Summary of Gs-ema: Integrating Gradient Surgery Exponential Moving Average with Boundary-aware Contrastive Learning For Enhanced Domain Generalization in Aneurysm Segmentation, by Fengming Lin et al.
GS-EMA: Integrating Gradient Surgery Exponential Moving Average with Boundary-Aware Contrastive Learning for Enhanced Domain Generalization in Aneurysm Segmentation
by Fengming Lin, Yan Xia, Michael MacRaild, Yash Deo, Haoran Dou, Qiongyao Liu, Nina Cheng, Nishant Ravikumar, Alejandro F. Frangi
First submitted to arxiv on: 23 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach for accurate cerebral aneurysm segmentation in 3D Rotational Angiography (3DRA) data is presented. The challenge lies in the significant domain shifts and class imbalance across various medical institutions, which affect image appearance, intensity distribution, resolution, and aneurysm size. To address this issue, a novel domain generalization strategy combines gradient surgery exponential moving average (GS-EMA) optimization with boundary-aware contrastive learning (BACL). This approach learns domain-invariant features to improve robustness and accuracy in aneurysm segmentation across diverse clinical datasets. By extracting more domain-invariant features, the proposed method minimizes over-segmentation and captures more complete aneurysm structures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Cerebral aneurysms need to be accurately diagnosed and treated. A big challenge is that images from different hospitals look quite different. Our new approach helps solve this problem by learning what’s important in an image, no matter where it comes from. This makes the results more accurate and reliable. We tested our method on many different sets of images and saw that it worked really well. |
Keywords
* Artificial intelligence * Domain generalization * Optimization
