Deep learning has demonstrated remarkable achievements in medical image
segmentation. However, prevailing deep learning models struggle with poor
generalization due to (i) intra-class variations, where the same class appears
differently in different samples, and (ii) inter-class independence, resulting
in difficulties capturing intricate relationships between distinct objects,
leading to higher false negative cases. This paper presents a novel approach
that synergies spatial and spectral representations to enhance
domain-generalized medical image segmentation. We introduce the innovative
Spectral Correlation Coefficient objective to improve the model's capacity to
capture middle-order features and contextual long-range dependencies. This
objective complements traditional spatial objectives by incorporating valuable
spectral information. Extensive experiments reveal that optimizing this
objective with existing architectures like UNet and TransUNet significantly
enhances generalization, interpretability, and noise robustness, producing more
confident predictions. For instance, in cardiac segmentation, we observe a 0.81
pp and 1.63 pp (pp = percentage point) improvement in DSC over UNet and
TransUNet, respectively. Our interpretability study demonstrates that, in most
tasks, objectives optimized with UNet outperform even TransUNet by introducing
global contextual information alongside local details. These findings
underscore the versatility and effectiveness of our proposed method across
diverse imaging modalities and medical domains.

本研究介绍了一种新颖方法，通过结合空间和频谱表示来增强领域通用医学图像分割的能力，并引入了创新的谱相关系数目标函数来提高模型对中阶特征和上下文长距离依赖关系的捕捉能力。大量实验证明，优化该目标函数能显著提高 UNet 和 TransUNet 等现有架构的泛化能力、解释能力和噪声鲁棒性，生成更可信的预测结果。

鲁棒且普适的医学图像分割的空间和光谱学习的协调

Harmonized Spatial and Spectral Learning for Robust and Generalized  Medical Image Segmentation

Domain-generalized urban-scene semantic segmentation (USSS) aims to learn
generalized semantic predictions across diverse urban-scene styles. Unlike
domain gap challenges, USSS is unique in that the semantic categories are often
similar in different urban scenes, while the styles can vary significantly due
to changes in urban landscapes, weather conditions, lighting, and other
factors. Existing approaches typically rely on convolutional neural networks
(CNNs) to learn the content of urban scenes.
In this paper, we propose a Content-enhanced Mask TransFormer (CMFormer) for
domain-generalized USSS. The main idea is to enhance the focus of the
fundamental component, the mask attention mechanism, in Transformer
segmentation models on content information. To achieve this, we introduce a
novel content-enhanced mask attention mechanism. It learns mask queries from
both the image feature and its down-sampled counterpart, as lower-resolution
image features usually contain more robust content information and are less
sensitive to style variations. These features are fused into a Transformer
decoder and integrated into a multi-resolution content-enhanced mask attention
learning scheme.
Extensive experiments conducted on various domain-generalized urban-scene
segmentation datasets demonstrate that the proposed CMFormer significantly
outperforms existing CNN-based methods for domain-generalized semantic
segmentation, achieving improvements of up to 14.00\% in terms of mIoU (mean
intersection over union). The source code for CMFormer will be made available
at this
\href{this https URL}{repository}.

本文提出了一种基于 Content-enhanced Mask TransFormer (CMFormer) 的 Transformer 分割模型，该模型通过将图像内容信息和其下采样版本的图像特征融合到一个多分辨率内容增强的蒙版注意力学习方案中，从而在各种领域通用的城市场景语义分割数据集上显着优于现有的基于 CNN 的方法。