Closed spina bifida is a high-incidence developmental disorder among rare fetal diseases. Its signs in ultrasound imaging are subtle, making it prone to misdiagnosis and heavily reliant on sonographers' experience. Therefore, we propose a novel semantic enhancement framework incorporating projected attention for the automated screening of closed spina bifida through precise landmark detection. In this method, we utilize a multi-granularity deep supervision and voting mechanism to generate point-specific features and reconstruct saliency maps for each landmark, effectively reducing interference from homogeneous high-echogenic noise in ultrasound images while preserving rich semantic information. Additionally, a coordinate attention projection module is designed to convert the 2D landmark probability maps into one-dimensional vectors, ensuring low computational complexity along with precise coordinate regression. The clinical application potential of this intelligent system is significant, as it facilitates automated fetal spine counting and anatomical measurement, enabling early warnings of diseases based on identified anomalies. Extensive experiments comparing our method with advanced baselines on an in-house dataset and two public datasets demonstrate its clear advantage in computational complexity and accuracy.

Semi-supervised segmentation is gaining popularity in medical image analysis due to challenges in data acquisition and annotation. However, most methods focus on generating additional training pairs from unlabeled data through augmentation or perturbation for contrastive learning, often overlooking the unique characteristics and inherent priors of medical images. We identified two key anatomical priors in fetal cardiac ultrasound images: (1) anatomies have consistent shapes and locations due to standard views captured by sonographers from fixed angles; (2) category pixels are densely clustered, with each structure appearing only once per image. We propose AP-Net, which uses an anatomical prior generation module, a prior-feature fusion module, and a category-aware cropping strategy to effectively leverage these anatomical priors. Experiments on a real-world fetal cardiac ultrasound dataset show that AP-Net outperforms classical supervised and leading semi-supervised methods, with each component enhancing its performance.

As artificial intelligence progresses in the field of medical ultrasound image analysis, mitigating catastrophic forgetting during continuous learning processes in disease diagnosis and fetal ultrasound assistance is crucial. Inspired by the significant performance improvements achieved in various downstream visual tasks through advanced visual representations, we introduce a novel approach called Masked Ultrasound Image Modeling (MUIM) to prevent forgetting of new disease diagnosis tasks. In this approach, MUIM initially pre-trains on ultrasound images specific to the current task. By utilizing a masked autoencoder to train on unlabeled ultrasound images, it learns highly abstract representations of task-relevant information. To ensure the model’s adaptability to new tasks, we propose contrastive Historical-Current Learning strategy, which enhances the model’s ability to retain and integrate knowledge from previous tasks while learning new ones. By incorporating knowledge distillation loss and the exponential moving average (EMA) technique for joint inference and continuously updating new knowledge into the historical expert model, our approach enables the model to adaptively learn new tasks while preventing forgetting of old ones. We conducted extensive experiments on three datasets: the Breast Ultrasound Image dataset (BUSI), the Algeria Thyroid Ultrasound Image dataset (AUITD), and our designed Obstetrics and Gynecology Ultrasound Dataset (USOGD). The results demonstrate that our method significantly reduces the forgetting of old task knowledge while outperforming state-of-the-art methods in classification accuracy.

In the rapidly evolving landscape of blockchain technology, security emerges as a paramount concern. This paper introduces an innovative blockchain security threat awareness platform, designed to comprehensively address the multifaceted security challenges within blockchain networks, particularly focusing on Ethereum contracts. Central to the platform is a dual-database architecture, blending a NoSQL database with a graph database, enhancing data management, and enabling intricate transaction network visualizations. The platform's Threat Detection module, utilizing Large Language Models (LLMs) in conjunction with traditional methods, offers a novel approach to identifying and categorizing vulnerabilities in Ethereum smart contracts. Complementing this, the Threat Evidence Collection module provides detailed post-attack analysis, tracing transactions to their sources and evaluating address risks. This module's capabilities extend to producing statistical reports, including the transactional history and risk evaluation of individual addresses. Demonstrated on the Ethereum blockchain, the platform showcases its proficiency in handling complex data, rapid threat detection, and extensive forensic analysis, presenting a robust solution to fortifying blockchain security and offering a proactive defense mechanism for users and developers in the blockchain environment.

