Generalizability in deep neural networks plays a pivotal role in medical image segmentation. However, deep learning-based medical image analyses tend to overlook the importance of frequency variance, which is critical element for achieving a model that is both modality-agnostic and domain-generalizable. Additionally, various models fail to account for the potential information loss that can arise from multi-task learning under deep supervision, a factor that can impair the model representation ability. To address these challenges, we propose a Modality-agnostic Domain Generalizable Network (MADGNet) for medical image segmentation, which comprises two key components: a Multi-Frequency in Multi-Scale Attention (MFMSA) block and Ensemble Sub-Decoding Module (E-SDM). The MFMSA block refines the process of spatial feature extraction, particularly in capturing boundary features, by incorporating multi-frequency and multi-scale features, thereby offering informative cues for tissue outline and anatomical structures. Moreover, we propose E-SDM to mitigate information loss in multi-task learning with deep supervision, especially during substantial upsampling from low resolution. We evaluate the segmentation performance of MADGNet across six modalities and fifteen datasets. Through extensive experiments, we demonstrate that MADGNet consistently outperforms state-of-the-art models across various modalities, showcasing superior segmentation performance. This affirms MADGNet as a robust solution for medical image segmentation that excels in diverse imaging scenarios. Our MADGNet code is available in GitHub Link.

翻译：深度神经网络在医学图像分割中的泛化能力具有关键作用。然而，基于深度学习的医学图像分析往往忽视了频率变化的重要性，而这对实现模态无关且域泛化的模型至关重要。此外，现有模型未能充分考虑深度监督下多任务学习可能产生的信息损失，这一因素会削弱模型表征能力。为解决上述挑战，我们提出一种用于医学图像分割的模态无关域泛化网络（MADGNet），该网络包含两个核心组件：多尺度多频率注意力模块（MFMSA）和集成子解码模块（E-SDM）。MFMSA模块通过融合多频率与多尺度特征优化空间特征提取过程，尤其在边界特征捕获方面表现出色，从而为组织轮廓与解剖结构提供有效线索。同时，我们提出E-SDM以缓解深度监督下多任务学习中的信息损失问题，尤其在低分辨率大幅上采样过程中效果显著。我们在六种模态与十五个数据集上评估了MADGNet的分割性能。通过大量实验证明，MADGNet在多种模态下均持续超越现有最优模型，展现出卓越的分割性能。这证实了MADGNet作为医学图像分割鲁棒解决方案的优异表现，能够胜任多样化的成像场景。MADGNet代码已开源至GitHub链接。