Abstract
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.
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Notes
- 1.
There is no restriction on the kind of layers to be used, but in this work we use only fully connected layers.
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Ravishankar, H., Venkataramani, R., Anamandra, S., Sudhakar, P., Annangi, P. (2019). Feature Transformers: Privacy Preserving Lifelong Learners for Medical Imaging. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11767. Springer, Cham. https://doi.org/10.1007/978-3-030-32251-9_38
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DOI: https://doi.org/10.1007/978-3-030-32251-9_38
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