边界感知对比学习用于半监督细胞核实例分割
Boundary-aware Contrastive Learning for Semi-supervised Nuclei Instance Segmentation
AI总结：
针对半监督细胞核分割中伪标签边界噪声问题，提出边界感知对比学习网络，包含低分辨率去噪与跨RoI对比学习模块，有效提升分割性能。
摘要：
半监督分割方法在自然场景中已展现出有前景的结果，为减少对人工标注的依赖提供了解决方案。然而，由于细胞核与组织之间微妙的颜色差异以及细胞核之间显著的形态学变化，这些方法在直接应用于病理图像时面临重大挑战。因此，生成的伪标签通常包含大量噪声，尤其是在细胞核边界处。为解决上述问题，本文提出了一种边界感知对比学习网络，用于在半监督细胞核分割任务中对边界噪声进行去噪。该模型有两个关键设计：低分辨率去噪（LRD）模块和跨感兴趣区域对比学习（CRC）模块。LRD通过伪标签去噪提高细胞核边界的平滑度，CRC通过边界特征对比学习增强前景与背景之间的区分度。我们进行了大量实验，以证明我们提出的方法相对于现有半监督实例分割方法的优越性。
英文摘要：
Semi-supervised segmentation methods have demonstrated promising results in natural scenarios, providing a solution to reduce dependency on manual annotation. However, these methods face significant challenges when directly applied to pathological images due to the subtle color differences between nuclei and tissues, as well as the significant morphological variations among nuclei. Consequently, the generated pseudo-labels often contain much noise, especially at the nuclei boundaries. To address the above problem, this paper proposes a boundary-aware contrastive learning network to denoise the boundary noise in a semi-supervised nuclei segmentation task. The model has two key designs: a low-resolution denoising (LRD) module and a cross-RoI contrastive learning (CRC) module. The LRD improves the smoothness of the nuclei boundary by pseudo-labels denoising, and the CRC enhances the discrimination between foreground and background by boundary feature contrastive learning. We conduct extensive experiments to demonstrate the superiority of our proposed method over existing semi-supervised instance segmentation methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
