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

We present FIESTA, a model selection approach that significantly reduces the
computational resources required to reliably identify state-of-the-art
performance from large collections of candidate models. Despite being known to
produce unreliable comparisons, it is still common practice to compare model
evaluations based on single choices of random seeds. We show that reliable
model selection also requires evaluations based on multiple train-test splits
(contrary to common practice in many shared tasks). Using bandit theory from
the statistics literature, we are able to adaptively determine appropriate
numbers of data splits and random seeds used to evaluate each model, focusing
computational resources on the evaluation of promising models whilst avoiding
wasting evaluations on models with lower performance. Furthermore, our
user-friendly Python implementation produces confidence guarantees of correctly
selecting the optimal model. We evaluate our algorithms by selecting between 8
target-dependent sentiment analysis methods using dramatically fewer model
evaluations than current model selection approaches.

FIESTA 是一种模型选择方法，借助统计学中的赌博机理论根据多种数据集划分和随机种子选择评估每个模型，从而相对减少计算资源，此方法成功应用于情感分析的模型选择。