用跨金字塔一致性正则化，让少量标注的医疗影像数据也能训练出高精度分割模型。
Cross-pyramid consistency regularization for semi-supervised medical image segmentation
- 设计双分支金字塔网络，通过两个略有不同的解码器生成多尺度预测
- 在公开数据集上性能优于五种主流自监督方法，接近最新先进水平
- 适合医疗图像分割中标签稀缺但需高精度的任务场景
半监督学习（SSL）在仅有少量精心标注数据和大量未标注数据的前提下，能够训练出高性能模型。本文提出一种混合一致性学习方法，通过跨金字塔一致性正则化（CPCR）有效利用未标注数据进行半监督医学图像分割。首先，设计了一种双分支金字塔网络（DBPNet），包含一个编码器和两个结构略有差异的解码器，分别在多个分辨率层级上生成扰动的辅助预测。其次，提出一种名为CPCR的学习策略，将现有的一致性学习与不确定性最小化方法结合，并引入新颖的正则化项：将软标签设定扩展至双解码器间的金字塔预测，支持深层层次特征中的知识蒸馏。实验表明，采用CPCR的DBPNet在公开基准数据集上性能优于五种主流自监督学习方法，且与近期先进方法相当。
原文摘要 · Abstract (English)
Semi-supervised learning (SSL) enables training of powerful models with the assumption of limited, carefully labelled data and a large amount of unlabeled data to support the learning. In this paper, we propose a hybrid consistency learning approach to effectively exploit unlabeled data for semi-supervised medical image segmentation by leveraging Cross-Pyramid Consistency Regularization (CPCR) between two decoders. First, we design a hybrid Dual Branch Pyramid Network (DBPNet), consisting of an encoder and two decoders that differ slightly, each producing a pyramid of perturbed auxiliary predictions across multiple resolution scales. Second, we present a learning strategy for this network named CPCR that combines existing consistency learning and uncertainty minimization approaches on the main output predictions of decoders with our novel regularization term. More specifically, in this term, we extend the soft-labeling setting to pyramid predictions across decoders to support knowledge distillation in deep hierarchical features. Experimental results show that DBPNet with CPCR outperforms five state-of-the-art self-supervised learning methods and has comparable performance with recent ones on a public benchmark dataset.
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