Non-local attention and multi-task learning based lung segmentation in chest X-ray / 生物医学工程学杂志

Precise segmentation of lung field is a crucial step in chest radiographic computer-aided diagnosis system. With the development of deep learning, fully convolutional network based models for lung field segmentation have achieved great effect but are poor at accurate identification of the boundary and preserving lung field consistency. To solve this problem, this paper proposed a lung segmentation algorithm based on non-local attention and multi-task learning. Firstly, an encoder-decoder convolutional network based on residual connection was used to extract multi-scale context and predict the boundary of lung. Secondly, a non-local attention mechanism to capture the long-range dependencies between pixels in the boundary regions and global context was proposed to enrich feature of inconsistent region. Thirdly, a multi-task learning to predict lung field based on the enriched feature was conducted. Finally, experiments to evaluate this algorithm were performed on JSRT and Montgomery dataset. The maximum improvement of Dice coefficient and accuracy were 1.99% and 2.27%, respectively, comparing with other representative algorithms. Results show that by enhancing the attention of boundary, this algorithm can improve the accuracy and reduce false segmentation.

Fall detection algorithm for community healthcare / 中国医学物理学杂志

A fall detection algorithm for community healthcare is proposed to avoid the secondary injury caused by untimely treatment when the elder living alone falls in the community.The algorithm has two branches,namely 2D convolution and 3D convolution,which allow it can extract spatial and temporal features simultaneously.The dense connections added in the 3D branch enhance the ability to extract temporal features;the residual blocks in the 2D branch are redesigned to improve the ability of spatial feature extraction;and a non-local attention mechanism is introduced to the branch fusion for better feature fusion.The algorithm also takes scene information into consideration,and it is supervised by SIoU loss function and the combined loss function to realize fall detection.The experiment on the expanded public URFD dataset reveals that the proposed method has a detection accuracy of 98.3%,which verifies its performance and robustness for fall detection.