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PACE: Reputation-Driven Hierarchical Incentive Mechanism for Trusted Healthcare Transactions in Model Marketplace

IEEE Transactions on Consumer Electronics 2024年

作者： Lu, Jianfeng Dou, Xiaochi Rathore, Bharati Jhaveri, Rutvij H. Seid, Abegaz Mohammed Erbad, Aiman Wuhan University of Science and Technology School of Computer Science and Technology Wuhan430065 China Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System China Ministry of Education Key Laboratory of Social Computing and Cognitive Intelligence Dalian University of Technology China United Kingdom Pandit Deendayal Energy University Department of Computer Science and Engineering School of Technology India Hamad Bin Khalifa University College of Science and Engineering Doha Qatar Qatar University College of Engineering Doha Qatar

Federated learning-based model marketplaces have the potential to securely leverage healthcare data for efficient healthcare transactions. However, the willingness to participate in this marketplace is severely hindered by self-interested model sellers, who maximize their personal gains via distorting the quality of their models. To this end, we propose a rePutation-driven hierArchical inCentive mEchanism, called PACE, which combines contract cooperation and game pricing to motivate model sellers to be honest, thereby improving the sustainability of the model marketplace via two-layer cooperation. Specifically, at the training layer, we design a reward payment rule based on multidimensional contracts, while at the trading layer, we use a competitive market pricing game to develop the accuracy pricing rule. In particular, the above rules cleverly combine the fine-grained model sellers' reputations with the willingness of healthcare devices to indirectly affect the model sellers' utilities. Theoretically, we prove that PACE can effectively incentivize model sellers to truthfully report model quality while satisfying both individual rationality and incentive compatibility with a fairness guarantee. Experimental results conducted on four real-world datasets show that our PACE improves the global performance by up to 12.85% and reduces the global loss by at least 19.23% compared with the state-of-the-art baselines. © 1975-2011 IEEE.

关键词： Marketplaces

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Prediction of hot spots in protein-protein interaction by Nine-Pipeline Ensemble Learning strategy

Prediction of hot spots in protein-protein interaction by Ni...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Hu, Jing Li, Zonghao Zhang, Xiaolong Chen, Nansheng Wuhan University of Science and Technology School of Computer Science and Technology Hubei Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China Simon Fraser University Molecular Biology and Biochemistry BC Canada

ISBN: (纸本)9781665401265

This paper proposes a NPEL (Nine-Pipeline Ensemble Learning) strategy based on machine Learning algorithm to predict protein-protein interaction hotspots by training amino acid composition, surface area, amino acid chains and other complex/interface-related structural information. We applied Random Forest, Linear Svm, KNN, Gaussian Naive Bayes, Multi-layer Perceptron Neural Network, Adaboost, XGBoost etc. nine machine learning algorithms combination into an independent pipeline to predict protein hot spots, and the final results are optimized through voting and stacking scheme. In the stacking result of XGBoost and Logistic Regression, the highest accuracy is 0.8462 and improve the indicators of the pipeline results greatly. © 2021 IEEE.

关键词： Proteins

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Merging Clinical Knowledge into Large Language Models for Medical Research and Applications: A Survey

arXiv

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arXiv 2025年

作者： Li, Qiyuan Liu, Haijiang Guo, Caicai Chen, Deyu Wang, Meng Gao, Feng Gu, Jinguang College of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Hubei Wuhan430065 China The Key Laboratory of Rich-Media Knowledge Organization Service of Digital Publishing Content Institute of Scientific and Technical Information of China Beijing100038 China School of Computer Science and Technology Huazhong University of Science and Technology Hubei Wuhan430074 China School of Cyber Science and Engineering Wuhan University Hubei Wuhan430072 China

Clinical knowledge is the collection of information learned from studies on the causes, prognosis, diagnosis, and treatment of diseases. This type of knowledge can improve curing performances, and promote physical health. With the emergence of large language models (LLMs), medical artificial intelligence (medical AI), which aims to apply academic medical AI systems to real-world medical scenarios, has entered a new age of development, resulting in excellent works such as DoctorGPT and Pangu-Drug from academic and industrial researches. However, the field lacks a comprehensive compendium and comparison of building medical AI systems from academia and industry. Therefore, this survey focuses on the building paradigms of medical AI systems including the use of clinical databases, datasets, training pipelines, integrating medical knowledge graphs, system applications, and evaluation systems. We hope that this survey can help relevant practical researchers understand the current performance of academic models in various fields of healthcare, as well as the potential problems and future directions for implementing these scientific achievements. Copyright © 2025, The Authors. All rights reserved.

关键词： Clinical research

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FPGA implementation of SAR bi-channel synthesis based on inverse filtering 5

FPGA implementation of SAR bi-channel synthesis based on inv...

