GS-EMA: Integrating Gradient Surgery Exponential Moving Average with Boundary-Aware Contrastive Learning for Enhanced Domain Generalization in Aneurysm Segmentation
<|reference_start|>GS-EMA: Integrating Gradient Surgery Exponential Moving Average with Boundary-Aware Contrastive Learning for Enhanced Domain Generalization in Aneurysm Segmentation: The automated segmentation of cerebral aneurysms is pivotal for accurate diagnosis and treatment planning. Confronted with significant domain shifts and class imbalance in 3D Rotational Angiography (3DRA) data from various medical institutions, the task becomes challenging. These shifts include differences in image appearance, intensity distribution, resolution, and aneurysm size, all of which complicate the segmentation process. To tackle these issues, we propose a novel domain generalization strategy that employs gradient surgery exponential moving average (GS-EMA) optimization technique coupled with boundary-aware contrastive learning (BACL). Our approach is distinct in its ability to adapt to new, unseen domains by learning domain-invariant features, thereby improving the robustness and accuracy of aneurysm segmentation across diverse clinical datasets. The results demonstrate that our proposed approach can extract more domain-invariant features, minimizing over-segmentation and capturing more complete aneurysm structures.<|reference_end|>
@article{lin2024gs-ema:,
    title={GS-EMA: Integrating Gradient Surgery Exponential Moving Average with
  Boundary-Aware Contrastive Learning for Enhanced Domain Generalization in
  Aneurysm Segmentation},
    author={Fengming Lin, Yan Xia, Michael MacRaild, Yash Deo, Haoran Dou,
  Qiongyao Liu, Nina Cheng, Nishant Ravikumar, Alejandro F. Frangi},
    journal={arXiv preprint arXiv:2402.15239},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.15239},
    primaryClass={cs.CV cs.LG}
}