In weakly-supervised semantic segmentation, obtaining the class activation maps for pseudo masks is crucial. Since multiple organs appear in the same medical image, it is reasonable to obtain the activation maps of each organ by the organ-level features instead of the image-level features. The image-level features are decomposed into the organ-level features, yet the prior anatomical knowledge makes a spurious association between the image-level and organ-level features. To this end, we apply the causal intervention to cut off the spurious association and propose a novel deconfounded multi-organ weakly-supervised semantic segmentation (DeMos) method. Based on the original class activation mapping (CAM) method, the model is retrained to learn the deconfounded features of each organ via cross-attention, and we approximate the expectation of the intervention instead of the traditional likelihood. When the model converges, we extract the activation maps by CAM. Our method not only generates high-quality pseudo masks on the CHAOS, ACDC and ProMRI datasets, but is also applicable to other CAM variants. Furthermore, with the refinement, DeMos achieves the dice similarity coefficient of 93.26% on the task of the left ventricle segmentation, which outperforms the state-of-the-art methods.

中文翻译：

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通过因果干预解混多器官弱监督语义分割

在弱监督语义分割中，获取伪掩模的类激活图至关重要。由于同一医学图像中出现多个器官，因此通过器官级特征而不是图像级特征来获取每个器官的激活图是合理的。图像级特征被分解为器官级特征，但先验解剖知识在图像级特征和器官级特征之间建立了虚假关联。为此，我们应用因果干预来切断虚假关联，并提出了一种新颖的去混杂多器官弱监督语义分割（DeMos）方法。基于原始的类激活映射（CAM）方法，模型被重新训练以通过交叉注意力学习每个器官的去混杂特征，并且我们近似干预的期望而不是传统的可能性。当模型收敛时，我们通过 CAM 提取激活图。我们的方法不仅可以在 CHAOS、ACDC 和 ProMRI 数据集上生成高质量的伪掩模，而且还适用于其他 CAM 变体。此外，通过细化，DeMos 在左心室分割任务上实现了 93.26% 的骰子相似系数，优于最先进的方法。