研究人员开发了ZScribbleSeg，一个旨在通过高效的涂鸦标注改进医学图像分割的新框架。该方法通过最大化从有限涂鸦输入中获得的监督来解决完全标注数据集的劳动密集型问题。ZScribbleSeg 结合了空间关系和形状约束，利用 EM 算法进行准确的类别比例估计，并在六个不同的分割任务中展示了具有竞争力的性能。 AI
影响 提供了一种更有效的医学图像分割方法，有可能降低标注成本并提高模型准确性。
排序理由 这是一篇详细介绍新型图像分割框架的研究论文。
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arXiv:2605.06266v1 Announce Type: new Abstract: Curating fully annotated datasets for medical image segmentation is labour-intensive and expertise-demanding. To alleviate this problem, prior studies have explored scribble annotations for weakly supervised segmentation. Existing s…
Curating fully annotated datasets for medical image segmentation is labour-intensive and expertise-demanding. To alleviate this problem, prior studies have explored scribble annotations for weakly supervised segmentation. Existing solutions mainly compute losses on annotated area…