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An attention-based cascade R-CNN model for sternum fracture detection in X-ray images

CAAI Transactions on Intelligence Technology 2022年第4期7卷 658-670页

作者： Yang Jia Haijuan Wang Weiguang Chen Yagang Wang Bin Yang School of Computer Xi'an University of Posts and TelecommunicationsXi'anShaanxiChina Shaanxi Key Laboratory of Network Data Intelligent Processing Xi'an University of Posts and TelecommunicationsXi'anShaanxiChina Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'anShaanxiChina Department of Radiology Xi'an Honghui HospitalXi'anChina

Fracture is one of the most common and unexpected *** not treated in time,it may cause serious consequences such as joint stiffness,traumatic arthritis,and nerve *** computer vision technology to detect fractures can reduce the workload and misdiagnosis of fractures and also improve the fracture detection ***,there are still some problems in sternum fracture detection,such as the low detection rate of small and occult *** this work,the authors have constructed a dataset with 1227 labelled X-ray images for sternum fracture *** authors designed a fully automatic fracture detection model based on a deep convolution neural network(CNN).The authors used cascade R-CNN,attention mechanism,and atrous convolution to optimise the detection of small fractures in a large X-ray image with big local *** authors compared the detection results of YOLOv5 model,cascade R-CNN and other state-of-the-art *** authors found that the convolution neural network based on cascade and attention mechanism models has a better detection effect and arrives at an mAP of 0.71,which is much better than using the YOLOv5 model(mAP=0.44)and cascade R-CNN(mAP=0.55).

关键词： attention mechanism cascade R-CNN fracture detection X-ray image

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LOW-SAMPLING-FREQUENCY PLANE WAVE MEDICAL ULTRASOUND IMAGING BASED ON ADVERSARIAL LEARNING 30

LOW-SAMPLING-FREQUENCY PLANE WAVE MEDICAL ULTRASOUND IMAGING...

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30th IEEE International Conference on Image Processing (ICIP)

作者： Chen, Xiaoteng Chen, Junying South China Univ Technol Sch Software Engn Guangzhou 510006 Peoples R China Minist Educ Key Lab Big Data & Intelligent Robot SCUT Guangzhou Peoples R China

ISBN: (纸本)9781728198354

In plane wave medical ultrasound imaging, the analog echo signals are received via the transducer, and sampled as digital radio frequency data which is further processed by the beamforming algorithm to obtain the output ultrasound image. The sampling frequency of radio frequency data has a great impact on the image quality. The radio frequency data with higher sampling frequency is closer to the true distribution of the analog echo signals, leading to better image quality. However, high-frequency sampling chips are usually expensive. Thus, improving the plane wave imaging quality for radio frequency data with low sampling frequency has important practical significance. The ultrasound imaging quality is mainly affected by shallow features such as texture and color. However, low-sampling-frequency data has poor shallow features while high-sampling-frequency data has better shallow features. In this work, we propose an adversarial learning method to replace the shallow features of low-frequency data with the shallow features of high-frequency data, but retaining the deep features of low-frequency data. The experiment results demonstrate that the proposed method improves the plane wave imaging quality for low-sampling-frequency data by fusing shallow and deep features.

关键词： Feature fusion generative adversarial network low-frequency sampling plane wave imaging

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Policy Network-Based Dual-Agent Deep Reinforcement Learning for Multi-Resource Task Offloading in Multi-Access Edge Cloud Networks

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China Communications 2024年第4期21卷 53-73页

作者： Feng Chuan Zhang Xu Han Pengchao Ma Tianchun Gong Xiaoxue School of Communications and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing 400065China Institute of Intelligent Communication and Network Security Chongqing University of Posts and TelecommunicationsChongqing 400065China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and TelecommunicationsChongqing 400065China School of Information Engineering Guangdong University of TechnologyGuangzhou 510006China

