Retinal vessel image is an important biological information that can be used for personal identification in the social security domain, and for disease diagnosis in the medical domain. While automatic vessel image segmentation is essential, it is also a challenging task because the retinal vessels have complex topological structures, and the retinal vessels vary in size and shape. In recent years, image segmentation based on the deep learning technique has become a mainstream method. Unfortunately, the existing methods cannot make the best use of the global information, and the model complexity is high. In this paper, a convolutional neural network integrated with the attention mechanism is proposed. The overall network structure consists of a basic U-Net and an attention module, and the latter is used to capture global information and to enhance features by placing it in the process of feature fusion. Experiment results on five public datasets show that the proposed scheme outperforms other existing mainstream approaches, and most of the performance indicators are in the leading positions. More importantly, the proposed method has a significant reduction in the number of parameters.

With the improvement of hardware computing power and the development of deep learning algorithms, a revolution of "artificial intelligence (AI) + medical image" is taking place. Benefiting from diversified modern medical measurement equipment, a large number of medical images will be produced in the clinical process. These images improve the diagnostic accuracy of doctors, but also increase the labor burden of doctors. Deep learning technology is expected to realize an auxiliary diagnosis and improve diagnostic efficiency. At present, the method of deep learning technology combined with attention mechanism is a research hotspot and has achieved state-of-the-art results in many medical image tasks. This paper reviews the deep learning attention methods in medical image analysis. A comprehensive literature survey is first conducted to analyze the keywords and literature. Then, we introduce the development and technical characteristics of the attention mechanism. For its application in medical image analysis, we summarize the related methods in medical image classification, segmentation, detection, and enhancement. The remaining challenges, potential solutions, and future research directions are also discussed.

Accurate remaining useful life (RUL) prediction of lithium-ion batteries is critical for energy supply systems. In conventional data-driven RUL prediction approaches, the battery's degradation mechanism is difficult into incorporate in the RUL prediction. Furthermore, there are notable limitations in reflecting the significance of different time instances, and the uncertainty in the degradation process. Consequently, a novel data-model interactive RUL prediction approach based on particle filter-temporal attention mechanism-bidirectional gated recurrent unit (PF-BiGRU-TSAM) is proposed. Specifically, BiGRU-TSAM is trained offline through historical data, which assigns corresponding significance to battery capacities at different time instances. Moreover, regarding the interactive data-model for the online prediction phase based on PF-BiGRU-TSAM, the advantages of data-driven and model-based approaches are integrated, which accomplishes the purpose of modifying each other. The proposed PF-BiGRU-TSAM approach is validated with a real-world battery dataset. Experimental results demonstrate the proposed approach is better than some published approaches. Taking the 50th operational cycle of the four batteries B0005, B0006, B0007, and B0018 in the dataset as an instance, the absolute errors of the proposed PF-BiGRU-TSAM are 0, 1, 3, 3, respectively, which represents the proposed approach has an excellent performance.

As an indispensable energy device, 18650 lithium-ion battery has widespread applications in electric vehicles. Remaining useful life (RUL) prediction of lithium-ion battery is critical for the normal operation of electric vehicles. In conventional approaches, the adaptive estimation of model parameters and the detection of capacity regeneration await further research. To adaptively estimate the noise variables in the degradation model and to accurately detect the battery capacity regeneration, this article proposes a novel expectation maximization-unscented particle filter-Wilcoxon rank sum test (EM-UPF-W) approach. In detail, in the case of unlabeled small samples, this article constructs a dynamic degradation model on the basis of UPF for a single battery, which adaptively estimates the noise variables with the aid of EM algorithm. Furthermore, the Wilcoxon rank sum test is introduced to determine the capacity regeneration point, so as to decrease the prediction error. A 18650 lithium-ion battery dataset produced by NASA is used to demonstrate the approach. Experimental results show that the proposed EM-UPF-W outperforms some existing data-driven techniques.

Knee segmentation and landmark localization from 3D MRI are two significant tasks for diagnosis and treatment of knee diseases. With the development of deep learning, Convolutional Neural Network (CNN) based methods have become the mainstream. However, the existing CNN methods are mostly single-task methods. Due to the complex structure of bone, cartilage and ligament in the knee, it is challenging to complete the segmentation or landmark localization alone. And establishing independent models for all tasks will bring difficulties for surgeon’s clinical using. In this paper, a Spatial Dependence Multi-task Transformer (SDMT) network is proposed for 3D knee MRI segmentation and landmark localization. We use a shared encoder for feature extraction, then SDMT utilizes the spatial dependence of segmentation results and landmark position to mutually promote the two tasks. Specifically, SDMT adds spatial encoding to the features, and a task hybrided multi-head attention mechanism is designed, in which the attention heads are divided into the inter-task attention head and the intra-task attention head. The two attention head deal with the spatial dependence between two tasks and correlation within the single task, respectively. Finally, we design a dynamic weight multi-task loss function to balance the training process of two task. The proposed method is validated on our 3D knee MRI multi-task datasets. Dice can reach 83.91% in the segmentation task, and MRE can reach 2.12 mm in the landmark localization task, it is competitive and superior over other state-of-the-art single-task methods.

Federated learning (FL) faces significant challenges in medical image analysis due to data heterogeneity among clients, where balancing personalization and generalization is challenging. Existing methods often struggle to achieve both objectives simultaneously, as excessive personalization reduces generalization, while over-generalization weakens adaptation to client-specific features. To address these challenges, we propose a perceptual hashing hypernetwork personalized FL (PHH-FL) to enhance both personalization and generalization. PHH-FL first uses a perceptual hashing (pHash) algorithm to construct a similarity matrix that captures data distribution differences among clients and employs a hypernetwork to generate personalized parameters for each client. Meanwhile, the shared hypernetwork is introduced to promote knowledge transfer between clients, thereby enhancing the generalization ability of the local model. By selectively generating parameters for the initial layers of the target network, PHH-FL reduces computational and communication costs while maintaining performance. Experiments on medical image classification and segmentation tasks show that PHH-FL outperforms state-of-the-art methods. Ablation studies further demonstrate that the proposed framework effectively balances personalization and generalization.