Abstract
Deep learning models often encounter challenges in making accurate inferences when there are domain shifts between the source and target data. This issue is particularly pronounced in clinical settings due to the scarcity of annotated data resulting from the professional and private nature of medical data. Despite the existence of decent solutions, many of them are hindered in clinical settings due to limitations in data collection and computational complexity. To tackle domain shifts in data-scarce medical scenarios, we propose a Random frequency filtering enabled Single-source Domain Generalization algorithm (RaffeSDG), which promises robust out-of-domain inference with segmentation models trained on a single-source domain. A filter-based data augmentation strategy is first proposed to promote domain variability within a single-source domain by introducing variations in frequency space and blending homologous samples. Then Gaussian filter-based structural saliency is also leveraged to learn robust representations across augmented samples, further facilitating the training of generalizable segmentation models. To validate the effectiveness of RaffeSDG, we conducted extensive experiments involving out-of-domain inference on segmentation tasks for three human tissues imaged by four diverse modalities. Through thorough investigations and comparisons, compelling evidence was observed in these experiments, demonstrating the potential and generalizability of RaffeSDG. The code is available at this https URL.
Abstract (translated)
深度学习模型在目标数据和源数据之间存在领域转移时，通常会做出准确的推理。这个问题在临床环境中尤为突出，因为医疗数据的非专业和私有的性质导致缺乏注释数据。尽管存在可行的解决方案，但它们在临床环境中因数据收集和计算复杂性的限制而受到阻碍。为解决数据稀少的医疗场景中的领域转移问题，我们提出了一个由随机频率过滤启发的单源领域泛化算法（RaffeSDG），它承诺在单源领域上训练的分割模型的稳健离域推理能力。首先提出了一种基于滤波器的数据增强策略，通过引入频率空间中的变化和混合同样样本来提高领域差异。然后，利用高斯滤波器进行结构重要性学习，以学习增强样本中的稳健表示，进一步推动泛化分割模型的训练。为了验证RaffeSDG的有效性，我们进行了涉及四个不同模态成像的人体三种组织的分割任务的大量实验。通过深入调查和比较，在这些实验中观察到了令人信服的证据，证明了RaffeSDG的潜力和泛化能力。代码可在此处访问：https://www.kaggle.com/raffeym/raffeymdg
URL
https://arxiv.org/abs/2405.01228
