使用多尺度对比正则化的涂鸦监督细胞分割
Scribble-supervised Cell Segmentation Using Multiscale Contrastive Regularization
AI总结：
针对涂鸦监督细胞分割易过拟合的问题，提出多尺度对比正则化方法，通过提取网络中间层特征计算对比损失以分离不同尺度结构，有效提升了S2L的分割性能并达到监督学习水平。
摘要：
当前最先进的基于监督深度学习的分割方法在医学图像分割任务中表现出卓越的性能。然而，此类监督方法需要完全标注的像素级真实标签，获取这些标签既费时又费力。最近，Scribble2Label (S2L) 证明仅使用少量涂鸦结合自监督学习即可在无全标注的情况下生成准确的分割结果。然而，由于涂鸦相对较小，模型容易过拟合，且结果可能偏向于涂鸦的选择。在本研究中，我们通过为 S2L 引入一种新颖的多尺度对比正则化项来解决此问题。核心思想是从神经网络的中间层提取特征以计算对比损失，从而有效分离不同尺度的结构。为验证方法的有效性，我们在 Data Science Bowl 2018 和 MoNuSeg 等知名数据集上进行了消融研究。结果表明，所提出的多尺度对比损失能有效提升 S2L 的性能，使其达到与监督学习分割方法相当的水平。
英文摘要：
Current state-of-the-art supervised deep learning-based segmentation approaches have demonstrated superior performance in medical image segmentation tasks. However, such supervised approaches require fully annotated pixel-level ground-truth labels, which are labor-intensive and time-consuming to acquire. Recently, Scribble2Label (S2L) demonstrated that using only a handful of scribbles with self-supervised learning can generate accurate segmentation results without full annotation. However, owing to the relatively small size of scribbles, the model is prone to overfit and the results may be biased to the selection of scribbles. In this work, we address this issue by employing a novel multiscale contrastive regularization term for S2L. The main idea is to extract features from intermediate layers of the neural network for contrastive loss so that structures at various scales can be effectively separated. To verify the efficacy of our method, we conducted ablation studies on well-known datasets, such as Data Science Bowl 2018 and MoNuSeg. The results show that the proposed multiscale contrastive loss is effective in improving the performance of S2L, which is comparable to that of the supervised learning segmentation method.
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。
