Abstract
Medical image segmentation typically demands extensive dense annotations for model training, which is both time-consuming and skill-intensive. To mitigate this burden, exemplar-based medical image segmentation methods have been introduced to achieve effective training with only one annotated image. In this paper, we introduce a novel Cross-model Mutual learning framework for Exemplar-based Medical image Segmentation (CMEMS), which leverages two models to mutually excavate implicit information from unlabeled data at multiple granularities. CMEMS can eliminate confirmation bias and enable collaborative training to learn complementary information by enforcing consistency at different granularities across models. Concretely, cross-model image perturbation based mutual learning is devised by using weakly perturbed images to generate high-confidence pseudo-labels, supervising predictions of strongly perturbed images across models. This approach enables joint pursuit of prediction consistency at the image granularity. Moreover, cross-model multi-level feature perturbation based mutual learning is designed by letting pseudo-labels supervise predictions from perturbed multi-level features with different resolutions, which can broaden the perturbation space and enhance the robustness of our framework. CMEMS is jointly trained using exemplar data, synthetic data, and unlabeled data in an end-to-end manner. Experimental results on two medical image datasets indicate that the proposed CMEMS outperforms the state-of-the-art segmentation methods with extremely limited supervision.
Abstract (translated)
医学图像分割通常需要大量标注数据来训练模型，这既耗时又需要专业技能。为了减轻这一负担，基于示例的医学图像分割方法已经引入，通过仅使用一个标注图像实现有效的训练。在本文中，我们提出了一个新颖的跨模型 mutual learning 框架，即 Cross-Model Mutual Learning (CMEMS)，利用两个模型在多个粒度上共同挖掘未标注数据的隐含信息。CMEMS 可以通过在模型之间强制不同粒度上的一致性来消除确认偏差，并允许在模型之间进行协作训练，通过在模型之间强制不同粒度上的一致性来学习互补信息。具体来说，跨模型图像扰动基于 mutual learning 通过使用弱化扰动的图像生成高置信度的伪标签，监督模型中强烈扰动的图像的预测。这种方法在图像粒度上实现了预测一致性的共同追求。此外，跨模型多级特征扰动基于 mutual learning 是由让伪标签监督不同分辨率扰动的多级特征的预测而设计的。这可以拓宽扰动空间，增强框架的鲁棒性。CMEMS 采用示例数据、合成数据和未标注数据在端到端方式上进行联合训练。在两个医学图像数据集上的实验结果表明，与最先进的分割方法相比，CMEMS 在极其有限的监督下取得了卓越的性能。
URL
https://arxiv.org/abs/2404.11812
