End-to-end privacy preserving deep learning on multi-institutional medical imaging.
Georgios KaissisAlexander ZillerJonathan Passerat-PalmbachThéo RyffelDmitrii UsyninAndrew TraskIonésio LimaJason MancusoFriederike JungmannMarc-Matthias SteinbornAndreas SalehMarcus R. MakowskiDaniel RueckertRickmer BrarenPublished in: Nat. Mach. Intell. (2021)
Keyphrases
- end to end
- medical imaging
- privacy preserving
- deep learning
- medical images
- privacy preservation
- x ray
- privacy preserving data mining
- image registration
- unsupervised learning
- vertically partitioned data
- image processing
- image analysis
- image segmentation
- sensitive information
- computer vision
- private information
- secure multiparty computation
- remote sensing
- mental models
- data privacy
- machine learning
- privacy concerns
- weakly supervised
- data management
- image data
- digital libraries
- pattern recognition
- similarity measure
- real world