The Multi-access Edge Cloud(MEC) networks extend cloud computing services and capabilities to the edge of the networks. By bringing computation and storage capabilities closer to end-users and connected devices, MEC networks can support a wide range of applications. MEC networks can also leverage various types of resources, including computation resources, network resources, radio resources,and location-based resources, to provide multidimensional resources for intelligent applications in 5/***, tasks generated by users often consist of multiple subtasks that require different types of resources. It is a challenging problem to offload multiresource task requests to the edge cloud aiming at maximizing benefits due to the heterogeneity of resources provided by devices. To address this issue,we mathematically model the task requests with multiple subtasks. Then, the problem of task offloading of multi-resource task requests is proved to be NP-hard. Furthermore, we propose a novel Dual-Agent Deep Reinforcement Learning algorithm with Node First and Link features(NF_L_DA_DRL) based on the policy network, to optimize the benefits generated by offloading multi-resource task requests in MEC networks. Finally, simulation results show that the proposed algorithm can effectively improve the benefit of task offloading with higher resource utilization compared with baseline algorithms.

关键词： benefit maximization deep reinforcement learning multi-access edge cloud task offloading

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Multi-Granularity Feature Fusion for Image-Guided Story Ending Generation

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2024年 32卷 3437-3449页

作者： Li, Pijian Huang, Qingbao Li, Zhigang Cai, Yi Shuang, Feng Li, Qing Guangxi Univ Sch Elect Engn Nanning 530004 Peoples R China South China Univ Technol Sch Software Engn Guangzhou 510006 Peoples R China MOE China Key Lab Big Data & Intelligent Robot SCUT Guangzhou 510006 Peoples R China Guangxi Key Lab Intelligent Control & Maintenance Nanning 530004 Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong 999077 Peoples R China

Image-guided Story Ending Generation aims at generating a reasonable and logical ending given a story context and an ending-related image. The existing models have achieved some success by fusing global image features with story context through an attention mechanism. However, they ignore the logical relationship between the story context and the image regions, and have not considered the high-level semantic features of the image such as visual sentiment. This may cause the generated ending inconsistent with the logic or sentiment of the given information. In this paper, we propose a Multi-Granularity feature Fusion (MGF) model to solve this problem. Concretely, we first employ an image sentiment extractor to grasp the sentiment features of the image as part of the global image features. We then design a scene subgraph selector to capture the image features of the key region by picking the scene subgraph most relevant to the context. Finally, we fuse the textual and visual features from object level, region level, and global level, respectively. Our model is thereby capable of effectively capturing the key region features and visual sentiment of the image, so as to generate a more logical and sentimental ending. Experimental results show that our MGF model outperforms the state-of-the-art models on most metrics.

关键词： Feature extraction Visualization Context modeling Task analysis Fuses Logic Skeleton Image-guided story ending generation story ending generation multi-granularity feature fusion scene subgraph image sentiment

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GraphConvNet: A Dual Network Utilizing Local Features Coupled with Structural Information for Predicting Knee Osteoarthritis

GraphConvNet: A Dual Network Utilizing Local Features Couple...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Tang, Pengju Dai, Dawei Zou, Kai Yang, Xionghui Wang, Guoyin Chongqing University of Posts and Telecommunications Chongqing Key Laboratory of Computational Intelligence Key Laboratory of Big Data Intelligent Computing Chongqing400065 China Chongqing Normal University College of Computer and Information Science Chongqing401331 China

ISBN: (纸本)9798350386226

Knee Osteoarthritis (KOA) is a common joint disease that severely affects the normal lives of patients. In clinical practice, the severity of KOA is commonly evaluated by observing radiographs of the knee joint However, this approach heavily relies on a doctor's clinical experience and exhibits a certain degree of subjectivity. In previous studies, various advanced deep convolutional neural network (CNN) models have been used to diagnose KOA. As known, CNN models often focus on learning the local detailed features for decision-making and lack attention to global structural information. In this study, we propose a dual network called GraphConvNet that integrates a visual graph neural network with a deep CNN to enhance representation learning by leveraging both local detailed features and global structural information. Our proposed method was evaluated using an Osteoarthritis Initiative (OAI) dataset and achieved an overall accuracy, recall, precision, and mean absolute error (MAE) of 75.24%, 77.75%, 74.33% and 0.283, respectively. Experiments demonstrate that our proposed method significantly improves the performance and achieves state-of-the-art performance. All codes are available at https://***/ddw2AIGROUP2CQUPT/GraphConvNet. © 2024 IEEE.

