Skip to content

Latest commit

 

History

History
47 lines (33 loc) · 3 KB

index.md

File metadata and controls

47 lines (33 loc) · 3 KB

Start Here

About

PriMIA is a framework for end-to-end privacy preserving machine learning on medical images. It provides ready-made applications for federated training of convolutional neural networks and for encrypted inference from a simple command-line interface.

Use Cases

PriMIA is designed with flexibility and accessibility in mind. It's suitable for beginners just starting out their experimentation with privacy preserving machine learning and can scale up to decentralised cloud instance training on real-life medical datasets. We incorporate current state-of-the-art privacy preservation methods from PySyft and CrypTen and extend them with functionality tailored to medical images and technical innovations.

How to get started

To train a model on a real-life medical dataset, just run:

python train.py --config configs/torch/pneumonia-resnet-pretrained.ini --train_federated --data_dir data/server_simulation

PriMIA will train a ResNet18 using federated learning on PySyft VirtualWorkers and return the model weights and metrics in model_weights!

Then just run:

python inference.py --model_weights <path/to/weights.pt> --encrypted_inference --data_dir data/test

And PriMIA will return you end-to-end encrypted predictions on the test set using Function Secret Sharing!

We also provide many more options, such as other models, working with MNIST for weaker machines, turning on and off various privacy-enhancing technologies, a CrypTen benchmark, hyperparameter search for the whole federation and a gamut of choices, all easy to implement via a single .ini file. To have a look at some more recipes, go to this page!

Team

PriMIA is built by a diverse team of academics, open source contributors and industry researchers. Our core team consists of:

  • Georgios Kaissis (Project Lead), PostDoc at TUM and ICL, OpenMined Research Scientist
  • Alex Ziller (Lead Developer), PhD student at TUM
  • Jonathan Passerat Palmbach (Security Lead), PostDoc at ICL, Director of Cryptography at Consensys Health
  • Rickmer Braren (Medical Lead), Professor at TUM
  • Théo Ryffel, PhD student at INRIA, OpenMined Cryptography lead, CTO at Arkhn
  • Andrew Trask, PhD student at University of Oxford, OpenMind project leader

... and many others from the Technical University of Munich, Imperial College London, INRIA, University of Oxford, OpenMined, Facebook Research, Consensys Health and Arkhn.

Publication

This is the companion repository to our publication (xxx). The technical supplement can be found here.

Dataset

We used the Paediatric Pneumonia Dataset originally created by Kermany et al.

Disclaimer

This software is a research product and is provided as is without any guarantees of functionality, security, support or compatibility. Until appropriately validated, it must not be used in any critical workflow and we assume no liability for any user action or omission.