Echocardiography is an essential procedure for the prenatal examination of the fetus for congenital heart disease (CHD). Accurate segmentation of key anatomical structures in a four-chamber view is an essential step in measuring fetal growth parameters and diagnosing CHD. Currently, most obstetricians perform segmentation tasks manually, but the pixel-level operation is labor-intensive and requires extensive anatomical knowledge and clinical experience. As such, efficiently and accurately detecting structures from real-world fetal ultrasound images is a key challenge. In this paper, we propose a YOLOX-based deep instance segmentation neural network (i.e., IS-YOLOX) for cardiac anatomical structure location and segmentation in fetal ultrasound images. Specifically, we reconstruct a new instance segmentation branch based on a multi-task deep learning framework. We then design a new multi-level non-maximum suppression (NMS) mechanism to further improve the segmentation performance that consists of three levels of selection. Moreover, unlike two-stage instance segmentation approaches, our method does not rely on object detection results. To the best of our knowledge, this is the first study regarding instance segmentation on 13 types of anatomical structures in the fetal four-chamber view. Extensive experiments were carried out on clinical datasets, and the experimental results show that our method outperforms nine competitive baselines.

The apical four-chamber (A4C) view in fetal echocardiography is a prenatal examination widely used for the early diagnosis of congenital heart disease (CHD). Accurate segmentation of A4C key anatomical structures is the basis for automatic measurement of growth parameters and necessary disease diagnosis. However, due to the ultrasound imaging arising from artefacts and scattered noise, the variability of anatomical structures in different gestational weeks, and the discontinuity of anatomical structure boundaries, accurately segmenting the fetal heart organ in the A4C view is a very challenging task. To this end, we propose to combine an explicit Feature Pyramid Network (FPN), MobileNet and UNet, i.e., MobileUNet-FPN, for the segmentation of 13 key heart structures. To our knowledge, this is the first AI-based method that can segment so many anatomical structures in fetal A4C view. We split the MobileNet backbone network into four stages and use the features of these four phases as the encoder and the upsampling operation as the decoder. We build an explicit FPN network to enhance multi-scale semantic information and ultimately generate segmentation masks of key anatomical structures. In addition, we design a multi-level edge computing system and deploy the distributed edge nodes in different hospitals and city servers, respectively. Then, we train the MobileUNet-FPN model in parallel at each edge node to effectively reduce the network communication overhead. Extensive experiments are conducted and the results show the superior performance of the proposed model on the fetal A4C and femoral-length images.

It is known that the Pareto-based approach is not well suited for optimization problems with a large number of objectives, even though it is a class of mainstream methods in multiobjective optimization. Typically, a Pareto-based algorithm comprises two parts: 1) a Pareto dominance-based criterion and 2) a diversity estimator. The former guides the selection toward the optimal front, while the latter promotes the diversity of the population. However, the Pareto dominance-based criterion becomes ineffective in solving optimization problems with many objectives (e.g., more than 3) and, thus, the diversity estimator will determine the performance of the algorithm. Unfortunately, the diversity estimator usually has a strong bias toward dominance resistance solutions (DRSs), thereby failing to push the population forward. DRSs are solutions that are far away from the Pareto-optimal front but cannot be easily dominated. In this article, we propose a new Pareto-based algorithm to resolve the above issue. First, to eliminate the DRSs, we design an interquartile range method to preprocess the solution set. Second, to balance convergence and diversity, we present a penalty mechanism of alternating operations between selection and penalty. The proposed algorithm is compared with five state-of-the-art algorithms on a number of well-known benchmarks with 3-15 objectives. The experimental results show that the proposed algorithm can perform well on most of the test functions and generally outperforms its competitors.

