Recently, Visual Transformer (ViT) has been extensively used in medical image
segmentation (MIS) due to applying self-attention mechanism in the spatial
domain to modeling global knowledge. However, many studies have focused on
improving models in the spatial domain while neglecting the importance of
frequency domain information. Therefore, we propose Multi-axis External Weights
UNet (MEW-UNet) based on the U-shape architecture by replacing self-attention
in ViT with our Multi-axis External Weights block. Specifically, our block
performs a Fourier transform on the three axes of the input features and
assigns the external weight in the frequency domain, which is generated by our
External Weights Generator. Then, an inverse Fourier transform is performed to
change the features back to the spatial domain. We evaluate our model on four
datasets, including Synapse, ACDC, ISIC17 and ISIC18 datasets, and our approach
demonstrates competitive performance, owing to its effective utilization of
frequency domain information.

基于 U-shape 结构的 Multi-axis External Weights UNet 模型利用频域信息替代 ViT 中的自注意力机制，在医学图像分割中展现出竞争性能。

学习多轴频域表示在医学图像分割中的应用

Learning Multi-axis Representation in Frequency Domain for Medical Image  Segmentation

Multi-stage architectures have exhibited efficacy in image dehazing, which
usually decomposes a challenging task into multiple more tractable sub-tasks
and progressively estimates latent hazy-free images. Despite the remarkable
progress, existing methods still suffer from the following shortcomings: (1)
limited exploration of frequency domain information; (2) insufficient
information interaction; (3) severe feature redundancy. To remedy these issues,
we propose a novel Mutual Information-driven Triple interaction Network
(MITNet) based on spatial-frequency dual domain information and two-stage
architecture. To be specific, the first stage, named amplitude-guided haze
removal, aims to recover the amplitude spectrum of the hazy images for haze
removal. And the second stage, named phase-guided structure refined, devotes to
learning the transformation and refinement of the phase spectrum. To facilitate
the information exchange between two stages, an Adaptive Triple Interaction
Module (ATIM) is developed to simultaneously aggregate cross-domain,
cross-scale, and cross-stage features, where the fused features are further
used to generate content-adaptive dynamic filters so that applying them to
enhance global context representation. In addition, we impose the mutual
information minimization constraint on paired scale encoder and decoder
features from both stages. Such an operation can effectively reduce information
redundancy and enhance cross-stage feature complementarity. Extensive
experiments on multiple public datasets exhibit that our MITNet performs
superior performance with lower model complexity.The code and models are
available at this https URL

提出了一种基于空间频率双域信息和两阶段架构的互信息驱动的三重交互网络（MITNet），解决了现有方法中对频域信息的有限探索、信息交互不足和特征冗余严重等问题。MITNet 在多个公共数据集上展示出卓越的性能，并具有较低的模型复杂性。

基于互信息的三重交互网络用于高效图像去雾

Mutual Information-driven Triple Interaction Network for Efficient Image  Dehazing

Deep learning advancements have revolutionized scalable classification in
many domains including computer vision. However, when it comes to
wearable-based classification and domain adaptation, existing computer
vision-based deep learning architectures and pretrained models trained on
thousands of labeled images for months fall short. This is primarily because
wearable sensor data necessitates sensor-specific preprocessing, architectural
modification, and extensive data collection. To overcome these challenges,
researchers have proposed encoding of wearable temporal sensor data in images
using recurrent plots. In this paper, we present a novel modified-recurrent
plot-based image representation that seamlessly integrates both temporal and
frequency domain information. Our approach incorporates an efficient Fourier
transform-based frequency domain angular difference estimation scheme in
conjunction with the existing temporal recurrent plot image. Furthermore, we
employ mixup image augmentation to enhance the representation. We evaluate the
proposed method using accelerometer-based activity recognition data and a
pretrained ResNet model, and demonstrate its superior performance compared to
existing approaches.

本论文提出了一种新型的基于改进循环图像的时间频率信息表示法，着重于针对穿戴传感器数据的分类，结果表明该方法在活动识别方面相较于现有方法性能更佳。