomma, P., Gupta, O., Swedish, T. & Raskar, R · 2018
Cited alongside, same era.
Group normalization
Wu, Y. & He, K · 2018
Cited alongside, same era.
Geographic distribution of us cohorts used to train deep learning algorithms
Kaushal, A., Altman, R. & Langlotz, C · 2020
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Accounting for data variability in multi-institutional distributed deep learning for medical imaging
Balachandar, N., Chang, K., Kalpathy-Cramer, J. & Rubin, D. L · 2020
Later among the works it cites.