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A Multiscale Superpixel-Level Salient Object Detection Model Using Local-Global Contrast Cue

Journal of Shanghai Jiaotong university(Science) 2017年第1期22卷 121-128页

作者：穆楠徐新王英林张晓龙 School of Computer Science and Technology Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology School of Information Management and Engineering Shanghai University of Finance and Economics

The goal of salient object detection is to estimate the regions which are most likely to attract human's visual attention. As an important image preprocessing procedure to reduce the computational complexity, salient object detection is still a challenging problem in computer vision. In this paper, we proposed a salient object detection model by integrating local and global superpixel contrast at multiple scales. Three features are computed to estimate the saliency of superpixel. Two optimization measures are utilized to refine the resulting saliency map. Extensive experiments with the state-of-the-art saliency models on four public datasets demonstrate the effectiveness of the proposed model.

关键词： salient object detection superpixel multiple scales local contrast global contrast TP 391.4 A

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CPR-Net: A Novel Joint Learning Network for Pulmonary Nodule Evaluation

CPR-Net: A Novel Joint Learning Network for Pulmonary Nodule...

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2023 IEEE International Conference on systems, Man, and Cybernetics, SMC 2023

作者： Chen, Gong Liu, Jun Li, Chen-Qian Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China

ISBN: (纸本)9798350337020

Lung cancer is the most common malignant tumor worldwide, with high mortality rates. Pulmonary nodule is a common manifestation of lung cancer. Accurate segmentation and detection of pulmonary nodules from CT scans are essential for proper assessment of patient prognosis. However, this task remains challenging due to various factors, such as class imbalance and the need for detailed characterization of lung nodule segmentation. To address these issues, we propose a novel network, CPR-Net (Convolutional Pyramid Residuals-Net), that combines segmentation and benign-malignant classification for CT images of lung nodules. Our approach utilizes a PBMM module to expand the perception field and enhance the representation capability of the model, allowing it to learn more detailed information. We evaluate the classification performance using accuracy, precision, recall, F1 score, AUC, and the segmentation performance using the Dice coefficient. Experiments on both publicly available datasets and self-constructed datasets demonstrate that our proposed method in this paper outperforms other methods in terms of classification and segmentation performance under limited labeled data conditions. Moreover, Our results suggest that this model has great potential for improving lung nodule diagnosis in radiology for heterogeneous intranodal images. © 2023 IEEE.

关键词： class imbalance multi-task learning pulmonary nodule

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An Inverse Power Generation Mechanism Based Fruit Fly Algorithm for Function Optimization

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Journal of systems Science & Complexity 2019年第2期32卷 634-656页

作者： LIU Ao DENG Xudong REN Liang LIU Ying LIU Bo School of Management Wuhan University of Science and Technology Center for Service Science and Engineering Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System School of Economics and Management Beihang University Academy of Mathematics and Systems Science Chinese Academy of Sciences

As a novel population-based optimization algorithm, fruit fly optimization(FFO) algorithm is inspired by the foraging behavior of fruit flies and possesses the advantages of simple search operations and easy implementation. Just like most population-based evolutionary algorithms, the basic FFO also suffers from being trapped in local optima for function optimization due to premature *** this paper, an improved FFO, named IPGS-FFO, is proposed in which two novel strategies are incorporated into the conventional FFO. Specifically, a smell sensitivity parameter together with an inverse power generation mechanism(IPGS) is introduced to enhance local exploitation. Moreover,a dynamic shrinking search radius strategy is incorporated so as to enhance the global exploration over search space by adaptively adjusting the searching area in the problem domain. The statistical performance of FFO, the proposed IPGS-FFO, three state-of-the-art FFO variants, and six metaheuristics are tested on twenty-six well-known unimodal and multimodal benchmark functions with dimension 30, respectively. Experimental results and comparisons show that the proposed IPGS-FFO achieves better performance than three FFO variants and competitive performance against six other meta-heuristics in terms of the solution accuracy and convergence rate.

