Federated learning of a computer vision system for automated interpretation of medical images

Training computer vision algorithms for automated image interpretation requires a centralised data repository. When the data consists of medical imaging examinations, the need to extract millions of historical records from a pool of different hospitals, and transfer the data into a centralised data centre, is a non-trivial task and currently one of the largest hurdles to overcome when building AI systems in radiology. For instance, the sensitivity of the data requires large-scale anonymization at each site, which is a very costly and time consuming process.
In this project we will develop a new machine learning approach for Federated Learning enabling decentralised radiological repositories located at each hospital to collaboratively learn a shared machine learning model for computer vision while keeping all the training data on site, decoupling the ability to do machine learning from the need to store the data in a centralised location.
The Federated Learning system will be developed and tested in collaboration with NHS hospitals, and will allow for smarter models, lower latency, and less power consumption, all while ensuring privacy. Our approach will simplify the delivery of AI systems to support radiological reporting across imaging modalities (e.g. X-ray, CT and MRI).
Alligns with the artificial intelligence technologies research area.