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Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation
10 October 2018
Micah J. Sheller
G. A. Reina
Brandon Edwards
Jason Martin
Spyridon Bakas
FedML
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Papers citing
"Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation"
50 / 156 papers shown
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13 May 2021
The Federated Tumor Segmentation (FeTS) Challenge
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12 May 2021
Membership Inference Attacks on Deep Regression Models for Neuroimaging
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06 May 2021
Distributed Learning in Wireless Networks: Recent Progress and Future Challenges
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26 Mar 2021
Distributed Learning for Melanoma Classification using Personal Health Train
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24 Mar 2021
Deep Learning for Chest X-ray Analysis: A Survey
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15 Mar 2021
FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space
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Cheng Chen
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10 Mar 2021
FedDis: Disentangled Federated Learning for Unsupervised Brain Pathology Segmentation
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Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated Learning
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Jinyuan Zhou
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Vishal M. Patel
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274
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03 Mar 2021
Scalable federated machine learning with FEDn
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Addi Ait-Mlouk
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295
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27 Feb 2021
GaNDLF: A Generally Nuanced Deep Learning Framework for Scalable End-to-End Clinical Workflows in Medical Imaging
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Siddhesh P. Thakur
İbrahim Ethem Hamamcı
Ujjwal Baid
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D. Kontos
Alexandros Karargyris
Renato Umeton
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Spyridon Bakas
LM&MA
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26 Feb 2021
Scaling Neuroscience Research using Federated Learning
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FedML
157
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Semi-Synchronous Federated Learning for Energy-Efficient Training and Accelerated Convergence in Cross-Silo Settings
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257
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Failure Prediction in Production Line Based on Federated Learning: An Empirical Study
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226
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Personalized Federated Deep Learning for Pain Estimation From Face Images
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169
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FLAME: Taming Backdoors in Federated Learning (Extended Version 1)
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Phillip Rieger
Huili Chen
Hossein Yalame
Helen Mollering
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Azalia Mirhoseini
S. Zeitouni
F. Koushanfar
A. Sadeghi
T. Schneider
AAML
367
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06 Jan 2021
Fidel: Reconstructing Private Training Samples from Weight Updates in Federated Learning
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Zaid Al-Ars
FedML
216
15
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01 Jan 2021
Comparison of Privacy-Preserving Distributed Deep Learning Methods in Healthcare
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M. Gawali
S. ArvindC.
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Harshit Madaan
A. Gaikwad
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203
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227
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200
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Automated Pancreas Segmentation Using Multi-institutional Collaborative Deep Learning
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Dynamic Fusion based Federated Learning for COVID-19 Detection
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Haoyun Sun
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195
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22 Sep 2020
Federated Learning for Computational Pathology on Gigapixel Whole Slide Images
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Dehan Kong
Jana Lipkova
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Tiffany Y. Chen
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297
211
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21 Sep 2020
Local and Central Differential Privacy for Robustness and Privacy in Federated Learning
Network and Distributed System Security Symposium (NDSS), 2020
Mohammad Naseri
Jamie Hayes
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FedML
312
199
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08 Sep 2020
Blockchain-based Federated Learning for Device Failure Detection in Industrial IoT
Weishan Zhang
Qinghua Lu
Qiuyu Yu
Zhaotong Li
Yue Liu
Sin Kit Lo
Shiping Chen
Xiwei Xu
Liming Zhu
183
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06 Sep 2020
Federated Learning for Breast Density Classification: A Real-World Implementation
H. Roth
Ken Chang
Praveer Singh
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Wenqi Li
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I. Dayan
R. Naidu
Mona G. Flores
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173
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A Multisite, Report-Based, Centralized Infrastructure for Feedback and Monitoring of Radiology AI/ML Development and Clinical Deployment
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93
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Precision Health Data: Requirements, Challenges and Existing Techniques for Data Security and Privacy
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S. Çamtepe
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Inverse Distance Aggregation for Federated Learning with Non-IID Data
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151
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A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises
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S. Kevin Zhou
H. Greenspan
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282
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Privacy-Preserving Technology to Help Millions of People: Federated Prediction Model for Stroke Prevention
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Ruihui Zhao
Jichao Sun
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Bo Zhao
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Dashan Gao
Ben Tan
Han Yu
Chuning He
Yuan Jin
FedML
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165
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MU
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Decentralized Differentially Private Segmentation with PATE
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65
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An Overview of Federated Deep Learning Privacy Attacks and Defensive Strategies
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FedML
176
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Edge Intelligence: Architectures, Challenges, and Applications
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Tong Li
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Sasu Tarkoma
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272
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The Future of Digital Health with Federated Learning
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Maximilian Baust
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Multi-site fMRI Analysis Using Privacy-preserving Federated Learning and Domain Adaptation: ABIDE Results
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Artificial Intelligence in Glioma Imaging: Challenges and Advances
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M. Fatehi
Kumar Abhishek
Mayur Mallya
B. Toyota
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401
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Abnormal Client Behavior Detection in Federated Learning
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Yong Cheng
Yang Liu
Wei Wang
Tianjian Chen
AAML
155
155
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22 Oct 2019
CAI4CAI: The Rise of Contextual Artificial Intelligence in Computer Assisted Interventions
Proceedings of the IEEE (Proc. IEEE), 2019
Tom Vercauteren
Mathias Unberath
N. Padoy
Nassir Navab
226
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20 Oct 2019
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