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English(EN) ESRVS: Extreme Semi-Supervised Retinal Vessel Segmentation with a Single Annotated Image

ESRVS方法以最小监督实现高精度视网膜血管分割

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了ESRVS，一种新颖的视网膜血管分割方法，仅需一张标注图像和一组未标注图像。该方法利用基础模型标签传播，使用DINOv3特征和受物理启发的先验知识生成初始伪标签。ESRVS随后通过加权伪标签训练和对抗性精炼来优化监督，在多个公共数据集上取得了最先进的成果，并与全监督方法相比保持了很高的性能百分比。 AI

影响 展示了标签高效医学图像分割的巨大潜力，减少了对昂贵专家标注的依赖。

排序理由 详细介绍医学图像分析新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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ESRVS方法以最小监督实现高精度视网膜血管分割

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详细介绍医学图像分析新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Mingzhi Xu, Yizhe Zhang ·

    ESRVS：使用单张标注图像的极端半监督视网膜血管分割

    arXiv:2607.24453v1 Announce Type: cross Abstract: Learning from minimal human supervision is a long-standing goal in medical image analysis, where dense expert annotations are costly. We study retinal vessel segmentation in an extreme semi-supervised setting with one annotated im…

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