论文标题
在半监督图像分割中解决类别的阶级不平衡：心脏MRI的研究
Addressing Class Imbalance in Semi-supervised Image Segmentation: A Study on Cardiac MRI
论文作者
论文摘要
由于数据不平衡和有限，半监督的医学图像分割方法通常无法为某些特定的尾部类带来卓越的性能。这些特定班级的培训不足可能会给产生的伪标签带来更多噪音，从而影响整体学习。为了减轻这一缺点并确定表现不佳的课程，我们建议保持一个信心阵列，以记录培训期间的班级表现。提出了这些置信分数的模糊融合，以适应每个样本中的个人置信度指标，而不是传统的合奏方法，其中为所有测试案例分配了一组预定义的固定权重。此外，我们引入了强大的班级抽样方法和动态稳定方法，以获得更好的训练策略。我们提出的方法认为所有表现不佳的类别都具有动态权重，并试图在训练过程中消除大多数噪音。在对两个心脏MRI数据集评估ACDC和MMWHS时，我们提出的方法显示出有效性和概括性，并且表现优于文献中发现的几种最先进的方法。
Due to the imbalanced and limited data, semi-supervised medical image segmentation methods often fail to produce superior performance for some specific tailed classes. Inadequate training for those particular classes could introduce more noise to the generated pseudo labels, affecting overall learning. To alleviate this shortcoming and identify the under-performing classes, we propose maintaining a confidence array that records class-wise performance during training. A fuzzy fusion of these confidence scores is proposed to adaptively prioritize individual confidence metrics in every sample rather than traditional ensemble approaches, where a set of predefined fixed weights are assigned for all the test cases. Further, we introduce a robust class-wise sampling method and dynamic stabilization for a better training strategy. Our proposed method considers all the under-performing classes with dynamic weighting and tries to remove most of the noises during training. Upon evaluation on two cardiac MRI datasets, ACDC and MMWHS, our proposed method shows effectiveness and generalizability and outperforms several state-of-the-art methods found in the literature.
