Breast cancer is one of the most common cancers in the world, especially among women. Breast tumor segmentation is a key step in the identification and localization of the breast tumor region, which has important clinical significance. Inspired by the swin-transformer model with powerful global modeling ability, we propose a semantic segmentation framework named Swin-Net for breast ultrasound images, which combines Transformer and Convolutional Neural Networks (CNNs) to effectively improve the accuracy of breast ultrasound segmentation. Firstly, our model utilizes a swin-transformer encoder with stronger learning ability, which can extract features of images more precisely. In addition, two new modules are introduced in our method, including the feature refinement and enhancement module (RLM) and the hierarchical multi-scale feature fusion module (HFM), given that the influence of ultrasonic image acquisition methods and the characteristics of tumor lesions is difficult to capture. Among them, the RLM module is used to further refine and enhance the feature map learned by the transformer encoder. The HFM module is used to process multi-scale high-level semantic features and low-level details, so as to achieve effective cross-layer feature fusion, suppress noise, and improve model segmentation performance. Experimental results show that Swin-Net performs significantly better than the most advanced methods on the two public benchmark datasets. In particular, it achieves an absolute improvement of 1.4–1.8% on Dice. Additionally, we provide a new dataset of breast ultrasound images on which we test the effect of our model, further demonstrating the validity of our method. In summary, the proposed Swin-Net framework makes significant advancements in breast ultrasound image segmentation, providing valuable exploration for research and applications in this domain.

WiFi-based wireless sensing has gained popularity for enabling smart indoor services, one of which is fall detection which plays a vital role in mitigating health risks for elders. Previous approaches have treated daily activities as independent events and built models to distinguish falls from others. However, human activities are usually adjoint in practice, e.g., the elder may suddenly fall when she walks. This adjoining introduces shared features between different activities, thereby affecting the classification performance. To address this problem, we propose Fall-attention, an attention-based fall detection method that can focus on the features related to fall events and suppress interference of irrelevant activities to improve performance. Its basic idea is to produce a task-oriented feature representation of fall events inside the signal using attention-based sentence embedding techniques and Recurrent Neural Network (RNN). We incorporate multi-task learning into Fall-attention by adopting multiple independent classification modules. This enables the model to explore different regions of the signal, capturing the composition of adjoint activities. A series of signal preprocessing and data enhancement techniques are also adopted to promote model training. Experimental results of the dataset containing adjoint activities demonstrate the superiority of Fall-attention over previous methods, which achieves an average accuracy of 95%.

Globally, diabetic retinopathy (DR) is the primary cause of blindness, affecting millions of people worldwide. This widespread impact underscores the critical need for reliable and precise diagnostic techniques to ensure prompt diagnosis and effective treatment. Deep learning-based automated diagnosis for diabetic retinopathy can facilitate early detection and treatment. However, traditional deep learning models that focus on local views often learn feature representations that are less discriminative at the semantic level. On the other hand, models that focus on global semantic-level information might overlook critical, subtle local pathological features. To address this issue, we propose an adaptive multi-scale feature fusion network called (AMSFuse), which can adaptively combine multi-scale global and local features without compromising their individual representation. Specifically, our model incorporates global features for extracting high-level contextual information from retinal images. Concurrently, local features capture fine-grained details, such as microaneurysms, hemorrhages, and exudates, which are critical for DR diagnosis. These global and local features are adaptively fused using a fusion block, followed by an Integrated Attention Mechanism (IAM) that refines the fused features by emphasizing relevant regions, thereby enhancing classification accuracy for DR classification. Our model achieves 86.3% accuracy on the APTOS dataset and 96.6% RFMiD, both of which are comparable to state-of-the-art methods.

In medical services, the amount of data generated by medical devices is increasing explosively, and access to medical data is also put forward with higher requirements. Although HBase-based medical data storage solutions exist, they cannot meet the needs of fast locating and diversified access to medical data. In order to improve the retrieval speed, the recognition model S-TCR and the dynamic management algorithm SL-TCR, based on the behavior characteristics of access, were proposed to identify the frequently accessed hot data and dynamically manage the data storage medium as to maximize the system access performance. In order to improve the search performance of keys, an optimized secondary index strategy was proposed to reduce I/O overhead and optimize the search performance of non-primary key indexes. Comparative experiments were conducted on real medical data sets. The experimental results show that the optimized retrieval model can meet the needs of hot data access and diversified medical data retrieval.