English(EN) Bi-Level Collaborative Learning for Few-Shot Scribble-Supervised Medical Image Segmentation

新框架改进少样本医学图像分割

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-29 04:00

研究人员开发了一种新颖的双层协同学习框架，以解决少样本涂鸦监督医学图像分割的挑战。该方法利用一个可学习的超像素模型为低层分割模型提供结构先验，然后将解剖学语义反馈给上层。这种双向交互生成可靠的伪标签并提高分割精度，在ACDC和Prostate数据集上优于现有方法。 AI

影响这项研究可能带来更高效、更准确的医学图像分析工具，尤其是在标注数据有限的情况下。

排序理由这是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]

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

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arXiv cs.CV TIER_1 English(EN) · Xiang-Xiang Su, Yufan Ye, Yihang Zheng, Min Gan, Guang-Yong Chen · 2026-07-29 04:00

用于少样本涂鸦监督医学图像分割的双层协同学习

arXiv:2607.25432v1 Announce Type: new Abstract: Scribble annotations offer an efficient alternative to costly pixel-wise labeling for medical image segmentation, yet in real clinical scenarios, scribble-annotated samples are often still limited, imposing the dual challenges of sp…