3D ANEURYSM
该数据集是一个包含101个半理想化几何结构的医学影像数据集，通过数值模拟生成颅内动脉瘤的血流动力学数据，主要模态为三维网格和血流场数据，用于计算流体动力学和医学影像分析研究，旨在模拟完整心动周期内的血流行为以辅助动脉瘤的临床诊断与治疗规划。
基本信息
资源简介
3D ANEURYSM
【数据集背景】
本数据集源站标题为《arXiv Query: search_query=&id_list=2501.08738v1&start=0&max_results=1》。源站首次发布：2025-01-15。
【数据内容】
源站原始描述：We introduce a novel masked pre-training technique for graph neural networks (GNNs) applied to computational fluid dynamics (CFD) problems. By randomly masking up to 40\% of input mesh nodes during pre-training, we force the model to learn robust representations of complex fluid dynamics. We pair this masking strategy with an asymmetric encoder-decoder architecture and gated multi-layer perceptrons to further enhance performance. The proposed method achieves state-of-the-art results on seven CFD…
【主题与分类】
主题标签：cs.LG、physics.flu-dyn。
【获取与许可】
源站页面：https://arxiv.org/abs/2501.08738v1
下载信息
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使用方式
数据集说明
3D ANEURYSM 对应论文数据集（arXiv 预印本）。
数据获取指引
- 打开论文页面获取作者与项目信息：https://arxiv.org/abs/2501.08738v1
- 论文 Data Availability / Code Availability 章节标注了数据实际托管位置；
- 获取到实际数据链接后，按对应平台标准方式下载。
论文摘要：Abstract:We introduce a novel masked pre-training technique for graph neural networks (GNNs) applied to computational fluid dynamics (CFD) problems. By randomly masking up to 40% of input mesh nodes during pre-training, we force the model to learn robust representations of complex fluid dynamics. We pair this masking strategy with an asymmetric encoder-decoder architecture and gated multi-layer perceptrons to further enhance performance. The proposed method achieves state-of-the-art results on seven CFD datasets, including a new challenging dataset of 3D intracranial aneurysm simulations with over 250,000 nodes per mesh. Moreover, it significantly improves model performance and training efficiency across such diverse range of fluid simulation tasks. We demonstrate improvements of up to 60% in long-term prediction accuracy compared to previous best models, while maintaining similar computational costs. Notably, our approach enables effective pre-training on multiple datasets simultaneously, significantly reducing the time and data required to achieve high performance on new tasks. Through extensive ablation studies, we provide insights into the optimal masking ratio, architectural choices, and training strategies.
数据缺失？
依托陕西、四川两大基地，我们与超过十家三甲医院建立直接合作关系，覆盖合规授权、采集治理、专业标注、数据交付的全流程，为AI医疗团队提供即拿即用的高质量临床数据。
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