Mix-up is a key technique for consistency regularization-based semi-supervised learning methods, generating strong-perturbed samples for strong-weak pseudo-supervision. Existing mix-up operations are performed either randomly or with predefined rules, such as replacing low-confidence patches with high-confidence ones. The former lacks control over the perturbation degree, leading to overfitting on randomly perturbed samples, while the latter tends to generate images with trivial perturbations, both of which limit the effectiveness of consistency learning. This paper aims to answer the following question: How can image mix-up perturbation be adaptively performed during training? To this end, we propose an Adaptive Mix algorithm (AdaMix) for image mix-up in a self-paced learning manner. Given that, in general, a model's performance gradually improves during training, AdaMix is equipped with a self-paced curriculum that, in the initial training stage, provides relatively simple perturbed samples and then gradually increases the difficulty of perturbed images by adaptively controlling the perturbation degree based on the model's learning state estimated by a self-paced regularize. We develop three frameworks with our AdaMix, i.e., AdaMix-ST, AdaMix-MT, and AdaMix-CT, for semi-supervised medical image segmentation. Extensive experiments on three public datasets, including both 2D and 3D modalities, show that the proposed frameworks are capable of achieving superior performance. For example, compared with the state-of-the-art, AdaMix-CT achieves relative improvements of 2.62% in Dice and 48.25% in average surface distance on the ACDC dataset with 10% labeled data. The results demonstrate that mix-up operations with dynamically adjusted perturbation strength based on the segmentation model's state can significantly enhance the effectiveness of consistency regularization.

翻译：混合是半监督学习中基于一致性正则化的关键方法，它通过生成强扰动样本来实现强弱伪监督。现有的混合操作要么随机执行，要么遵循预定义规则，例如用高置信度图像块替换低置信度图像块。前者缺乏对扰动程度的控制，导致模型在随机扰动样本上过拟合；后者则倾向于生成扰动微不足道的图像，两者均限制了半监督学习的效果。本文旨在回答以下问题：如何在训练过程中自适应地执行图像混合扰动？为此，我们提出了一种自适应混合算法（AdaMix），以自步学习的方式实现图像混合。考虑到模型性能在训练过程中通常会逐步提升，AdaMix配备了一个自步课程：在训练初期提供相对简单的扰动样本，随后通过基于自步正则器估计的模型学习状态来自适应控制扰动程度，逐步增加扰动图像的难度。我们基于AdaMix开发了三个框架，即AdaMix-ST、AdaMix-MT和AdaMix-CT，用于半监督医学图像分割。在包含2D和3D模态的三个公开数据集上进行的大量实验表明，所提出的框架能够取得优异的性能。例如，在仅使用10%标注数据的ACDC数据集上，AdaMix-CT相比现有最优方法在Dice系数和平均表面距离上分别实现了2.62%和48.25%的相对提升。实验结果表明，基于分割模型状态动态调整扰动强度的混合操作能够显著提升一致性正则化的有效性。