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English(EN) Cross-Architectural Feature Fusion for Coronary Artery Stenosis Detection in X-ray Angiography

新型AI模型融合特征以改进冠状动脉狭窄检测

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种名为Fusion-YOLOv11-L的新型深度学习框架，用于在X射线血管造影中检测冠状动脉狭窄。该混合模型集成了YOLOv11-L检测器和UNet++分割网络，利用通道注意力门控融合语义和结构特征。UNet++组件在ARCADE血管分割数据集上进行了预训练，以增强解剖学理解。在ARCADE*数据集上的实验表明，性能有所提高，AP@50达到0.585，F1分数达到0.596，优于独立的YOLOv11-L。通过测试时适应和增强技术，该框架在OxAMI Study数据集上也表现出更强的泛化能力。 AI

影响 该模型可以提高冠状动脉疾病诊断的准确性和效率，可能导致更早的干预。

排序理由 学术论文，详细介绍了新模型及其在特定数据集上的性能。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新型AI模型融合特征以改进冠状动脉狭窄检测

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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Haobo Zhu, Mojtaba Lashgari, Jason Chai, Giovanni Luigi De Maria, Vicente Grau, Abhirup Banerjee ·

    X射线血管造影冠状动脉狭窄检测的跨架构特征融合

    arXiv:2610.32730v1 Announce Type: new Abstract: Coronary artery disease remains a leading cause of mortality, with stenosis detection from invasive X-ray Coronary Angiography (XCA) critical for diagnosis. Manual interpretation is time-consuming, subjective, and variable, especial…

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