重新思考半监督医学图像分割中的数据扰动与模型稳定化
Rethinking Data Perturbation and Model Stabilization for Semi-supervised Medical Image Segmentation
AI总结：
该研究针对半监督医学图像分割核心挑战，提出DPMS方法，通过强增强数据扰动、双前向传播与归一化统计量动量更新策略，在多个公开数据集上取得新的最优性能。
摘要：
半监督医学图像分割（SSMIS）研究近年来进展迅速。由于标注数据有限，SSMIS方法主要聚焦于有效利用无标注数据以提升分割性能。然而，尽管现有最优方法表现可观，但它们往往优先整合复杂技术与损失项，而非直接解决半监督场景的核心挑战。我们认为，SSMIS的关键在于在无标注数据上生成充分且适当的预测分歧。为此，我们强调数据扰动与模型稳定化在半监督分割中的重要性，并提出一种简单却有效的方法以显著提升SSMIS性能，该方法名为DPMS。具体而言，我们首先从数据、模型、损失三个不同视角重新审视SSMIS，并对相应策略开展全面研究以检验其有效性。基于这些检验，我们提出DPMS，其采用普通的师生框架，搭配标准监督损失与无监督一致性损失。为生成适当的预测分歧，DPMS通过强增强对无标注数据进行扰动，以大幅扩大预测分歧。另一方面，在应用强增强时使用指数移动平均（EMA）教师模型未必能提升性能。DPMS进一步采用前向传播两次与归一化统计量动量更新策略，有效稳定无标注数据上的训练。尽管方法简单，DPMS在公开2D ACDC数据集与3D LA数据集的多种半监督设置下均取得了新的最优性能，例如在标注占比5%的ACDC数据集上，相较此前的最优方法取得了22.62%的显著提升。
英文摘要：
Studies on semi-supervised medical image segmentation (SSMIS) have seen fast progress recently. Due to the limited labelled data, SSMIS methods mainly focus on effectively leveraging unlabeled data to enhance the segmentation performance. However, despite their promising performance, current state-of-the-art methods often prioritize integrating complex techniques and loss terms rather than addressing the core challenges of semi-supervised scenarios directly. We argue that the key to SSMIS lies in generating substantial and appropriate prediction disagreement on unlabeled data. To this end, we emphasize the crutiality of data perturbation and model stabilization in semi-supervised segmentation, and propose a simple yet effective approach to boost SSMIS performance significantly, dubbed DPMS. Specifically, we first revisit SSMIS from three distinct perspectives: the data, the model, and the loss, and conduct a comprehensive study of corresponding strategies to examine their effectiveness. Based on these examinations, we then propose DPMS, which adopts a plain teacher-student framework with a standard supervised loss and unsupervised consistency loss. To produce appropriate prediction disagreements, DPMS perturbs the unlabeled data via strong augmentations to enlarge prediction disagreements considerably. On the other hand, using EMA teacher when strong augmentation is applied does not necessarily improve performance. DPMS further utilizes a forwarding-twice and momentum updating strategies for normalization statistics to stabilize the training on unlabeled data effectively. Despite its simplicity, DPMS can obtain new state-of-the-art performance on the public 2D ACDC and 3D LA datasets across various semi-supervised settings, e.g. obtaining a remarkable 22.62% improvement against previous SOTA on ACDC with 5% labels.
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