Medical image segmentation is crucial for the diagnosis and analysis of disease. Deep convolutional neural network methods have achieved great success in medical image segmentation. However, they are highly susceptible to noise interference during the propagation of the network, where weak noise can dramatically alter the network output. As the network deepens, it can face problems such as gradient explosion and vanishing. To improve the robustness and segmentation performance of the network, we propose a wavelet residual attention network (WRANet) for medical image segmentation. We replace the standard downsampling modules (e.g., maximum pooling and average pooling) in CNNs with discrete wavelet transform, decompose the features into low- and high-frequency components, and remove the high-frequency components to eliminate noise. At the same time, the problem of feature loss can be effectively addressed by introducing an attention mechanism. The combined experimental results show that our method can effectively perform aneurysm segmentation, achieving a Dice score of 78.99%, an IoU score of 68.96%, a precision of 85.21%, and a sensitivity score of 80.98%. In polyp segmentation, a Dice score of 88.89%, an IoU score of 81.74%, a precision rate of 91.32%, and a sensitivity score of 91.07% were achieved. Furthermore, our comparison with state-of-the-art techniques demonstrates the competitiveness of the WRANet network.

Federated learning enables cooperative computation between multiple participants while protecting user privacy. Currently, federated learning algorithms assume that all participants are trustworthy and their systems are secure. However, the following problems arise in real-world scenarios: (1) Malicious clients disrupt federated learning through model poisoning and data poisoning attacks. Although some research has proposed secure aggregation methods to solve this problem, most methods have limitations. (2) Due to the variance in data quality and computational resources among participants, rewards cannot be distributed equally. Some clients also exhibit free-rider behavior, seeking to cheat the reward system and manipulate global models. Evaluating client contribution and distributing rewards also present challenges. To address these challenges, we design a trustworthy federated framework to ensure secure computing throughout the federated task process. First, we propose a malicious model detection method for secure model aggregation. Then, we also propose a fair method of assessing contribution to identify client-side free-riding behavior. Lastly, we develop a computation process grounded in blockchain and smart contracts to guarantee the trustworthiness and fairness of federated tasks. To validate the performance of our framework, we simulate different types of client attacks and contribution evaluation scenarios on several open-source datasets. The experiments show that our framework guarantees the federated task’s credibility and achieves fair client contribution evaluation.

The optimization of logistics distribution can be defined as the multiple traveling salesman problem (MTSP). The purpose of existing heuristic algorithms, such as Genetic Algorithm (GA), Ant Colony Algorithm (ACO), etc., is to find the optimal path in a short time. However, two important factors of logistics distribution optimization, including work time window and the carrying capacity of the vehicle in distribution system, have been ignored. In this paper, we consider the influences of time limitation of modern commercial logistics and carrying capacity of the vehicle on the logistics optimization, and then propose a MTSP with constraints of time window and capacity of each salesman. We design a novel hybrid algorithm by combining the minimum spanning 1-tree with ACO to find the optimal solution. In addition, we improve the pheromone update rules to increase the search efficiency of ACO algorithm. The experiments show that the novel hybrid algorithm achieves a shorter path than the other algorithms.

Fetal congenital heart disease (CHD) is the most common type of fatal congenital malformation. Fetal four-chamber (FC) view is a significant and easily accessible ultrasound (US) image among fetal echocardiography images. Automatic detection of four fetal heart chambers considerably contributes to the early diagnosis of fetal CHD. Furthermore, robust and discriminative features are essential for detecting crucial visualizing medical images, especially fetal FC views. However, it is an incredibly challenging task due to several key factors, such as numerous speckles in US images, the fetal four chambers with small size and unfixed positions, and category confusion caused by the similarity of cardiac chambers. These factors hinder the process of capturing robust and discriminative features, hence destroying the fetal four chambers’ precise detection. Therefore, we propose an intelligent feature learning detection system (FLDS) for FC views to detect the four chambers. A multistage residual hybrid attention module (MRHAM) presented in this paper is incorporated in the FLDS for learning powerful and robust features, helping FLDS accurately locate the four chambers in the fetal FC views. Extensive experiments demonstrate that our proposed FLDS outperforms the current state-of-the-art, including the precision of 0.919, the recall of 0.971, the $F_{1}$ score of 0.944, the mAP of 0.953, and the frames per second (FPS) of 43. In addition, our proposed FLDS is also validated on other visualizing nature images such as the PASCAL VOC dataset, achieving a higher mAP of 0.878 while input size is 608 × 608.