Handling data heterogeneity with generative replay in collaborative learning for medical imaging.
Handling data heterogeneity with generative replay in collaborative learning for medical imaging.
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DOI：
10.1016/j.media.2022.102424
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发表时间：
2022-05
影响因子：
10.9
通讯作者：
Rubin, Daniel
中科院分区：
文献类型：
--
作者：
Qu, Liangqiong;Balachandar, Niranjan;Zhang, Miao;Rubin, Daniel
关键词：
协作学习能够以隐私保护的方式在多个机构对深度神经网络进行协作和分散训练，正在迅速成为医疗保健应用中的一种有价值的技术。然而，它的分布式特性往往导致机构间数据分布的显著异质性。在本文中，我们提出了一种新的生成重放策略，以解决协作学习方法中的数据异构性的挑战。与传统的直接聚合模型参数的方法不同，我们利用生成对抗学习来聚合来自所有本地机构的知识。具体来说，我们开发了一种新的双模型架构，而不是直接训练任务性能模型：主模型学习所需的任务，辅助的“生成重放模型”允许从异构客户端聚合知识。然后将辅助模型广播到中心服务器，以调节具有无偏目标分布的主模型的训练。实验结果表明，该方法在处理异构数据跨机构的能力。在高度异构的数据分区上，与最先进的协作学习方法相比，我们的模型在糖尿病视网膜病变分类数据集上的预测准确率提高了约4.88%，在骨龄预测数据集上的平均绝对值降低了约49.8%。
Collaborative learning, which enables collaborative and decentralized training of deep neural networks at multiple institutions in a privacy-preserving manner, is rapidly emerging as a valuable technique in healthcare applications. However, its distributed nature often leads to significant heterogeneity in data distributions across institutions. In this paper, we present a novel generative replay strategy to address the challenge of data heterogeneity in collaborative learning methods. Different from traditional methods that directly aggregating the model parameters, we leverage generative adversarial learning to aggregate the knowledge from all the local institutions. Specifically, instead of directly training a model for task performance, we develop a novel dual model architecture: a primary model learns the desired task, and an auxiliary “generative replay model” allows aggregating knowledge from the heterogenous clients. The auxiliary model is then broadcasted to the central sever, to regulate the training of primary model with an unbiased target distribution. Experimental results demonstrate the capability of the proposed method in handling heterogeneous data across institutions. On highly heterogeneous data partitions, our model achieves ~4.88% improvement in the prediction accuracy on a diabetic retinopathy classification dataset, and ~49.8% reduction of mean absolution value on a Bone Age prediction dataset, respectively, compared to the state-of-the art collaborative learning methods.
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影响因子：
7.7
作者：
Buda, Mateusz;Saha, Ashirbani;Mazurowski, Maciej A.
通讯作者：
Mazurowski, Maciej A.
影响因子：
19.7
作者：
Langlotz, Curtis P.;Allen, Bibb;Kandarpa, Krishna
通讯作者：
Kandarpa, Krishna
DOI：
10.1093/jamia/ocy017
发表时间：
2018-08-01
期刊：
Journal of the American Medical Informatics Association : JAMIA
影响因子：
--
作者：
Chang K;Balachandar N;Lam C;Yi D;Brown J;Beers A;Rosen B;Rubin DL;Kalpathy-Cramer J
通讯作者：
Kalpathy-Cramer J
影响因子：
82.9
作者：
Dayan, Ittai;Roth, Holger R.;Zhong, Aoxiao;Harouni, Ahmed;Gentili, Amilcare;Abidin, Anas Z.;Liu, Andrew;Costa, Anthony Beardsworth;Wood, Bradford J.;Tsai, Chien-Sung;Wang, Chih-Hung;Hsu, Chun-Nan;Lee, C. K.;Ruan, Peiying;Xu, Daguang;Wu, Dufan;Huang, Eddie;Kitamura, Felipe Campos;Lacey, Griffin;de Antonio Corradi, Gustavo Cesar;Nino, Gustavo;Shin, Hao-Hsin;Obinata, Hirofumi;Ren, Hui;Crane, Jason C.;Tetreault, Jesse;Guan, Jiahui;Garrett, John W.;Kaggie, Joshua D.;Park, Jung Gil;Dreyer, Keith;Juluru, Krishna;Kersten, Kristopher;Rockenbach, Marcio Aloisio Bezerra Cavalcanti;Linguraru, Marius George;Haider, Masoom A.;AbdelMaseeh, Meena;Rieke, Nicola;Damasceno, Pablo F.;Silva, Pedro Mario Cruz E.;Wang, Pochuan;Xu, Sheng;Kawano, Shuichi;Sriswasdi, Sira;Park, Soo Young;Grist, Thomas M.;Buch, Varun;Jantarabenjakul, Watsamon;Wang, Weichung;Tak, Won Young;Li, Xiang;Lin, Xihong;Kwon, Young Joon;Quraini, Abood;Feng, Andrew;Priest, Andrew N.;Turkbey, Baris;Glicksberg, Benjamin;Bizzo, Bernardo;Kim, Byung Seok;Tor-Diez, Carlos;Lee, Chia-Cheng;Hsu, Chia-Jung;Lin, Chin;Lai, Chiu-Ling;Hess, Christopher P.;Compas, Colin;Bhatia, Deepeksha;Oermann, Eric K.;Leibovitz, Evan;Sasaki, Hisashi;Mori, Hitoshi;Yang, Isaac;Sohn, Jae Ho;Murthy, Krishna Nand Keshava;Fu, Li-Chen;Furtado de Mendonca, Matheus Ribeiro;Fralick, Mike;Kang, Min Kyu;Adil, Mohammad;Gangai, Natalie;Vateekul, Peerapon;Elnajjar, Pierre;Hickman, Sarah;Majumdar, Sharmila;McLeod, Shelley L.;Reed, Sheridan;Graf, Stefan;Harmon, Stephanie;Kodama, Tatsuya;Puthanakit, Thanyawee;Mazzulli, Tony;de Lavor, Vitor Lima;Rakvongthai, Yothin;Lee, Yu Rim;Wen, Yuhong;Gilbert, Fiona J.;Flores, Mona G.;Li, Quanzheng
通讯作者：
Li, Quanzheng
影响因子：
4.8
作者：
DICE, LR
通讯作者：
DICE, LR