计算机应用研究2024，Vol.41Issue(6)：1882-1887,6.DOI:10.19734/j.issn.1001-3695.2023.08.0408
基于特征级损失和可学习噪声的医学图像域泛化方法
Domain generalization method for medical images based on feature-level loss and learnable noise
摘要
Abstract
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.关键词
医学图像分割/域泛化/表示学习/特征级损失/可学习噪声Key words
medical image segmentation/domain generalization/representation learning/feature-level loss/learnable noise分类
信息技术与安全科学引用本文复制引用
史轶伦,于磊,徐巧枝..基于特征级损失和可学习噪声的医学图像域泛化方法[J].计算机应用研究,2024,41(6):1882-1887,6.基金项目
内蒙古自治区自然科学基金资助项目(2021MS06031,2022ZD05) （2021MS06031,2022ZD05）
内蒙古师范大学基本科研业务费专项资金资助项目(2022JBYJ034) （2022JBYJ034）
内蒙古自治区"十四五"社会公益领域重点研发和成果转化计划项目(2022YFSHO010) （2022YFSHO010）
无穷维哈密顿系统及其算法应用教育部重点实验室开放课题资助项目(2023KFYB06) （2023KFYB06）
