Lung segmentation in chest X-ray images is a critical task in medical image
analysis, enabling accurate diagnosis and treatment of various lung diseases.
In this paper, we propose a novel approach for lung segmentation by integrating
Hierarchical SegNet with a proposed multi-modal attention mechanism. The
channel attention mechanism highlights specific feature maps or channels
crucial for lung region segmentation, while the context attention mechanism
adaptively weighs the importance of different spatial regions. By combining
both mechanisms, the proposed mechanism enables the model to better capture
complex patterns and relationships between various features, leading to
improved segmentation accuracy and better feature representation. Furthermore,
an attention gating mechanism is employed to integrate attention information
with encoder features, allowing the model to adaptively weigh the importance of
different attention features and ignore irrelevant ones. Experimental results
demonstrate that our proposed approach achieves state-of-the-art performance in
lung segmentation tasks, outperforming existing methods. The proposed approach
has the potential to improve the accuracy and efficiency of lung disease
diagnosis and treatment, and can be extended to other medical image analysis
tasks.

本研究提出了一种将分层 SegNet 与多模态注意力机制相结合的新方法用于肺分割，实验证明该方法在肺分割任务中表现优异，超过现有方法，具有提高肺病诊断和治疗准确性和效率的潜力，并可推广到其他医学图像分析任务。

基于通道和上下文注意力的分层 SegNet 用于胸部 X 光图像的准确肺部分割

Hierarchical SegNet with Channel and Context Attention for Accurate Lung  Segmentation in Chest X-ray Images

This paper proposes a novel multi-modal transformer network for detecting
actions in untrimmed videos. To enrich the action features, our transformer
network utilizes a new multi-modal attention mechanism that computes the
correlations between different spatial and motion modalities combinations.
Exploring such correlations for actions has not been attempted previously. To
use the motion and spatial modality more effectively, we suggest an algorithm
that corrects the motion distortion caused by camera movement. Such motion
distortion, common in untrimmed videos, severely reduces the expressive power
of motion features such as optical flow fields. Our proposed algorithm
outperforms the state-of-the-art methods on two public benchmarks, THUMOS14 and
ActivityNet. We also conducted comparative experiments on our new instructional
activity dataset, including a large set of challenging classroom videos
captured from elementary schools.

本文提出了一种新的多模态转换器网络，用于检测未修剪视频中的动作，利用新的多模态注意机制计算不同空间和动态模态组合之间的相关性，并提出一种算法来纠正相机运动引起的动态变形，该算法在 THUMOS14 和 ActivityNet 两个公共基准测试上优于现有方法，在我们的新教育活动数据集上进行了比较实验，该数据集包括从小学捕捉的大量具有挑战性的课堂视频。