Artificial intelligence-assisted security is an important field of research in relation to information security. One of the most important tasks is to distinguish between normal and abnormal network traffic (such as malicious or sudden traffic). Traffic data are usually extremely unbalanced, and this seriously hinders the detection of outliers. Therefore, the identification of outliers in unbalanced datasets has become a key issue. To help solve this challenge, there is increasing interest in focusing on one-class classification methods that train models based on the samples of a single given class. In this paper, long short-term memory (LSTM) is introduced into one-class classification, and one-class LSTM (OC-LSTM) is proposed based on the traditional one-class support vector machine (OC-SVM). In contrast with other hybrid deep learning methods based on auto-encoders, the proposed method is an end-to-end training network that uses a loss function such as the OC-SVM optimization objective for model training. A comprehensive experiment on three large complex network traffic datasets showed that this method is superior to the traditional shallow method and the most advanced deep method. Furthermore, the proposed method can provide an effective reference for anomaly detection research in the field of network security, especially for the application of one-class classification.

The Intravascular Ultrasound (IVUS) technology is an important imaging modality used in realistic clinical practice, it is often combined with coronary angiography (CAG) to diagnose coronary artery disease. As the golden standard for in vivo imaging of coronary artery walls, it can provide high-resolution images of the artery wall. Generally, the IVUS acquisition device uses an ultrasonic transducer to acquire the fine-grained anatomical information of the cardiovascular tissue by means of pulse echo imaging. However, widely used mechanical rotating imaging system suffered from guidewire artifacts. The inadequate visualization caused by artifacts often caused huge trouble for clinical diagnosis and subsequent tissue structure evaluation, and there is no suitable way to solve this long-standing problem so far. In this paper, we conducted an exploratory study and proposed the first deep learning based network named AIVUS for repairing the corrupted IVUS images. The network has a novel generative adversarial architecture, the united of gated convolution and spatiotemporal aggregation structure has been introduced to enhance its restoration capability. The proposed network can handle large-scale, moving guidewire artifacts, and it can fully utilize spatial and temporal information hidden in sequence to recover the high-fidelity original content and maintain consistency between frames. Furthermore, we compared it with several latest restoration models, including both image restoration and video restoration models. Qualitative and quantitative comparison results on the collected IVUS datasets demonstrate that our method has achieved outstanding performance and its potential clinical value.

Intravascular ultrasound (IVUS) can provide high-resolution cross-sectional images of coronary arteries, showing detailed information of the vascular lumen, tube wall, and athermanous plaques, which is helpful for the discovery or identification of early coronary atherosclerotic plaques. Multiple parameters extractable from the IVUS image can help the cardiologist analyze the pathology and assist in disease diagnosis and postoperative treatment. Typically, cardiologists manually label the intima and adventitia in the IVUS image, and obtain a limited number of parameters through the IVUS instrument, which is time consuming and labor-intensive. To assist the cardiologist in automatically obtaining more clinically relevant value parameters, a fully automatic IVUS multiparameter extraction framework is proposed. Based on the intima and adventitia obtained by DeepLab V3+, we propose a targeted noise reduction preprocessing framework adapted to IVUS. The framework implements the basic parameter extraction of IVUS through two newly proposed algorithms. And through the standard medical formula, the basic parameters are converted into 10 standard medical indicators. Standardized medical indicators are obtained by clinically relevant basic parameters. In terms of accuracy, this article used a clinical database obtained from Qilu Hospital of Shandong University and compared the results of the framework with the gold standard of cardiologists. The relative error of continuous IVUS main parameters between frames did not exceed 10.10%. The relative error of independent IVUS did not exceed 10.03%. Based on the distribution consistency of the parameters and the gold standard, a Bland-Altman plot of the parameters is proposed. It was verified that this distribution is basically in line with the gold standard of cardiologists. The algorithm in this article obtained a total of 10 parameters, far exceeding the parameters obtained by cardiologists and traditional IVUS machines. Its accuracy and speed can also meet the requirements of cardiologists for clinical diagnosis.

Clinical workflow of cardiac assessment on 2D echocardiography requires both accurate segmentation and quantification of the Left Ventricle (LV) from paired apical 4-chamber and 2-chamber. Moreover, uncertainty estimation is significant in clinically understanding the performance of a model. However, current research on 2D echocardiography ignores this vital task while joint segmentation with quantification, hence motivating the need for a unified optimization method. In this paper, we propose a multitask model with Task Relation Spatial co-Attention (referred as TRSA-Net) for joint segmentation, quantification, and uncertainty estimation on paired 2D echo. TRSA-Net achieves multitask joint learning by novelly exploring the spatial correlation between tasks. The task relation spatial co-attention learns the spatial mapping among task-specific features by non-local and co-excitation, which forcibly joints embedded spatial information in the segmentation and quantification. The Boundary-aware Structure Consistency (BSC) and Joint Indices Constraint (JIC) are integrated into the multitask learning optimization objective to guide the learning of segmentation and quantification paths. The BSC creatively promotes structural similarity of predictions, and JIC explores the internal relationship between three quantitative indices. We validate the efficacy of our TRSA-Net on the public CAMUS dataset. Extensive comparison and ablation experiments show that our approach can achieve competitive segmentation performance and highly accurate results on quantification.

Integrating the advantages of coronary angiography (CAG) and intravascular ultrasound (IVUS) images can significantly enhance the effectiveness of diagnosing coronary artery disease. However, current techniques for aligning CAG and IVUS rely solely on the manual determination of IVUS. To address this issue, this article introduces an innovative 3-D intelligent reconstruction pipeline for coronary arteries based on CAG and IVUS. The pipeline introduces three main innovations. First, the introduction of a label-text-guided multitask network model for classification and segmentation. This model facilitates end-to-end detection of blood vessel bifurcation and vascular wall segmentation, enhancing segmentation accuracy and calculating bifurcation width information by determining the number of bifurcated blood vessel frames. Second, the proposal of an improved IVUS 3-D reconstruction algorithm that integrates dual-boundary extraction, dual-line segment patch area extraction, and the moving filter-based 3-D smoothing algorithm into the reconstruction processing flow. This method not only presents branch information in the 3-D reconstruction of blood vessels, but also highlights plaque areas, improving the visual impact of the reconstruction. Third, the introduction of a registration algorithm for key branches of blood vessels based on CAG and IVUS. This algorithm aligns CAG and IVUS using key branch information extracted from CAG, achieving intelligent alignment and reconstruction without manual intervention. Experimental results demonstrate that this algorithm effectively aligns CAG and IVUS, assisting doctors in intuitively assessing and locating vascular plaque information, thereby making a significant contribution to the auxiliary diagnosis of coronary heart disease.