论文标题
可通过随机振幅混合和特定域特异性图像修复进行概括的医学图像分割
Generalizable Medical Image Segmentation via Random Amplitude Mixup and Domain-Specific Image Restoration
论文作者
论文摘要
对于医学图像分析，在一个或几个领域训练的分割模型由于不同数据采集策略之间的差异而缺乏概括性的能力，无法看不见域。我们认为，分割性能的退化主要归因于源域和域移位。为此，我们提出了一种新型的可推广医学图像分割方法。具体而言，我们通过将分割模型与自学域特异性图像恢复（DSIR）模块相结合，将方法设计为多任务范式。我们还设计了一个随机的振幅混音（RAM）模块，该模块包含不同域图像的低级频率信息以合成新图像。为了指导我们的模型抵抗域转移，我们引入了语义一致性损失。我们证明了我们在医学图像中两个公共可推广的分割基准上的方法的性能，这证明了我们的方法可以实现最先进的性能。
For medical image analysis, segmentation models trained on one or several domains lack generalization ability to unseen domains due to discrepancies between different data acquisition policies. We argue that the degeneration in segmentation performance is mainly attributed to overfitting to source domains and domain shift. To this end, we present a novel generalizable medical image segmentation method. To be specific, we design our approach as a multi-task paradigm by combining the segmentation model with a self-supervision domain-specific image restoration (DSIR) module for model regularization. We also design a random amplitude mixup (RAM) module, which incorporates low-level frequency information of different domain images to synthesize new images. To guide our model be resistant to domain shift, we introduce a semantic consistency loss. We demonstrate the performance of our method on two public generalizable segmentation benchmarks in medical images, which validates our method could achieve the state-of-the-art performance.
