Single-source domain generalization (SDG) in medical image segmentation (MIS)
aims to generalize a model using data from only one source domain to segment
data from an unseen target domain. Despite substantial advances in SDG with
data augmentation, existing methods often fail to fully consider the details
and uncertain areas prevalent in MIS, leading to mis-segmentation. This paper
proposes a Fourier-based semantic augmentation method called FIESTA using
uncertainty guidance to enhance the fundamental goals of MIS in an SDG context
by manipulating the amplitude and phase components in the frequency domain. The
proposed Fourier augmentative transformer addresses semantic amplitude
modulation based on meaningful angular points to induce pertinent variations
and harnesses the phase spectrum to ensure structural coherence. Moreover,
FIESTA employs epistemic uncertainty to fine-tune the augmentation process,
improving the ability of the model to adapt to diverse augmented data and
concentrate on areas with higher ambiguity. Extensive experiments across three
cross-domain scenarios demonstrate that FIESTA surpasses recent
state-of-the-art SDG approaches in segmentation performance and significantly
contributes to boosting the applicability of the model in medical imaging
modalities.

本文提出一种基于 Fourier 的语义增强方法 FIESTA，利用不确定性指导增强过程，通过操纵频率域中的振幅和相位组件来增强医学图像分割的基本目标，在单源域通用性的背景下，通过调整有意义的角点来引入相关变化，利用相位光谱确保结构的一致性。此外，FIESTA 使用先验不确定性来优化增强过程，提高模型适应多样化增强数据和集中处理高不确定性区域的能力。在三种跨领域场景的大量实验中，显示 FIESTA 在分割性能上超过了最近的最先进的单源域通用方法，并显著促进了模型在医学成像模态中的适用性。

FIESTA: 基于傅里叶变换的不确定性引导下的语法增强方法，用于提升医学图像分割的领域通用性

FIESTA: Fourier-Based Semantic Augmentation with Uncertainty Guidance  for Enhanced Domain Generalizability in Medical Image Segmentation

Self-supervised methods received tremendous attention thanks to their
seemingly heuristic approach to learning representations that respect the
semantics of the data without any apparent supervision in the form of labels. A
growing body of literature is already being published in an attempt to build a
coherent and theoretically grounded understanding of the workings of a zoo of
losses used in modern self-supervised representation learning methods. In this
paper, we attempt to provide an understanding from the perspective of a Laplace
operator and connect the inductive bias stemming from the augmentation process
to a low-rank matrix completion problem. To this end, we leverage the results
from low-rank matrix completion to provide theoretical analysis on the
convergence of modern SSL methods and a key property that affects their
downstream performance.

这篇研究论文从 Laplace 算子的角度出发，探讨了现代自监督表征学习方法中使用的一系列损失的理论基础，将数据扩充的感性偏置与低秩矩阵完成问题联系起来，利用低秩矩阵完成结果分析了现代自监督学习方法的收敛性及影响其下游性能的重要属性。