Feature Transformers: Privacy Preserving Lifelong Learners for Medical Imaging

Deep learning algorithms have achieved tremendous success in many medical imaging problems leading to multiple commercial healthcare applications. For sustaining the performance of these algorithms post-deployment, it is necessary to overcome catastrophic forgetting and continually evolve with data. While catastrophic forgetting could be managed using historical data, a fundamental challenge in Healthcare is data-privacy, where regulations constrain restrict data sharing. In this paper, we present a single, unified mathematical framework - feature transformers, for handling the myriad variants of lifelong learning to overcome catastrophic forgetting without compromising data-privacy. We report state-of-the-art results for lifelong learning on iCIFAR100 dataset and also demonstrate lifelong learning on medical imaging applications - X-ray Pneumothorax classification and Ultrasound cardiac view classification.