# Towards continuous learning for glioma segmentation with elastic weight consolidation

arXiv: https://arxiv.org/abs/1909.11479 · HTML: https://mlsanity.com/paper/1909.11479/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Sep 2019.

## Built on by (most cited first, 11 of 11)

- [Continual Learning in Medical Image Analysis: A Comprehensive Review of Recent Advancements and Future Prospects](https://mlsanity.com/paper/2312.17004/) (2023)
- [Rethinking Exemplars for Continual Semantic Segmentation in Endoscopy Scenes: Entropy-based Mini-Batch Pseudo-Replay](https://mlsanity.com/paper/2308.14100/) (2023)
- [Adversarial Continual Learning for Multi-Domain Hippocampal Segmentation](https://mlsanity.com/paper/2107.08751/) (2021)
- [Continual Hippocampus Segmentation with Transformers](https://mlsanity.com/paper/2204.08043/) (2022)
- [Continual Learning in Medical Imaging: A Survey and Practical Analysis](https://mlsanity.com/paper/2405.13482/) (2024)
- [Stabilizing Elastic Weight Consolidation method in practical ML tasks and using weight importances for neural network pruning](https://mlsanity.com/paper/2109.10021/) (2021)
- [Segmentation of Multiple Sclerosis Lesions across Hospitals: Learn Continually or Train from Scratch?](https://mlsanity.com/paper/2210.15091/) (2022)
- [A Path Towards Clinical Adaptation of Accelerated MRI](https://mlsanity.com/paper/2208.12835/) (2022)
- [Continual learning for surface defect segmentation by subnetwork creation and selection](https://mlsanity.com/paper/2312.05100/) (2023)
- [Empirical investigations on WVA structural issues](https://mlsanity.com/paper/2208.05791/) (2022)
- [Correlation of the importances of neural network weights calculated by modern methods of overcoming catastrophic forgetting](https://mlsanity.com/paper/2211.17012/) (2022)

## Abstract (arXiv)

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.