论文标题
半监督医学图像细分的一种令人尴尬的简单一致性正则化方法
An Embarrassingly Simple Consistency Regularization Method for Semi-Supervised Medical Image Segmentation
论文作者
论文摘要
像素级注释的稀缺性是医学图像分割任务中普遍的问题。在本文中，我们引入了一种新型的正则化策略，涉及半监督医学图像分割的基于插值的混合。提出的方法是一种新的一致性正则化策略，该策略鼓励分割两个未标记数据，以与这些数据的分割图的插值保持一致。该方法代表一种特定类型的数据自适应正则化范式，有助于最大程度地减少在高置信值下标记的数据的过度拟合。所提出的方法比对抗性和生成模型是有利的，因为它不需要其他计算。通过对两个公开可用的MRI数据集进行评估：ACDC和MMWHS，实验结果证明了与现有的半监督模型相比，该方法的优越性。代码可在以下网址找到：https：//github.com/hritam-98/ict-medseg
The scarcity of pixel-level annotation is a prevalent problem in medical image segmentation tasks. In this paper, we introduce a novel regularization strategy involving interpolation-based mixing for semi-supervised medical image segmentation. The proposed method is a new consistency regularization strategy that encourages segmentation of interpolation of two unlabelled data to be consistent with the interpolation of segmentation maps of those data. This method represents a specific type of data-adaptive regularization paradigm which aids to minimize the overfitting of labelled data under high confidence values. The proposed method is advantageous over adversarial and generative models as it requires no additional computation. Upon evaluation on two publicly available MRI datasets: ACDC and MMWHS, experimental results demonstrate the superiority of the proposed method in comparison to existing semi-supervised models. Code is available at: https://github.com/hritam-98/ICT-MedSeg
