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GS-EMA: Integrating Gradient Surgery Exponential Moving Average with Boundary-Aware Contrastive Learning for Enhanced Domain Generalization in Aneurysm Segmentation
Feb. 26, 2024, 5:43 a.m. | Fengming Lin, Yan Xia, Michael MacRaild, Yash Deo, Haoran Dou, Qiongyao Liu, Nina Cheng, Nishant Ravikumar, Alejandro F. Frangi
cs.LG updates on arXiv.org arxiv.org
Abstract: 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) …
abstract aneurysm arxiv automated cerebral class cs.cv cs.lg data diagnosis domain gradient medical moving pivotal planning segmentation surgery treatment type
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