Breast cancer is a major health concern affecting women worldwide. Early detection and accurate diagnosis of breast cancer are crucial for improving patient outcomes. In recent years, deep learning techniques have been increasingly applied to medical imaging, including mammography, for the detection and diagnosis of breast cancer. In this study, we proposed a deep learning-based approach using the EfficientNet architecture for the detection and classification of breast cancer. We evaluated the performance of our proposed approach using mammography images from the CBIS-DDSM dataset and achieved accuracy of 0.75 and AUC of 0.83. Our results demonstrate the effectiveness of using deep learning techniques in medical imaging for breast cancer detection and diagnosis.
Cyberspace mimic defense (CMD) is an emerging defense technology that combines dynamic heterogeneous redundancy (DHR) to enhance the security of information systems. At present, the applications of CMD focus more on data tamper resistance and do not more pay attention to the privacy disclosure of executors. This paper proposes to introduce the secret sharing mechanism into the CMD and use a secret sharing scheme with dimension switching to achieve the security of the private data of executors without adding extra key management. And experiments proved that the combination of the secret sharing scheme and mimic defense can increase data security at a tolerable cost.
Most existing methods for few-shot image classification treat the support features and query features separately, and the category of a query image is determined based on its similarity to the support images. However, the relationships between the query features and support features are often ignored. In this paper, we propose a strategy that exploits the relationships between query and support features and give more weights to the highly related features. A cross-feature fusion method is further proposed to combine with metric learning to reduce overfitting. Extensive experiments on a wide range of datasets show that our method has achieved advanced results.
Bokeh rendering is a popular and effective technique used in photography to create an aesthetically pleasing effect. It is widely used to blur the background and highlight the subject in the foreground, thereby drawing the viewer's attention to the main focus of the image. In traditional digital single-lens reflex cameras (DSLRs), this effect is achieved through the use of a large aperture lens. This allows the camera to capture images with shallow depth-of-field, in which only a small area of the image is in sharp focus, while the rest of the image is blurred. However, the hardware embedded in mobile phones is typically much smaller and more limited than that found in DSLRs. Consequently, mobile phones are not able to capture natural shallow depth-of-field photos, which can be a significant limitation for mobile photography. To address this challenge, we propose a novel method for bokeh rendering based on the Vision Transformer. The proposed method employs an adaptive depth calibration network (ADCN) that acts as a confidence level to compensate for errors in monocular depth estimation. The ADCN is used to supervise the rendering process in conjunction with depth information, allowing for the generation of high-quality bokeh images at high resolutions. Extensive experiments demonstrate that the proposed method outperforms state-of-the-art (SOTA) methods, achieving about 24.7% improvements on LPIPS and obtaining higher PSNR scores.
The employment of decentralized microservices requires a lightweight service composition scheme with flexibility. However, no schemes can fulfil the requirement. In this paper, a data and control interdependent problem in traditional API form is identified as the root preventing a scheme fulfilling the requirement from being devised. A new configurable API form, Standardized Control Data Separation API (SCONDAS API), is devised to resolve the data and control interdependent problem. An API framework implementing the form of SCANDAS API is further proposed. The proposed API framework enables lightweight and flexible microservice composition. Experimental evaluations validate the feasibility of the API framework.
