基于特征级损失和可学习噪声的医学图像域泛化方法

在医学图像分割任务中，域偏移问题会影响训练好的分割模型在未见域的性能，因此，提高模型泛化性对于医学图像智能模型的实际应用至关重要。表示学习是目前解决域泛化问题的主流方法之一，大多使用图像级损失和一致性损失来监督图像生成，但是对医学图像微小形态特征的偏差不够敏感，会导致生成图像边缘不清晰，影响模型后续学习。为了提高模型的泛化性，提出一种半监督的基于特征级损失和可学习噪声的医学图像域泛化分割模型FLLN-DG，首先引入特征级损失改善生成图像边界不清晰的问题，其次引入可学习噪声组件，进一步增加数据多样性，提升模型泛化性。与基线模型相比，FLLN-DG在未见域的性能提升2%~4%，证明了特征级损失和可学习噪声组件的有效性，与nnUNet，SDNet+AUG，LDDG，SAML，Meta等典型域泛化模型相比，FLLN-DG也表现出更优越的性能。

Domain generalization method for medical images based on feature-level loss and learnable noise

In medical image segmentation tasks, the domain shift problem affects the performance of trained segmentation models in the unseen domain. Therefore, improving model generalization is crucial for the practical application of intelligent models for medical images. Representation learning is currently one of the dominant methods for solving domain generalization problems, mostly using image-level loss and consistency loss to supervise image generation. However, it is not sensitive enough to the deviation of small morphological features of medical images, which can lead to unclear edges of the generated images and affect the subsequent learning of the model. In order to improve the generalization of the model, this paper proposed a semi-supervised feature-level loss and learnable noise domain generalization(FLLN-DG) method for medical image segmentation. Firstly, the introduction of feature level loss improved the problem of unclear boundaries of the generated images. Secondly, the introduction of the learnable noise components further increased the data diversity and improved the model generalization. Compared with the baseline model, FLLN-DG improved the performance in the unseen domain by 2% to 4%, which demonstrated the effectiveness of to feature-level loss and to learnable noise components. FLLN-DG also has the best generalization and segmentation results compared to typical generalization models such as nnUNet, SDNet+AUG, LDDG, SAML, and Meta.