arXiv 2305.162142023-05-25cs.CV

用于半监督医学图像分割的自感知与跨样本原型学习

Self-aware and Cross-sample Prototypical Learning for Semi-supervised Medical Image Segmentation

Xidian University（西安电子科技大学）；The First Affiliated Hospital of Xi’an Jiaotong University（西安交通大学第一附属医院）；AIQ；Warren Alpert Medical School of Brown University（布朗大学沃伦·阿尔珀特医学院）

Zhenxi Zhang, Ran Ran, Chunna Tian, Heng Zhou, Xin Li, Fan Yang, Zhicheng Jiao

备注：: 14 pages, Early accepted in MICCAI 2023, code will be released soon

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AI总结：

针对半监督医学图像分割中预测多样性不足和训练不稳定问题，提出SCP-Net，通过跨样本原型学习、自感知一致性学习和双重损失重加权，在ACDC和PROMISE12数据集上取得优于现有方法的性能。

摘要：

一致性学习在半监督医学图像分割中起着至关重要的作用，因为它能够在利用大量未标注数据的同时，有效利用有限的标注数据。一致性学习的有效性和效率受到预测多样性和训练稳定性的挑战，而现有研究往往忽视了这些问题。同时，用于训练的标注数据数量有限，往往不足以形成伪标签的类内紧凑性和类间差异性。为了解决这些问题，我们提出了一种自感知与跨样本原型学习方法（SCP-Net），通过利用来自多个输入的更广泛的语义信息来增强一致性学习中预测的多样性。此外，我们引入了一种自感知一致性学习方法，利用未标注数据来提高每个类别内伪标签的紧凑性。并且，在跨样本原型一致性学习方法中集成了双重损失重新加权方法，以提高我们模型的可靠性和稳定性。在ACDC数据集和PROMISE12数据集上的大量实验验证了SCP-Net优于其他最先进的半监督分割方法，并且与有限的监督训练相比取得了显著的性能提升。我们的代码即将发布。

英文摘要：

Consistency learning plays a crucial role in semi-supervised medical image segmentation as it enables the effective utilization of limited annotated data while leveraging the abundance of unannotated data. The effectiveness and efficiency of consistency learning are challenged by prediction diversity and training stability, which are often overlooked by existing studies. Meanwhile, the limited quantity of labeled data for training often proves inadequate for formulating intra-class compactness and inter-class discrepancy of pseudo labels. To address these issues, we propose a self-aware and cross-sample prototypical learning method (SCP-Net) to enhance the diversity of prediction in consistency learning by utilizing a broader range of semantic information derived from multiple inputs. Furthermore, we introduce a self-aware consistency learning method that exploits unlabeled data to improve the compactness of pseudo labels within each class. Moreover, a dual loss re-weighting method is integrated into the cross-sample prototypical consistency learning method to improve the reliability and stability of our model. Extensive experiments on ACDC dataset and PROMISE12 dataset validate that SCP-Net outperforms other state-of-the-art semi-supervised segmentation methods and achieves significant performance gains compared to the limited supervised training. Our code will come soon.

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。