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Simultaneous segmentation and classification of colon cancer polyp images using a dual branch multi-task learning network

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Mathematical Biosciences and Engineering 2024年第2期21卷 2024-2049页

作者： Li, Chenqian Liu, Jun Tang, Jinshan School of Computer Science and Technology Wuhan University of Science and Technology Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan430065 China Department of Health Administration and Policy College of Public Health George Mason University FairfaxVA22030 United States

Accurate classification and segmentation of polyps are two important tasks in the diagnosis and treatment of colorectal cancers. Existing models perform segmentation and classification separately and do not fully make use of the correlation between the two tasks. Furthermore, polyps exhibit random regions and varying shapes and sizes, and they often share similar boundaries and backgrounds. However, existing models fail to consider these factors and thus are not robust because of their inherent limitations. To address these issues, we developed a multi-task network that performs both segmentation and classification simultaneously and can cope with the aforementioned factors effectively. Our proposed network possesses a dual-branch structure, comprising a transformer branch and a convolutional neural network (CNN) branch. This approach enhances local details within the global representation, improving both local feature awareness and global contextual understanding, thus contributing to the improved preservation of polyp-related information. Additionally, we have designed a feature interaction module (FIM) aimed at bridging the semantic gap between the two branches and facilitating the integration of diverse semantic information from both branches. This integration enables the full capture of global context information and local details related to polyps. To prevent the loss of edge detail information crucial for polyp identification, we have introduced a reverse attention boundary enhancement (RABE) module to gradually enhance edge structures and detailed information within polyp regions. Finally, we conducted extensive experiments on five publicly available datasets to evaluate the performance of our method in both polyp segmentation and classification tasks. The experimental results confirm that our proposed method outperforms other state-of-the-art methods. ©2024 the Author(s), licensee AIMS Press.

关键词： Classification (of information)

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Strengthened Dominance Relation NSGA-Ⅲ Algorithm Based on Differential Evolution to Solve Job Shop Scheduling Problem

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Computers, Materials & Continua 2024年第1期78卷 375-392页

作者： Liang Zeng Junyang Shi Yanyan Li Shanshan Wang Weigang Li School of Electrical and Electronic Engineering Hubei University of TechnologyWuhan430068China Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System Hubei University of TechnologyWuhan430068China School of Information Science and Engineering Wuhan University of Science and TechnologyWuhan430081China

The job shop scheduling problem is a classical combinatorial optimization challenge frequently encountered in manufacturing *** involves determining the optimal execution sequences for a set of jobs on various machines to maximize production efficiency and meet multiple *** Non-dominated Sorting Genetic Algorithm Ⅲ(NSGA-Ⅲ)is an effective approach for solving the multi-objective job shop scheduling ***,it has some limitations in solving scheduling problems,including inadequate global search capability,susceptibility to premature convergence,and challenges in balancing convergence and *** enhance its performance,this paper introduces a strengthened dominance relation NSGA-Ⅲ algorithm based on differential evolution(NSGA-Ⅲ-SD).By incorporating constrained differential evolution and simulated binary crossover genetic operators,this algorithm effectively improves NSGA-Ⅲ’s global search capability while mitigating pre-mature convergence ***,it introduces a reinforced dominance relation to address the trade-off between convergence and diversity in NSGA-Ⅲ.Additionally,effective encoding and decoding methods for discrete job shop scheduling are proposed,which can improve the overall performance of the algorithm without complex *** validate the algorithm’s effectiveness,NSGA-Ⅲ-SD is extensively compared with other advanced multi-objective optimization algorithms using 20 job shop scheduling test *** experimental results demonstrate that NSGA-Ⅲ-SD achieves better solution quality and diversity,proving its effectiveness in solving the multi-objective job shop scheduling problem.

关键词： Multi-objective job shop scheduling non-dominated sorting genetic algorithm differential evolution simulated binary crossover

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Prediction of piRNA-mRNA interactions based on an interactive inference network

Prediction of piRNA-mRNA interactions based on an interactiv...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Liu, Yajun Li, Ru Li, Aimin Fei, Rong Guo, Xie Wu, Fang-Xiang Xi'an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an China University of Saskatchewan Division of Biomedical Engineering Department of Computer Science Department of Mechanical Engineering Saskatoon Canada

ISBN: (纸本)9798350337488

As the largest class of small non-coding RNAs, piRNAs primarily present in the reproductive cells of mammals, which influence post-transcriptional processes of mRNAs in multiple ways. Effective methods for predicting piRNA and mRNA target relationships can help identify piRNA functions, investigate the possibility of piRNAs as biomarkers and therapeutic targets. In this study, we propose a computational approach for classifying the relationships of piRNA-mRNA pairs based on an interactive inference network (IIN). First, we gather piRNA-mRNA target data, collect sequence data by position alignment, and construct a benchmark dataset. Furthermore, a reliable negative set is constructed by positive-unlabeled learning. Finally, we view a piRNA and a mRNA sequence as a premise and hypothesis sentence, respectively, and IIN model is used to predict the relationship between them. The experiments demonstrate that our method effectively characterizes piRNA-mRNA interaction and could be beneficial for researchers to investigate piRNA functions. © 2023 IEEE.

