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Advancing Privacy-Preserving Health Care Analytics and Implementation of the Personal Health Train: Federated Deep Learning Study

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Machine learning and modeling: Data, validation, communication challenges

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Deep learning‐based internal gross target volume definition in 4D CT images of lung cancer patients

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Privacy-Enhancing Technologies

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Privacy-Preserving Federated Data Analysis: Data Sharing, Protection, and Bioethics in Healthcare

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How to Share Data and Promote a Rapid Learning Health Medicine?

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ResNetFed: Federated Deep Learning Architecture for Privacy-Preserving Pneumonia Detection from COVID-19 Chest Radiographs

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Feasibility of Privacy-Preserving Federated Deep Learning on Medical Images

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Privacy preservation for federated learning in health care

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Distributed learning on 20 000+ lung cancer patients – The Personal Health Train

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SplitAVG: A Heterogeneity-Aware Federated Deep Learning Method for Medical Imaging

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Usage Patterns of Privacy-Enhancing Technologies

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Distributed Analytics on Sensitive Medical Data: The Personal Health Train

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A Survey on Automatic Delineation of Radiotherapy Target Volume based on Machine Learning

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Automatic Segmentation of the Gross Target Volume in Non-Small Cell Lung Cancer Using a Modified Version of ResNet

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Towards developing a secure medical image sharing system based on zero trust principles and blockchain technology

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Federated Learning for Medical Image Analysis with Deep Neural Networks

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IKNL/VANTAGE6: 0.3.0-alpha5

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Incoming claims: where this work appears in other papers’ reference lists and arguments.

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