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IET International Radar Conference 2020, IET IRC 2020

作者： Shao, Xin Shi, Hao Chen, Liang Zhang, Na Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing China Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing China Beijing Institute of Technology Chongqing Innovation Center Chongqing China

ISBN: (纸本)9781839535635

In a multi-channel synthetic aperture radar (MSAR) system, non-uniform azimuth sampling is an important factor affecting imaging. If it is not processed and then imaged directly, it will inevitably lead to deterioration of imaging performance. The channel synthesis algorithm based on inverse filtering is a method to solve this problem. However, in actual applications, it is necessary to perform specific optimization processing on the algorithm to meet real-time requirements. This paper designs a new architecture for efficient implementation of channel synthesis algorithms. The design makes full use of the parallel characteristics of FPGA to improve the speed of the algorithm. By optimizing the DDR read-write control logic, the data transfer rate is improved. Finally, the Modelsim software is used to obtain the simulation results. At 150 MHz, the processing speed could reach 461.6MSamples/s (64bit/sample). © 2020 Institution of Engineering and technology. All rights reserved.

关键词： Data transfer

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A Diabetic Retinopathy Classification Method Based on Novel Attention Mechanism 17th

A Diabetic Retinopathy Classification Method Based on Novel ...

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17th International Conference on Intelligent Computing, ICIC 2021

作者： Zou, Jinfan Zhang, Xiaolong Lin, Xiaoli School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Institute of Big Data Science and Engineering Wuhan University of Science and Technology Wuhan China Hubei Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9783030845216

The convolutional neural network (CNN) has been effectively used to do feature extraction in medical image analysis including fundus images for diabetic retinopathy. However, some important detail features are easily ignored, which causes low prediction accuracy. To solve this problem, a CNN feature extraction method based on a novel pixel-level attention mechanism is proposed to achieve an efficient and accurate identification of diabetic retinopathy. Firstly, data normalization and data augmentation are used to improve the quality of datasets. Then, according to the structure characteristics of the two backbone networks Inception- ResnetV2 and EfficientNet-B5, the corresponding attention modules are introduced respectively. Finally, attention networks are trained as feature extractors of fundus images, and complementary deep attention feature descriptors are formed through feature fusion, which can effectively improve the accuracy of the classification model. The experimental results on EyePACS, Messidor and OIA datasets show that the proposed method outperforms the previous ones. © 2021, Springer Nature Switzerland AG.

关键词： Image classification

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Efficient Robust Principal Component Analysis via Block Krylov Iteration and CUR Decomposition

Efficient Robust Principal Component Analysis via Block Kryl...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Shun Fang Zhengqin Xu Shiqian Wu Shoulie Xie School of Information Science and Engineering Wuhan University of Science and Technology China Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial Systems Signal Processing RF & Optical Dept. Institute for Infocomm Research A*STAR Singapore

Robust principal component analysis (RPCA) is widely studied in computer vision. Recently an adaptive rank estimate based RPCA has achieved top performance in low-level vision tasks without the prior rank, but both the rank estimate and RPCA optimization algorithm involve singular value decomposition, which requires extremely huge computational resource for large-scale matrices. To address these issues, an efficient RPCA (eRPCA) algorithm is proposed based on block Krylov iteration and CUR decomposition in this paper. Specifically, the Krylov iteration method is employed to approximate the eigenvalue decomposition in the rank estimation, which requires $O(ndrq+n(rq)^{2})$ for an $(n\times d)$ input matrix, in which $q$ is a parameter with a small value, $r$ is the target rank. Based on the estimated rank, CUR decomposition is adopted to replace SVD in updating low-rank matrix component, whose complexity reduces from $O(rnd)$ to $O(r^{2}n)$ per iteration. Experimental results verify the efficiency and effectiveness of the proposed eRPCA over the state-of-the-art methods in various low-level vision applications.

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SAR Parameter Estimation Method for Rectangle Plane Based on information Geometry

SAR Parameter Estimation Method for Rectangle Plane Based on...

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2019 IEEE International Conference on Signal, information and Data processing, ICSIDP 2019

作者： Wen, Yuhan Chen, Xinliang Wei, Yangkai Fan, Yujie Zeng, Tao DIng, Zegang Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing China

ISBN: (纸本)9781728123455

After establishing scattering models, the traditional synthetic aperture radar (SAR) parameter estimation methods usually utilize vanilla gradients to estimate target parameters from SAR echoes or images. However, the vanilla gradients will lead to lower iteration efficiency. Hence, in this paper, to take the rectangle plane as an example, we propose a SAR parameter estimation method based on the information geometry (SPEM-IG) to solve the above problem. The SPEM-IG adopts the natural gradient to estimate the parameters based on the information geometry optimization algorithm. The central objective of SPEM-IG is to project the least square function into probability distribution space and find the optimal solution in the new parameter space. In order to reduce the computational complexity caused by the multi-peak feature of least squares function, SPEM-IG utilizes the range of parameters rather than its exact value in calculating the value of least squares function, and takes the optimal solution in this range as the result of the current iteration process. By considering the geometric structure of manifolds, SPEM-IG calculates the optimal gradient direction called natural gradient to obtain faster convergence rate. The experimental results show that the SPEM-IG is effective for estimating target parameters. Besides, SPEM-IG has good performance even at low signal-to-noise ratios of SAR images. © 2019 IEEE.

