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English(EN) VesselBridge3D: A Foundation Model Adaptation Framework for Label-Efficient 3D Vessel Segmentation

VesselBridge3D框架适应基础模型以实现低数据3D血管分割

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了VesselBridge3D，一个旨在改进医学影像中3D血管分割的新框架，特别是在数据稀疏的情况下。该框架使用轻量级3D适应模块来适应现有的基础模型，如DINOv3、MedSAM和MedGemma。VesselBridge3D展示了显著的性能提升，在仅有五个训练样本的情况下，比最先进的方法提高了30%，并在应对域偏移方面表现出卓越的鲁棒性。 AI

影响 增强了数据稀缺环境下的医学影像分析能力，有可能提高诊断准确性并降低标注成本。

排序理由 该集群包含一篇arXiv预印本，详细介绍了用于医学图像分割的新框架和方法。[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 →

VesselBridge3D框架适应基础模型以实现低数据3D血管分割

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该集群包含一篇arXiv预印本，详细介绍了用于医学图像分割的新框架和方法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Kirato Yoshihara, Yohei Sugawara, Yuta Tokuoka, Lihang Hong ·

    VesselBridge3D：用于标签高效3D血管分割的基础模型适应框架

    arXiv:2602.23782v2 Announce Type: replace-cross Abstract: State-of-the-art vessel segmentation methods typically require large-scale annotated datasets and suffer from severe performance degradation under domain shifts. In clinical practice, however, acquiring extensive annotatio…

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