关键词： interactive inference network noncoding RNA piRNA-mRNA interactions positive-unlabeled learning

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Efficient Reinforcement Learning for 3D LiDAR Navigation of Mobile Robot 41

Efficient Reinforcement Learning for 3D LiDAR Navigation of ...

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第41届中国控制会议

作者： Yu Zhai Zhe Liu Yanzi Miao Hesheng Wang China University of Mining and Technology MoE Key Lab of Artificial Intelligence Shanghai Jiao Tong University Department of Automation Key Laboratory of System Control and Information Processing of Ministry of EducationShanghai Engineering Research Center of Intelligent Control and ManagementShanghai Jiao Tong University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Developing an efficient automatic navigation system for mobile robots is challenging in the strange scenarios where robots can only observe the environment of the surrounding limited *** other distributed automatic navigation systems exist,they often require extracting semantic information to calculate navigation action,which requires extra modules to provide perceptual information and is not *** propose an end-to-end automatic navigation system based on the reinforcement learning *** particular,the raw 3 D LiDAR data is used to directly map an efficient navigation *** design a novel dense reward function to handle the reward sparsity issue and provide a graphical representation method to enable the efficient feature learning from the raw 3 D LiDAR data in our navigation *** addition,an imitation learning based policy initialization is introduced before the subsequent reinforcement learning,which increases the learning efficiency and,in the meantime,still encouraging the robot to explore all the potential states to achieve advanced performance than the imitated *** navigation model is trained in the Webots environment and the experimental results show that our model has efficient and flexible navigation performance in complex *** importantly,trained model can be easily extended to unfamiliar environments.

关键词： End-to-end Navigation Mobile robot Reinforcement Learning 3D LiDAR

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Semantic Correspondence with Peripheral Position Coding 13

Semantic Correspondence with Peripheral Position Coding

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13th International Conference on information Technology in Medicine and Education, ITME 2023

作者： Chen, Jinjian Li, Zuoyong Lai, Taotao Xie, Huosheng College of Computer and Data Science Fuzhou University Fuzhou350108 China College of Computer and Control Engineering Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou350121 China

ISBN: (纸本)9798350319156

Establishing dense correspondences between semantically similar images is a challenging task. Cost aggregation is a crucial step in finding correct dense correspondences, with the goal of optimizing the initial correlation map thereby removing the ambiguity of the correspondences. Current approaches use transformer architectures for cost aggregation, which lack local priors to adequately capture the local information contained in the correlation map. We propose to incorporate peripheral position coding into the transformer to explore the local information to obtain the matching set and call it the Peripheral Transformer Matcher (PTM). This coding technique partitions the overall receptive field of the self-attention mechanism into diverse peripheral regions, each with its own set of weights. By doing this, the proposed PTM gets a specific local prior by adding an inductive bias to the transformer models and making the initial correlation map less confusing. In addition, a local self-attention module is used to enhance the image features and obtain an enhanced initial correlation map. Comparisons of the experimental results with baselines on public datasets demonstrate the effectiveness of the proposed PTM. © 2023 IEEE.

关键词： Image enhancement

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Multi-adversarial Testing and Retraining for Improving the Robustness of Autonomous Driving Policies 42

Multi-adversarial Testing and Retraining for Improving the R...

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42nd Chinese control Conference, CCC 2023

作者： Liu, keyu Zhou, Junjie Wang, Lin School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai202400 China Key Laboratory of System Control and Information Process Ministry of Education of China Shanghai202400 China

ISBN: (纸本)9789887581543

Autonomous vehicles, especially those based on deep reinforcement learning, are known for their susceptibility to the adversarial perturbations. To enhance their robustness, it is imperative to not only detect their decision errors through testing, but also fortify their robustness against these errors. This paper proposes an iterative optimization method that trains multiple adversarial agents with varying adversarial intensities to identify decision errors in the driving vehicle and enhance its robustness by retraining to counteract these adversarial agents. The effectiveness of the method is evaluated in a lane-changing scenario and the results demonstrate improved robustness of deep reinforcement learning based autonomous driving strategies compared to the adversarial reinforcement learning with a single adversary. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Autonomous vehicles

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Lightweight LiDAR-Camera 3D Dynamic Object Detection and Multi-Class Trajectory Prediction

arXiv

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arXiv 2025年

作者： He, Yushen Zhao, Lei Deng, Tianchen Fang, Zipeng Chen, Weidong Institute of Medical Robotics Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China

