标题： 半监督医学图像分割的解耦竞争框架 显示英文标题

标题： Decoupled Competitive Framework for Semi-supervised Medical Image Segmentation

摘要： 针对医学领域标注样本不足这一关键挑战，半监督医学图像分割（SSMIS）成为一种有前景的解决方案。 具体而言，大多数遵循均值教师（MT）或双学生（DS）架构的方法都取得了可喜的结果。 然而，迄今为止，这些方法由于存在两个固有的局限性而面临性能瓶颈：\textit{例如}，即由于使用指数移动平均（EMA）机制导致的MT结构内部过度耦合问题，以及DS结构中两个学生之间严重的认知偏差，这两个问题都有可能导致效果降低，甚至最终导致模型崩溃。 为了解决这些问题，本文详细阐述了一种解耦竞争框架（DCF），该框架利用简单的竞争机制来更新EMA，从而以动态方式有效地解耦学生和教师。 此外，该框架还促进了学生之间宝贵且精确见解的无缝交换，确保了更好的学习范式。 所提出的DCF在三个公开可用的数据集上进行了严格的验证，这些数据集包括二维和三维数据集。 结果显示，我们的方法优于先前的最先进竞争对手。 代码将在https://github.com/JiaheChen2002/DCF获取。 显示英文摘要

摘要： Confronting the critical challenge of insufficiently annotated samples in medical domain, semi-supervised medical image segmentation (SSMIS) emerges as a promising solution. Specifically, most methodologies following the Mean Teacher (MT) or Dual Students (DS) architecture have achieved commendable results. However, to date, these approaches face a performance bottleneck due to two inherent limitations, \textit{e.g.}, the over-coupling problem within MT structure owing to the employment of exponential moving average (EMA) mechanism, as well as the severe cognitive bias between two students of DS structure, both of which potentially lead to reduced efficacy, or even model collapse eventually. To mitigate these issues, a Decoupled Competitive Framework (DCF) is elaborated in this work, which utilizes a straightforward competition mechanism for the update of EMA, effectively decoupling students and teachers in a dynamical manner. In addition, the seamless exchange of invaluable and precise insights is facilitated among students, guaranteeing a better learning paradigm. The DCF introduced undergoes rigorous validation on three publicly accessible datasets, which encompass both 2D and 3D datasets. The results demonstrate the superiority of our method over previous cutting-edge competitors. Code will be available at https://github.com/JiaheChen2002/DCF.