用图结构聚类提升医疗图像分割的少样本效果
GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation
- 构建统一深度模型，联合建模图像的图结构信息
- 在三个基准数据集上均超越当前最优方法
- 适合医疗图像少标注场景下的分割任务
半监督学习（SSL）在医疗图像分割（MIS）中取得显著进展，尤其在标注数据有限的情况下，显著提升了数据利用效率。以往方法主要关注复杂的训练策略以利用无标签数据，却忽视了图结构信息的重要性。与现有方法不同，本文提出一种基于图的聚类方法（GraphCL），通过统一深度模型联合建模图数据结构。该方法首次将数据结构信息引入半监督医疗图像分割（SSMIS）。为获得跨图的聚类特征，整合局部图像特征间的成对相似性与原始特征作为输入。在三个标准基准数据集上的大量实验表明，所提GraphCL算法优于当前最优的半监督医学图像分割方法。
原文摘要 · Abstract (English)
Semi-supervised learning (SSL) has made notable advancements in medical image segmentation (MIS), particularly in scenarios with limited labeled data and significantly enhancing data utilization efficiency. Previous methods primarily focus on complex training strategies to utilize unlabeled data but neglect the importance of graph structural information. Different from existing methods, we propose a graph-based clustering for semi-supervised medical image segmentation (GraphCL) by jointly modeling graph data structure in a unified deep model. The proposed GraphCL model enjoys several advantages. Firstly, to the best of our knowledge, this is the first work to model the data structure information for semi-supervised medical image segmentation (SSMIS). Secondly, to get the clustered features across different graphs, we integrate both pairwise affinities between local image features and raw features as inputs. Extensive experimental results on three standard benchmarks show that the proposed GraphCL algorithm outperforms state-of-the-art semi-supervised medical image segmentation methods.
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