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English(EN) ThreshGuide: Class-Aware Labeled-Guided Thresholding for Semi-Supervised 3D Abdominal Multi-Organ Segmentation

新框架改进半监督式3D器官分割

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了ThreshGuide，一种用于半监督式3D腹部多器官分割的新型框架。该方法通过逐类调整阈值，解决了伪标签中固定置信度阈值法的局限性。ThreshGuide利用标签数据指导从无标签数据中选择伪标签，优化了难以学习的器官分割。在FLARE2022和AMOS2022数据集上的实验表明，其具有竞争力，尤其是在具有挑战性的器官分割任务上。 AI

影响 增强了医学图像分割的半监督学习能力，有望提高复杂器官识别的诊断准确性。

排序理由 该集群包含一篇详细介绍医学图像分割新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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新框架改进半监督式3D器官分割

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该集群包含一篇详细介绍医学图像分割新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Hongyu Liu, Yinlong Wang, Lusha Li, Hui Meng ·

    ThreshGuide：用于半监督式3D腹部多器官分割的类别感知标签引导阈值法

    arXiv:2609.14943v1 Announce Type: new Abstract: Pseudo-labeling is a strong paradigm for semi-supervised medical image segmentation, yet its effectiveness is highly sensitive to confidence thresholding. In abdominal multi-organ segmentation, a fixed global threshold is particular…

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