提出解耦竞争框架，解决医学图像分割中标注数据少的难题
Decoupled Competitive Framework for Semi-supervised Medical Image Segmentation
- 用动态竞争机制解耦师生关系，避免模型过拟合
- 在三个公开数据集上超越现有最优方法，2D/3D均有效
- 适合医疗图像分割研究者，尤其关注半监督学习场景
针对医学领域标注样本不足的挑战，半监督医学图像分割（SSMIS）成为有前景的解决方案。尽管基于均值教师（MT）或双学生（DS）结构的方法已取得良好效果，但其仍受限于两大内在问题：MT结构因采用指数移动平均（EMA）机制导致师生过度耦合，而DS结构中两学生间存在严重认知偏差，二者均可能降低模型性能，甚至引发模型崩溃。为此，本文提出解耦竞争框架（DCF），通过简单竞争机制动态更新EMA，实现师生关系的解耦；同时促进学生间高质量、高精度知识的无缝传递，构建更优的学习范式。DCF在三个公开数据集（涵盖2D与3D）上进行了严格验证，结果表明该方法优于此前顶尖基准。代码将开源于https://github.com/JiaheChen2002/DCF。
原文摘要 · Abstract (English)
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.
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