基于特征级损失和可学习噪声的医学图像域泛化方法OA北大核心CSTPCD

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 devia-tion 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 fea-ture-level loss and learnable noise domain generalization(FLLN-DG)method for medical image segmentation.Firstly,the intro-duction 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 improves the performance in the unseen domain by 2％to 4％,which demonstrates 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.