关键词： Evolutionary algorithms fruit fly optimization function optimization meta-heuristics

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Development of multi-target tracking technique based on background modeling and particle filtering

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Sensors and Transducers 2014年第6期173卷 6-15页

作者： Xue-Chao, Liao Zhen-Xing, Liu School of Computer Science Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China School of Information Science and Engineering Wuhan University of Science and Technology Wuhan China

Based on implementing target tracking by means of particle filtering, a technique framework of tracking target by integrating particle filtering and background modeling is presented. The multi-target tracking (MTT) is classified into 5 modules as background modeling, multi-target tracking, initializing, re-initializing and particle filtering. Firstly, the author models each pixel of the image with Gaussian Mixture Model (GMM) to calculate the probability of background pixel in the current image so as to abstract foreground moving objects. Based on the background modeling, the algorithm flow and technique framework of generating the particle set of each object and particle filtering are presented. In the process of evaluating particle weight, in order to distinguish the different color features of the objects, the original algorithm (evaluating through Bhattacharyya distance) is improved. Only the color distribution of the foreground pixel in particle area after the background modeling is counted, therefore the accuracy and efficiency of target tracking are increased. The experiments prove that this algorithm can realize the effective tracking several moving persons. © 2014 IFSA Publishing, S. L.

关键词： Target tracking

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Discovering DTI and DDI by Knowledge Graph with MHRW and Improved Neural Network

Discovering DTI and DDI by Knowledge Graph with MHRW and Imp...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Zhang, Shuo Lin, Xiaoli Zhang, Xiaolong Wuhan University of Science and Technology Hubei Key Laboratory of Intelligent Information Processing and Real-time Industrial System School of Computer Science and Technology Wuhan China

ISBN: (纸本)9781665401265

Drug discovery is of great significance in medical and biological research, while the study of Drug-Target Interaction (DTI) and Drug-Drug Interaction (DDI) can help accelerate drug discovery progress. This paper proposes a new hybrid method for DTI prediction and DDI prediction, which is called MHRW2Vec-TBAN that combines graph representation learning and neural network. MHRW2VecTBAN first constructs knowledge graph KG-DTI and KG-DDI that integrate data related to drugs and targets. Then, an improved graph representation learning model, MHRW2Vec model, is used to obtain feature vectors of reflecting the network structure information for improving the performance of representation learning. Finally, the feature vectors obtained are input to the improved neural network model TextCNN-BiLSTM-Attention Network (TBAN). The experimental results show that, compared with other existing methods, our method could discover more deeper the relationship between drugs and their potential neighborhoods, and has great improvements in DTI prediction and DDI prediction. In addition, the case study of prediction COVID-19 DTI also shows that the proposed model has the potential for actual drug discovery. © 2021 IEEE.

关键词： Knowledge graph

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Drug-Target Interaction Prediction via Multiple Output Graph Convolutional Networks 17th

Drug-Target Interaction Prediction via Multiple Output Graph...

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17th International Conference on intelligent Computing, ICIC 2021

作者： Ye, Qing Zhang, Xiaolong Lin, Xiaoli 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

ISBN: (纸本)9783030845315

Computational prediction of drug-target interaction (DTI) is very important for the new drug discovery. Currently, graph convolutional networks (GCNs) have been gained a lot of momentum, as its performance on non-Euclidean data. Although drugs and targets are two typical non-Euclidean data, many problems are also existed when use GCNs in DTI prediction. Firstly, most state-of-the-art GCN models are prone to the vanishing gradient problem, which is more serious in DTI prediction, as the number of interactions is limit. Secondly, a suitable graph is hardly defined for GCN in DTI prediction, as the relationship between the samples is not explicitly provided. To overcome the above problems, in this paper, a multiple output graph convolutional network (MOGCN) based DTI prediction method is designed. MOGCN enhances its learning ability with many new designed auxiliary classifier layers and a new designed graph calculation method. Many auxiliary classifier layers can increase the gradient signal that gets propagated back, utilize multi-level features to train the model, and use the features produced by the higher, middle or lower layers in a unified framework. The graph calculation method can use both the label information and the manifold. The conducted experiments validate the effectiveness of our MOGCN. © 2021, Springer Nature Switzerland AG.

关键词： Forecasting

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Budget Constraint Task Allocation for Mobile Crowd Sensing with Hybrid Participant 16th

Budget Constraint Task Allocation for Mobile Crowd Sensing w...

