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Towards continuous learning for glioma segmentation with elastic weight consolidation
- Publication Year :
- 2019
-
Abstract
- When finetuning a convolutional neural network (CNN) on data from a new domain, catastrophic forgetting will reduce performance on the original training data. Elastic Weight Consolidation (EWC) is a recent technique to prevent this, which we evaluated while training and re-training a CNN to segment glioma on two different datasets. The network was trained on the public BraTS dataset and finetuned on an in-house dataset with non-enhancing low-grade glioma. EWC was found to decrease catastrophic forgetting in this case, but was also found to restrict adaptation to the new domain.
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.1909.11479
- Document Type :
- Working Paper