面向体积医学图像分割的通用半监督框架
Towards Generic Semi-Supervised Framework for Volumetric Medical Image Segmentation
AI总结：
本文提出一种聚合与解耦的通用半监督框架，通过扩散编码器提取分布不变特征并解耦训练过程，以解决体积医学图像分割中标注数据有限的问题，在SSL、UDA和SemiDG等场景中均优于现有方法。
摘要：
在三维医学图像中进行体积级标注是一项耗时且需要专业知识的工作。因此，利用半监督学习（SSL）技术来训练具有有限标注数据的模型日益受到关注。然而，挑战和实际应用不仅限于SSL，还扩展到无监督域适应（UDA）和半监督域泛化（SemiDG）等设置。本研究旨在开发一个能够处理所有这三种设置的通用SSL框架。我们识别出现有SSL框架中实现这一目标的两个主要障碍：1）捕获分布不变特征的能力不足；2）未标注数据容易被标注数据淹没，导致训练过程中对标注数据过拟合。为解决这些问题，我们提出了一个聚合与解耦（Aggregating & Decoupling）框架。聚合部分由一个扩散编码器组成，该编码器通过从多个分布/域的聚合信息中提取分布不变特征来构建一个公共知识集。解耦部分由三个解码器组成，这些解码器将标注和未标注数据的训练过程解耦，从而避免对标注数据、特定域和类别的过拟合。我们在四个基准数据集上评估了所提出的框架，分别用于SSL、类不平衡SSL、UDA和SemiDG。结果表明，在所有四种设置中，与最先进的方法相比，我们的方法都取得了显著改进，表明我们的框架有潜力应对更具挑战性的SSL场景。代码和模型可在 https://github.com/xmed-lab/GenericSSL 获取。
英文摘要：
Volume-wise labeling in 3D medical images is a time-consuming task that requires expertise. As a result, there is growing interest in using semi-supervised learning (SSL) techniques to train models with limited labeled data. However, the challenges and practical applications extend beyond SSL to settings such as unsupervised domain adaptation (UDA) and semi-supervised domain generalization (SemiDG). This work aims to develop a generic SSL framework that can handle all three settings. We identify two main obstacles to achieving this goal in the existing SSL framework: 1) the weakness of capturing distribution-invariant features; and 2) the tendency for unlabeled data to be overwhelmed by labeled data, leading to over-fitting to the labeled data during training. To address these issues, we propose an Aggregating & Decoupling framework. The aggregating part consists of a Diffusion encoder that constructs a common knowledge set by extracting distribution-invariant features from aggregated information from multiple distributions/domains. The decoupling part consists of three decoders that decouple the training process with labeled and unlabeled data, thus avoiding over-fitting to labeled data, specific domains and classes. We evaluate our proposed framework on four benchmark datasets for SSL, Class-imbalanced SSL, UDA and SemiDG. The results showcase notable improvements compared to state-of-the-art methods across all four settings, indicating the potential of our framework to tackle more challenging SSL scenarios. Code and models are available at: https://github.com/xmed-lab/GenericSSL.
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。