关键词： Joints (anatomy)

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A video archive management method based on blockchain technology 11

A video archive management method based on blockchain techno...

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Optoelectronic Imaging and Multimedia Technology XI 2024

作者： Wu, Rongteng College of Computer and Big Data Minjiang University Fuzhou350108 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350108 China

ISBN: (纸本)9781510682061

Video archives play a crucial role in various industries, from media and entertainment to security and surveillance. Managing these archives efficiently and securely is a growing challenge due to the increasing volume of video data being generated. In this paper, a video archive management method is presented to solve security and sensibility of the video files on blockchain. By encrypting video files and generating cryptographic hashes, the presented method can protect the integrity of the data and prevent unauthorized modifications. The method based on blockchain technology offers a compelling solution to the challenges faced in managing video archives. By combining the inherent security and transparency of blockchain with innovative data management algorithms, organizations can ensure the integrity, security, and accessibility of their video archives. © 2024 SPIE.

关键词： data integrity

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Learning refined features for open-world text classification with class description and commonsense knowledge

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WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS 2023年第2期26卷 637-660页

作者： Ren, Haopeng Li, Zeting Cai, Yi Tan, Xingwei Wu, Xin South China Univ Technol Key Lab Big Data & Intelligent Robot Minist Educ Guangzhou Peoples R China South China Univ Technol Sch Software Engn Guangzhou Peoples R China Univ Warwick Dept Comp Sci Coventry W Midlands England

Open-world classification requires a classifier not only to classify samples of the observed classes but also to detect samples which are not suitable to be classified as the known classes. State-of-the-art methods train a feature extractor to extract features for separating known classes with limited training data. Then some strategies, such as outlier detector, are used to reject samples from unknown classes based on the feature space. However, they are prone to extract the discriminative features among known classes and cannot model comprehensive features of known classes, which causes the classification errors when detecting the samples from the unknown classes in an open world scenario. Motivated by the theory of psychology and cognitive science, we utilize both class descriptions and commonsense knowledge summarized by human to refine the discriminant features and propose a regularization strategy. The regularization is incorporated into the feature extractor, which is enabled to further improve the performance of our model in an open-world environment. Extensive experiments and visualization analysis are conducted to evaluate the effectiveness of our proposed model.

关键词： Natural language processing Open world classification Prototype learning Knowledge Graph

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Compact and Accurate Adaptive Width Radial Basis Function Neural Network with Discriminative Features for Thyroid Gland Segmentation

Compact and Accurate Adaptive Width Radial Basis Function Ne...

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IEEE/RSJ International Conference on intelligent robots and Systems (IROS)

作者： You, Haijun Chen, Junying South China Univ Technol Sch Software gineering Guangzhou Peoples R China SCUT Minist Educ Key Lab Big Data & Intelligent Robot Guangzhou 510006 Peoples R China

ISBN: (纸本)9781665491907

The goal of this work is to develop a fast, compact, accurate, and robust neural network for automatic thyroid gland segmentation in medical ultrasound images when the computational resources are limited, such as portable intelligent medical diagnostic devices or robots. An adaptive width radial basis function neural network model with discriminative features is proposed in this work. The model combines the local characteristics and global context of the entire feature space, which improves the performance of the automatic thyroid gland segmentation, and enhances the data fitting capability and robustness of the neural network. A new feature selection method combining the measurements of area under the curve values and Pearson correlation coefficients is proposed to select six discriminative and low correlated features from a set of handcrafted features, which helps to construct a compact yet accurate radial basis function neural network. The proposed method is experimented on thyroid ultrasound images using five-fold cross validation. The proposed method achieves an average accuracy of 0.9770 and an average IoU of 0.7841, achieving competitive segmentation performance compared to convolutional neural networks, with thousands of times faster training speed on a CPU.