Service mobile robots are often required to avoid dynamic objects while performing their tasks, but they usually have only limited computational resources. So we present a lightweight multi-modal framework for 3D object detection and trajectory prediction. Our system synergistically integrates LiDAR and camera inputs to achieve real-time perception of pedestrians, vehicles, and riders in 3D space. The framework proposes two novel modules: 1) a Cross-Modal Deformable Transformer (CMDT) for object detection with high accuracy and acceptable amount of computation, and 2) a Reference Trajectory-based Multi-Class Transformer (RTMCT) for efficient and diverse trajectory prediction of mult-class objects with flexible trajectory lengths. Evaluations on the CODa benchmark demonstrate superior performance over existing methods across detection (+2.03% in mAP) and trajectory prediction (-0.408m in minADE5 of pedestrians) metrics. Remarkably, the system exhibits exceptional deployability - when implemented on a wheelchair robot with an entry-level NVIDIA 3060 GPU, it achieves realtime inference at 13.2 fps. To facilitate reproducibility and practical deployment, we release the related code of the method at https://***/TossherO/3D Perception and its ROS inference version at https://***/TossherO/ros packages. Copyright © 2025, The Authors. All rights reserved.

关键词： Mobile robots

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Digital-Twin-Based Clock Synchronization in Industrial IoT systems 13

Digital-Twin-Based Clock Synchronization in Industrial IoT S...

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13th IEEE International Conference on CYBER Technology in Automation, control, and Intelligent systems, CYBER 2023

作者： Cheng, Ting Liu, Shuai Xu, Qimin Zhu, Shanying Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China School of Control Science and Engineering Shandong University Jinan250061 China

ISBN: (纸本)9798350315196

Close and efficient cooperation between devices in the Industrial Internet of Things (IIoT) requires precise clock synchronization as a prerequisite. The uncertainty of IIoT networks and the complexity of industrial environments have led to less precise clock synchronization achieved by traditional algorithms and higher network resource consumption. This paper proposes a digital-twin-based clock synchronization scheme for IIoT systems. By building the digital model for each local clock to predict their clock skew, the information interaction required to achieve synchronization is reduced. In order to model the clock accurately, this paper proposes to use the extended state Kalman filter to handle uncertain dynamics and unknown measurement bias to improve the estimation accuracy of clock skew further. The simulation results show that the proposed algorithm can achieve high- precision clock synchronization while reducing the consumption of network resources. © 2023 IEEE.

关键词： Internet of things

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Event-Triggered Cooperative Tracking control of Multiagent systems with a Dynamic Leader via Approximate Dynamic Programming

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2752-2765页

作者： Fu, Hao He, Haodong Chen, Yang Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System The School of Computer Science and Technology Wuhan430081 China Wuhan University of Science and Technology Eng. Res. Center for Metallurgical Automation and Measurement Technology of Ministry of Education Wuhan430081 China

This article investigates the optimal cooperative tracking control problem for unknown nonlinear multiagent systems with a real dynamic leader and uncertainties. Its main difficulty lies in eliminating effect of the unmatched uncertainty and noncooperation in the event-triggered manner. To overcome this difficulty, a distributed robust event-triggered control algorithm is developed with the aid of a disturbance observer and robust approximate dynamic programming (ADP). Specifically, this problem is converted into a cooperative multiplayer game for the distributed auxiliary systems employing observer-based feedforward compensation, which contributes to reducing conservatism;via an adaptive event-triggered mechanism and a new weight tuning law, a distributed robust ADP approach is proposed with hope to obviate judging the existence of the saddle point beforehand. Moreover, the local neighbor tracking error and the weight estimation error are demonstrated to be uniformly ultimately bounded by means of Lyapunov stability theory. We also prove that Zeno behavior is excluded. A comparative simulation example for multiquadrotor systems is carried out to verify the effectiveness and superiority of our algorithm. © 2020 IEEE.

关键词： Dynamic programming

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Advancing Few-Shot Instance Segmentation of Military Aircraft in Remote Sensing Imagery with Large Vision Models

Advancing Few-Shot Instance Segmentation of Military Aircraf...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Xiyu Qi Yifan Wu Wenhui Zhang Zhan Chen Yidan Zhang Lei Wang Aerospace Information Research Institute Chinese Academy of Sciences Key Laboratory of Target Cognition and Application Technology (TCAT) Key Laboratory of Network Information System Technology (NIST) Key Laboratory of Technology in Geo-Spatial Information Processing and Application System

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

This study introduces a novel few-shot instance segmentation framework MAR FSIS for identifying and segmenting aircraft in high-resolution remote sensing imagery under stringent few-shot conditions. Utilizing large vision models in conjunction with a sophisticated detector and a novel object prompter, MAR FSIS demonstrates superior performance in discerning intricate details of aircraft with minimal training examples. The effectiveness of this approach is confirmed through experimental evaluations on the proposed MAR20IS dataset, achieving remarkable precision in both detection and segmentation tasks.

关键词： Instance segmentation Training Atmospheric modeling Detectors Military aircraft Data models Proposals

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