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16th EAI International Conference on Collaborative Computing: Networking, Applications, and Worksharing, CollaborateCom 2020

作者： Wang, Xin Li, Peng Xiao, Junlei College of Computer Science and Technology Wuhan University of Science and Technology Hubei China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Hubei China

ISBN: (纸本)9783030675394

In the Mobile crowdsensing (MCS) system, the task allocation problem is a crucial problem. In this paper, we focus on the task allocation problem for hybrid participants with a budget-constrained MCS system. There are two types of participants: mobile participants and static participants. Mobile participants have low cost, large numbers, and flexibility. However, most of the sensing data they submitted are of low quality. On the other hand, static participants, such as city cameras, roadside infrastructure, have high-quality sensing data. Despite the benefit of high quality, static participants have less coverage and high cost. Given a budget, the problem is how to assign the task to the two types of participants, such that the social welfare is maximized. To solve the problem, we propose a reverse auction-based task allocation method (ORA) to select winning bids round by round. Then, a Shapley value based online algorithm (OAA) is proposed to ensure the task is finished. Moreover, we consider the different types of participants to have a different probability to finish tasks. We exploit the semi-Markov model to calculate the probability that participants finished tasks. We prove that the proposed task allocation method has truthfulness and individual rationality. We conduct extensive experiments to evaluate the performance of our system, and the evaluation results demonstrate the remarkable effect of the proposed task allocation method. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Budget control

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Generating Molecules Conditional on 3D Protein Pockets with HGAF

Generating Molecules Conditional on 3D Protein Pockets with ...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Zhu, Qinglian Lin, Xiaoli Zhang, Xiaolong Wuhan University of Science and Technology Hubei Key Laboratory of Intelligent Information Processing and Real-Time Industrial System School of Computer Science and Technology Wuhan China

ISBN: (纸本)9798350337488

Most current representations of protein pockets are the atom-pair graph, which ignore the global structural information of amino acids. Therefore, we propose a new molecular generation model, which uses the hypergraph to represent protein pocket structure, and combines the structural features obtained by atom-pair graph representation. These two levels of graphs are more capable of representing the complex structural information of protein pockets. Then, the graphs of the two levels of the protein pockets are input into the improved network model, which is named Hypergraph Graph Attention Fusion (HGAF), to obtain the embedding representation of the protein pockets, which is used as a condition to constrain the molecule generation. The molecules sampled by HGAF are subjected to quality assessment and docking targeting validation. Experimental results show that the molecules generated by proposed method can achieve better results in both of these assessment approaches. © 2023 IEEE.

关键词： 3D Protein Pockets Drug Design Graph Attention Networks

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Anti-3CLpro Molecular Design Based on the Model Constrained by Specific DTIs

Anti-3CLpro Molecular Design Based on the Model Constrained ...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Lin, Xiaoli Liu, Xuan Zhang, Xiaolong School of Computer Science and Technology Wuhan University of Science and Technology Hubei Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China

ISBN: (纸本)9798350337488

Computer-aided drug design and artificial intelligence-driven drug design have accelerated drug discovery. However, how to design effective drugs that have strong interaction ability with target proteins to further improve the efficacy of drugs in treating diseases remains a key issue. This paper proposes a new target-specific drug generative model 3CLpro2mol to generate new drug molecules, which uses features of drug-target interactions (DTIs) to constrain the correlation between the drug and the target protein. To obtain as many drug-target interaction features as possible from a small amount of data, a small molecule extraction strategy is proposed to ensure the diversity of small molecules in the training samples. To improve the efficiency and accuracy of the generative model, a TOP K sampling strategy is used to generate tokens, which can improve the rationality and diversity of the generated molecules. The experimental results show that the proposed model has the potential to generate small molecules that interact better with the target protein. © 2023 IEEE.

关键词： COVID-19 DTIs generative model molecular design molecular docking

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A Multi Target Drug Design Method Based on Target Protein Sequence and Feature Similarity

A Multi Target Drug Design Method Based on Target Protein Se...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Liu, Haoran Lin, Xiaoli Hu, Jing Zhang, Xiaolong Wuhan University of Science and Technology Hubei Key Laboratory of Intelligent Information Processing and Real-Time Industrial System School of Computer Science and Technology Wuhan China

ISBN: (纸本)9798350386226

This paper describes a multi target drug design method based on features of the target proteins. The multi target drug which inhibit multiple proteins have prospective applications, but design is difficult. In this study, the target protein sequences are embedded to obtain target features. The features of each target are independently encoded as target latent vectors, and the features of multiple targets are jointly encoded as target similarity latent vectors. Based on the features of targets and the similarity features among the targets, multi target drugs are efficiently designed. In the experiments, the designed multi target drugs can be docked with target proteins with a proprietary molecular structure according to the requirements of different target pocket structures. The excellent fitness among the molecular structure of multi target drugs and the protein structure of multiple targets confirms the performance of the proposed method. In the molecular docking part of the experi¬ments, the binding affinity of designed multi target drugs is far better than that in the previous studies, which administrates the better performance of this work. © 2024 IEEE.

关键词： Molecular docking

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