Intelligent ultrasound imaging based on deep learning is one of the important applications in the field of intelligent medical care. In this article, we propose an automatic fetal ultrasound standard plane recognition (FUSPR) model based on deep learning in the Industrial Internet of Things (IIoT) environment. We build a distributed ultrasound data processing and predicting platform by using the IIoT and high-performance computing (HPC) technology. The FUSPR model deployed in the HPC center consists of a convolutional neural network (CNN) component and a recurrent neural network (RNN) component, which learns the spatial and temporal features of the ultrasound video stream by using multitask learning, respectively. The CNN component identifies fetal key anatomical structures from each video frame and accurately recognizes the potential four fetal standard planes. The RNN component obtains the temporal information between adjacent frames, and it realizes precise localization and tracking of fetal organs across frames. In addition, we introduce two feature fusion strategies into the FUSPR model, i.e., CNN fusion and RNN fusion, to fit the spatial sequence and motion representation in the video stream, thereby effectively improving the accuracy and robustness of the model. Extensive experiments conducted on more than 1000 ultrasound videos show that the FUSPR model is superior to the competing baselines in terms of accuracy and performance.

To accurately detect and track the thyroid nodules in a video is a crucial step in the thyroid screening for identification of benign and malignant nodules in computer-aided diagnosis (CAD) systems. Most existing methods just perform excellent on static frames selected manually from ultrasound videos. However, manual acquisition is labor-intensive work. To make the thyroid screening process in a more natural way with less labor operations, we develop a well-designed framework suitable for practical applications for thyroid nodule detection in ultrasound videos. Particularly, in order to make full use of the characteristics of thyroid videos, we propose a novel post-processing approach, called Cache-Track, which exploits the contextual relation among video frames to propagate the detection results into adjacent frames to refine the detection results. Additionally, our method can not only detect and count thyroid nodules, but also track and monitor surrounding tissues, which can greatly reduce the labor work and achieve computer-aided diagnosis. Experimental results show that our method performs better in balancing accuracy and speed.

Crowdsensing is a popular method that leverages a crowd of sensor users to collect data. For many crowdsensing applications, the collected raw data need to be preprocessed before further analysis, and the preprocessing work is mainly done by the crowdsourcer. However, as the amount of collected data increases, this type of preprocessing approach has many disadvantages. In this article, we construct monetary-based incentive mechanisms to motivate users to preprocess the collected raw data for the crowdsourcer. For two common crowdsensing scenarios, we propose two system models, which are the single-task-multiple-participants (STMP) model and the multiple-tasks-multiple-participants (MTMP) model. In the STMP model, we design an incentive mechanism based on game theory and prove that there is a Nash equilibrium. In the MTMP model, we develop an incentive mechanism based on an auction and demonstrate that the incentive mechanism has the desirable properties of truthfulness, individual rationality, profitability, and computational efficiency. Furthermore, the utility maximization problems of the crowdsourcer and users are simultaneously considered in our incentive mechanisms. Through theoretical analysis and extensive experiments, we evaluate the performance of our incentive mechanisms.

Motion-compensated frame interpolation (MCFI), a frame-interpolation technique to increase the motion continuity of low frame-rate video, can be utilized by counterfeiters for faking high bitrate video or splicing videos with different frame rates. For existing MCFI detectors, their performances are degraded under real-world scenarios such as H.264/AVC compression, noise, or blur. To address this issue, a robust MCFI detector is proposed to locate interpolated frames. By analyzing the distribution of residual energies within interpolated frames, we observe that there exist strong correlations between artifact regions and high residual energies. Thus, an artifact indicated map is introduced to select candidate artifact regions. Then, Tchebichef moments (TMs) are exploited to characterize the blurring effects or deformed structures among these regions. Specifically, the mean value of absolute high-order TMs of selected regions is used to model these temporal inconsistencies. Finally, a sliding window is adopted to locate interpolated frames, which are further refined by three post-processing operations. Chrominance information is also integrated with luminance information for robust identification of interpolated frames. Extensive experimental results show that compared with the state-of-the-art MCFI detectors, the proposed approach is more robust for compressed videos under various real-world scenarios.