关键词： Neural network models

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Reinforcement Learning Framework for Delivery Capacity Scheduling with Vehicle Constraints 20

Reinforcement Learning Framework for Delivery Capacity Sched...

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20th IEEE International Conference on e-Business Engineering, ICEBE 2024

作者： Ma, Zhongneng Qin, Zhuorui Shi, Shuxia Wu, Qingyao South China University of Technology Guangzhou China School of Software Engineering South China University of Technology China Key Laboratory of Big Data and Intelligent Robot Ministry of Education China Guangzhou Henll Electronics Equipment Co. Ltd Guangzhou China Peng Cheng Laboratory China

ISBN: (纸本)9798350365856

Improving the risk prevention capability of grid operations is a crucial requirement for smart resilient grids, where efficient dispatch and scheduling of delivery capacity play a pivotal role in enhancing grid resilience. Therefore, it is necessary to develop targeted intelligent dispatch algorithms based on operational data. To address this issue, this paper proposes a framework with inherent constraints and utilizes reinforcement learning using recursive neural networks. A constraint module is designed to identify available vehicle combinations based on de-livery capacity, allocate goods considering vehicle number, load, and size constraints, and generate delivery routes sequentially. Additionally, a reinforcement learning model is introduced, using negative trip length as a reward signal, optimizing parameters of the recursive neural network through policy gradient methods to automatically improve strategies for selecting optimal deliv-ery routes with improved vehicle combinations. To validate the effectiveness of this algorithm, extensive experiments were conducted using both real-world application validation data and the TSPLIB public dataset. Within a limited computational budget, the proposed algorithm outperforms several mature methods in terms of effectiveness and real-time performance, achieving near-optimal results in Euclidean graphs with nearly 100 nodes. These results demonstrate that this approach effectively addresses the intelligent scheduling of delivery capacity in digital twin systems for power storage © 2024 IEEE.

关键词： Budget control

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Agent Planning with World Knowledge Model 38

Agent Planning with World Knowledge Model

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Qiao, Shuofei Fang, Runnan Zhang, Ningyu Zhu, Yuqi Chen, Xiang Deng, Shumin Jiang, Yong Xie, Pengjun Huang, Fei Chen, Huajun Zhejiang University China National University of Singapore NUS-NCS Joint Lab Singapore Alibaba Group China Zhejiang Key Laboratory of Big Data Intelligent Computing China

Recent endeavors towards directly using large language models (LLMs) as agent models to execute interactive planning tasks have shown commendable results. Despite their achievements, however, they still struggle with brainless trial-and-error in global planning and generating hallucinatory actions in local planning due to their poor understanding of the "real" physical world. Imitating humans' mental world knowledge model which provides global prior knowledge before the task and maintains local dynamic knowledge during the task, in this paper, we introduce parametric World Knowledge Model (WKM) to facilitate agent planning. Concretely, we steer the agent model to self-synthesize knowledge from both expert and sampled trajectories. Then we develop WKM, providing prior task knowledge to guide the global planning and dynamic state knowledge to assist the local planning. Experimental results on three complex real-world simulated datasets with three state-of-the-art open-source LLMs, Mistral-7B, Gemma-7B, and Llama-3-8B, demonstrate that our method can achieve superior performance compared to various strong baselines. Other interesting findings include: 1) our instance-level task knowledge can generalize better to unseen tasks, 2) weak WKM can guide strong agent model planning, and 3) unified WKM training has promising potential for further development. © 2024 Neural information processing systems foundation. All rights reserved.

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