Enhancing medical image segmentation with a multi-transformer U-Net.

Various segmentation networks based on Swin Transformer have shown promise in medical segmentation tasks. Nonetheless, challenges such as lower accuracy and slower training convergence have persisted. To tackle these issues, we introduce a novel approach that combines the Swin Transformer and Deformable Transformer to enhance overall model performance. We leverage the Swin Transformer's window attention mechanism to capture local feature information and employ the Deformable Transformer to adjust sampling positions dynamically, accelerating model convergence and aligning it more closely with object shapes and sizes. By amalgamating both Transformer modules and incorporating additional skip connections to minimize information loss, our proposed model excels at rapidly and accurately segmenting CT or X-ray lung images. Experimental results demonstrate the remarkable, showcasing the significant prowess of our model. It surpasses the performance of the standalone Swin Transformer's Swin Unet and converges more rapidly under identical conditions, yielding accuracy improvements of 0.7% (resulting in 88.18%) and 2.7% (resulting in 98.01%) on the COVID-19 CT scan lesion segmentation dataset and Chest X-ray Masks and Labels dataset, respectively. This advancement has the potential to aid medical practitioners in early diagnosis and treatment decision-making.

A Transformer-Based Neural Network for Gait Prediction in Lower Limb Exoskeleton Robots Using Plantar Force.

Lower limb exoskeleton robots have shown significant research value due to their capabilities of providing assistance to wearers and improving physical motion functions. As a type of robotic technology, wearable robots are directly in contact with the wearer's limbs during operation, necessitating a high level of human-robot collaboration to ensure safety and efficacy. Furthermore, gait prediction for the wearer, which helps to compensate for sensor delays and provide references for controller design, is crucial for improving the the human-robot collaboration capability. For gait prediction, the plantar force intrinsically reflects crucial gait patterns regardless of individual differences. To be exact, the plantar force encompasses a doubled three-axis force, which varies over time concerning the two feet, which also reflects the gait patterns indistinctly. In this paper, we developed a transformer-based neural network (TFSformer) comprising convolution and variational mode decomposition (VMD) to predict bilateral hip and knee joint angles utilizing the plantar pressure. Given the distinct information contained in the temporal and the force-space dimensions of plantar pressure, the encoder uses 1D convolution to obtain the integrated features in the two dimensions. As for the decoder, it utilizes a multi-channel attention mechanism to simultaneously focus on both dimensions and a deep multi-channel attention structure to reduce the computational and memory consumption. Furthermore, VMD is applied to networks to better distinguish the trends and changes in data. The model is trained and tested on a self-constructed dataset that consists of data from 35 volunteers. The experimental results show that FTSformer reduces the mean absolute error (MAE) up to 10.83%, 15.04% and 8.05% and the mean squared error (MSE) by 20.40%, 29.90% and 12.60% compared to the CNN model, the transformer model and the CNN transformer model, respectively.

Enhanced mechanisms of pooling and channel attention for deep learning feature maps.

The pooling function is vital for deep neural networks (DNNs). The operation is to generalize the representation of feature maps and progressively cut down the spatial size of feature maps to optimize the computing consumption of the network. Furthermore, the function is also the basis for the computer vision attention mechanism. However, as a matter of fact, pooling is a down-sampling operation, which makes the feature-map representation approximately to small translations with the summary statistic of adjacent pixels. As a result, the function inevitably leads to information loss more or less. In this article, we propose a fused max-average pooling (FMAPooling) operation as well as an improved channel attention mechanism (FMAttn) by utilizing the two pooling functions to enhance the feature representation for DNNs. Basically, the methods are to enhance multiple-level features extracted by max pooling and average pooling respectively. The effectiveness of the proposals is verified with VGG, ResNet, and MobileNetV2 architectures on CIFAR10/100 and ImageNet100. According to the experimental results, the FMAPooling brings up to 1.63% accuracy improvement compared with the baseline model; the FMAttn achieves up to 2.21% accuracy improvement compared with the previous channel attention mechanism. Furthermore, the proposals are extensible and could be embedded into various DNN models easily, or take the place of certain structures of DNNs. The computation burden introduced by the proposals is negligible.

Optimizing the Deep Neural Networks by Layer-Wise Refined Pruning and the Acceleration on FPGA.

To accelerate the practical applications of artificial intelligence, this paper proposes a high efficient layer-wise refined pruning method for deep neural networks at the software level and accelerates the inference process at the hardware level on a field-programmable gate array (FPGA). The refined pruning operation is based on the channel-wise importance indexes of each layer and the layer-wise input sparsity of convolutional layers. The method utilizes the characteristics of the native networks without introducing any extra workloads to the training phase. In addition, the operation is easy to be extended to various state-of-the-art deep neural networks. The effectiveness of the method is verified on ResNet architecture and VGG networks in terms of dataset CIFAR10, CIFAR100, and ImageNet100. Experimental results show that in terms of ResNet50 on CIFAR10 and ResNet101 on CIFAR100, more than 85% of parameters and Floating-Point Operations are pruned with only 0.35% and 0.40% accuracy loss, respectively. As for the VGG network, 87.05% of parameters and 75.78% of Floating-Point Operations are pruned with only 0.74% accuracy loss for VGG13BN on CIFAR10. Furthermore, we accelerate the networks at the hardware level on the FPGA platform by utilizing the tool Vitis AI. For two threads mode in FPGA, the throughput/fps of the pruned VGG13BN and ResNet101 achieves 151.99 fps and 124.31 fps, respectively, and the pruned networks achieve about 4.3× and 1.8× speed up for VGG13BN and ResNet101, respectively, compared with the original networks on FPGA.

AI-based Prevention Embedded System Against COVID-19 in Daily Life.

Since the prevalence of COVID-19, the virus has spread all over the world. A large number of people have been infected and died, and countries all over the world have experienced the most severe crisis. Vaccination can effectively resist the virus. However, it does not mean that vaccination can suppress virus spread completely. Hence, wearing a mask correctly and keeping the social distance become emergency methods for reducing the risk of infection. This paper proposes an AI-based prevention embedded system against COVID-19 in daily life by keeping the function of the emergency method. The system consists of two functions, mask-wearing-status detection, and social-distance measurement. Mask-wearing-status detection employs YOLO and realizes the detection and classification of three mask-wearing-status, corrected-wearing, non-corrected-wearing, and without-wearing. Social-distance measurement equips a depth camera for measuring the distance between humans. The system gives an alert when people do not wear a mask correctly or do not keep their social distance. The system has been implemented on Jetson-nano, a compact embedded board, and achieves 6 f ps. The experimental results also show that the mask-wearing-status detection accuracy archives at 93.21% and the error of social-distance measurement are within 3 cm, which have proved the effectiveness of the system.