Multimodal magnetic resonance imaging (MRI) can reveal different patterns of
human tissue and is crucial for clinical diagnosis. However, limited by cost,
noise and manual labeling, obtaining diverse and reliable multimodal MR images
remains a challenge. For the same lesion, different MRI manifestations have
great differences in background information, coarse positioning and fine
structure. In order to obtain better generation and segmentation performance, a
coordination-spatial attention generation adversarial network (CASP-GAN) based
on the cycle-consistent generative adversarial network (CycleGAN) is proposed.
The performance of the generator is optimized by introducing the Coordinate
Attention (CA) module and the Spatial Attention (SA) module. The two modules
can make full use of the captured location information, accurately locating the
interested region, and enhancing the generator model network structure. The
ability to extract the structure information and the detailed information of
the original medical image can help generate the desired image with higher
quality. There exist some problems in the original CycleGAN that the training
time is long, the parameter amount is too large, and it is difficult to
converge. In response to this problem, we introduce the Coordinate Attention
(CA) module to replace the Res Block to reduce the number of parameters, and
cooperate with the spatial information extraction network above to strengthen
the information extraction ability. On the basis of CASP-GAN, an attentional
generative cross-modality segmentation (AGCMS) method is further proposed. This
method inputs the modalities generated by CASP-GAN and the real modalities into
the segmentation network for brain tumor segmentation. Experimental results
show that CASP-GAN outperforms CycleGAN and some state-of-the-art methods in
PSNR, SSMI and RMSE in most tasks.

提出了一种基于 CASP-GAN 的协调空间注意力生成对抗网络，以及一种关注性生成跨模态分割方法，该方法在脑肿瘤分割中取得了比 CycleGAN 和部分前沿方法更好的结果。

基于注意力机制的脑肿瘤二阶段 MR 图像分割方法

Two-stage MR Image Segmentation Method for Brain Tumors based on  Attention Mechanism

At present, the identification of agricultural pests and diseases has the
problem that the model is not lightweight enough and difficult to apply. Based
on MobileNetV3, this paper introduces the Coordinate Attention block. The
parameters of MobileNetV3-large are reduced by 22%, the model size is reduced
by 19.7%, and the accuracy is improved by 0.92%. The parameters of
MobileNetV3-small are reduced by 23.4%, the model size is reduced by 18.3%, and
the accuracy is increased by 0.40%. In addition, the improved MobileNetV3-small
was migrated to Jetson Nano for testing. The accuracy increased by 2.48% to
98.31%, and the inference speed increased by 7.5%. It provides a reference for
deploying the agricultural pest identification model to embedded devices.

本文介绍了基于 MobileNetV3 的 Coordinate Attention 块来解决农业害虫和疾病识别模型过重难以应用的问题，在减小模型体积和提高模型准确率的基础上，成功将模型部署到 Jetson Nano 上，可为其他嵌入式设备的农业害虫和疾病识别提供参考。

基于 MobileNetV3 的农业病害快速识别方法改进

Improved lightweight identification of agricultural diseases based on MobileNetV3

Recent studies on mobile network design have demonstrated the remarkable
effectiveness of channel attention (e.g., the Squeeze-and-Excitation attention)
for lifting model performance, but they generally neglect the positional
information, which is important for generating spatially selective attention
maps. In this paper, we propose a novel attention mechanism for mobile networks
by embedding positional information into channel attention, which we call
"coordinate attention". Unlike channel attention that transforms a feature
tensor to a single feature vector via 2D global pooling, the coordinate
attention factorizes channel attention into two 1D feature encoding processes
that aggregate features along the two spatial directions, respectively. In this
way, long-range dependencies can be captured along one spatial direction and
meanwhile precise positional information can be preserved along the other
spatial direction. The resulting feature maps are then encoded separately into
a pair of direction-aware and position-sensitive attention maps that can be
complementarily applied to the input feature map to augment the representations
of the objects of interest. Our coordinate attention is simple and can be
flexibly plugged into classic mobile networks, such as MobileNetV2, MobileNeXt,
and EfficientNet with nearly no computational overhead. Extensive experiments
demonstrate that our coordinate attention is not only beneficial to ImageNet
classification but more interestingly, behaves better in down-stream tasks,
such as object detection and semantic segmentation. Code is available at
this https URL

本文提出了一种名为坐标注意力机制的新型注意力机制，将位置信息嵌入到通道注意力中以获取空间选择性的注意力图，通过将特征图分别编码成一对有方向和位置敏感的注意力图，提高了 MobileNetV2、MobileNeXt、EfficientNet 等网络中物体检测和语义分割的性能。