关键词： Synthetic aperture radar

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Select Informative Samples for Night-time Vehicle Detection Benchmark in Urban Scenes

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Remote Sensing 2023年第17期15卷 4310-4310页

作者： Wang, Xiao Tu, Xingyue Al-Hassani, Baraa Lin, Chia-Wen Xu, Xin School of Computer Science and Technology Wuhan University of Science and Technology Wuhan430081 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology Wuhan430081 China School of Computer Science Wuhan University Wuhan430081 China Department of Electrical Engineering Industrial Technology Research Institute National Tsing Hua University Hsinchu30013 Taiwan

Night-time vehicle detection plays a vital role due to the high incidence of abnormal events in our daily security field. However, existing studies mainly focus on vehicle detection in autonomous driving and traffic intersection scenes, but ignore urban scenes. There are vast differences between these scenes, such as viewpoint, position, illumination, etc. In this paper, the authors present a night-time vehicle detection dataset collected from urban scenes, named Vehicle Detection in Night-time Urban Scene (VD-NUS). The VD-NUS dataset consists of more than 100 K challenging images, comprising a total of about 500 K labelled vehicles. This paper introduces a vehicle detection framework via an active auxiliary mechanism (AAM) to reduce the annotation workload. The proposed AAM framework can actively select the informative sample for annotation by estimating its uncertainty and locational instability. Furthermore, this paper proposes a computer-assisted detection module embedded in the AAM framework to help human annotators to rapidly and accurately label the selected data. AAM outperformed the baseline method (random sampling) by up to 0.91 AP and 3.0 MR−2 on the VD-NUS dataset. © 2023 by the authors.

关键词： Vehicles

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Inferring DTIs Based on Similarity Clustering and CaGCN-DTI Model from Heterogeneous Network

Inferring DTIs Based on Similarity Clustering and CaGCN-DTI ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Aoxing Li Xiaoli Lin Haiping Yu Hubei Key Laboratory of Intelligent Information Processing and Real-time Industrial System School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Key Laboratory of Intelligent Information Processing and Real-time Industrial System School of Computer Science Huanggang Normal University Wuhan China

ISBN: (纸本)9781665429825

Although much progress has been made in new drug development, it is still a costly, complicated and less efficient process. Therefore, drug repositioning studies are highly desirable. So far, many methods based on known drug-target interactions (DTIs) have been designed to detect potential DTIs, but there are many challenges for improving the performance of prediction DTIs. This paper proposes a new method (CaGCN-DTI) for DTIs prediction, which uses a heterogeneous network that incorporates a wide variety of biological data (drug, target, disease, and side effect) to discover potential interactions between drugs and targets. First, the drug-protein similarity network is preprocessed by Spectral clustering, then the graph convolutional network (GCN) combined with attention mechanism and random walk with restart (RWR) is used to aggregate message transmission to the network. Finally, the embedding vectors are used to discover potential DTIs by matrix decomposition. Experiments are performed based on ten-fold cross-validation, and the results show that CaGCN-DTI outperforms other previous methods in terms of AUC and AUPR.

关键词： Drugs Proteins Protein engineering Computational modeling Aggregates Conferences Predictive models

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Pruning convolutional neural networks for remote sensing images 5

Pruning convolutional neural networks for remote sensing ima...

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IET International Radar Conference 2020, IET IRC 2020

作者： Qi, Baogui Xu, Yinsheng Chen, He Chen, Liang Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing Institute of Technology Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China Shanghai Institute of Satellite Engineering Shanghai200240 China

ISBN: (纸本)9781839535635

Deep convolutional neural networks (DCNNs) have shown excellent performance in remote sensing image processing. However, DCNNs contain many parameters and require many computational resources. It is very difficult to deploy DCNNs on spaceborne, airborne, or other mobile platforms with limited resources. It is necessary to compress the parameters of DCNNs. In this paper, we introduce a novel network pruning strategy that can prune redundant filters or weights of networks and preserve the performance of DCNNs in remote sensing image classification tasks. Unlike traditional pruning methods using a single criterion, this paper improves the pruning process based on the combined pruning method of multiple criteria. VGGNet and ResNet are used to validate our method. The dataset used in our experiments is the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset. The results demonstrate that our method is effective and can maintain the accuracy of a pruned model in remote sensing image classification tasks. © 2020 Institution of Engineering and technology. All rights reserved.

关键词： Deep neural networks

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