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An attention-guided network for surgical instrument segmentation from endoscopic images

COMPUTERS IN BIOLOGY AND MEDICINE 2022年第Pt A期151卷 106216-106216页

作者： Yang, Lei Gu, Yuge Bian, Guibin Liu, Yanhong Zhengzhou Univ Sch Elect & Informat Engn Zhengzhou 450001 Henan Peoples R China Robot Percept & Control Engn Lab Zhengzhou 450001 Henan Peoples R China Chinese Acad Sci Inst Automation State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Accurate surgical instrument segmentation can provide the precise location and pose information to the surgeons, assisting the surgeon to accurately judge the follow-up operation during the robot-assisted surgery procedures. Due to strong context extraction ability, there have been significant advances in research of automatic surgical instrument segmentation, especially U-Net and its variant networks. However, there are still some problems to affect segmentation accuracy, like insufficient processing of local features, class imbalance issue, etc. To deal with these problems, with the typical encoder-decoder structure, an effective surgical instrument segmentation network is proposed for providing an end-to-end detection scheme. Specifically, aimed at the problem of insufficient processing of local features, the residual path is introduced for the full feature extraction to strengthen the backward propagation of low-level features. Further, to achieve feature enhancement of local feature maps, a non-local attention block is introduced to insert into the bottleneck layer to acquire global contexts. Besides, to highlight the pixel areas of the surgical instruments, a dual -attention module (DAM) is introduced to make full use of the high-level features extracted from decoder unit and the low-level features delivered by the encoder unit to acquire the attention features and suppress the irrelevant features. To prove the effectiveness and superiority of the proposed segmentation model, experiments are conducted on two public surgical instrument segmentation data sets, including Kvasir-instrument set and Endovis2017 set, which could acquire a 95.77% Dice score and 92.13% mIOU value on Kvasir-instrument set, and simultaneously reach 95.60% Dice score and 92.74% mIOU value on Endovis2017 set respectively. Experimental results show that the proposed segmentation model realizes a superior performance on surgical instruments in comparison to other advanced models, which could

关键词： Surgical instruments Deep learning Semantic segmentation Residual network Dual attention mechanism

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Locomotion control of a hybrid propulsion biomimetic underwater vehicle via deep reinforcement learning

Locomotion control of a hybrid propulsion biomimetic underwa...

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2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021

作者： Zhang, Tiandong Wang, Rui Wang, Yu Wang, Shuo Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai200031 China

ISBN: (纸本)9781665436786

This paper presents a novel deep reinforcement learning (DRL) method to solve the locomotion control problem of the biomimetic underwater vehicle (BUV) with hybrid propulsion, in order to meet the challenge of intractable multi-fins coordination and the complex hydrodynamic model. The system overview of the BUV, named RoboDact, with two flexible long fins and a double-joint fishtail as hybrid propulsion, is introduced. After that, the locomotion control problem is modeled as a Markov decision process (MDP) to be solved. Therefore, the locomotion control method based on soft actor-critic (SAC, a novel DRL algorithm) is proposed. The simulation environment is established based on the kinetic model for interaction. Finally, the feasibility and effectiveness of the proposed control method is demonstrated after extensive simulations. It will provide rich insights into the coordination control of biomimetic underwater vehicles. © 2021 IEEE.

关键词： Reinforcement learning

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Probabilistic Graph Matching with Multiplicative Updating Algorithm for Correspondence between Remote Sensing Images

Probabilistic Graph Matching with Multiplicative Updating Al...

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Information Communication and Software Engineering (ICICSE), IEEE International Conference on

作者： Jing Yang Xu Yang Zhang-Bing Zhou Zhi-Yong Liu School of Information Engineering China University of Geosciences(Beijing) State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Information Engineering China University of Geosciences(Beijing) TELECOM SudParis Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology Beijing China

ISBN: (纸本)9781665430302

Point correspondence is an important problem in remote sensing images matching task which lays the foundation for the image registration task. Due to the existence of the rotation transformation and the large scale, and the change of scenery between the two obtained images, the point correspondence problem is still challenging. Tradition algorithms such as the SIFT and SURF aim at using the point appearance to find the point correspondence. However, when the above situations happens, the appearance based algorithms will fail to solve the problem. In this paper, the structural information between points is introduced to the correspondence problem and a spectral graph matching algorithm is proposed based on the probabilistic scheme and the multiplicative updating scheme. The proposed algorithm has three advantages compared with the traditional algorithms. First, incorporating the structural cues to the matching problem makes it more robust to the large scale and the geometric transformation. Second, the best assignment between points which is interpreted by a probabilistic manner is figured out by ranking the probabilities. Third, the optimization problem is solved by an approximate spectral method efficiently and a multiplicative updating algorithm is introduced to ensure the final discrete solution. Experiments on the real remote sensing images validate the effectiveness of the proposed algorithm.

关键词： Image registration Software algorithms Approximation algorithms Probabilistic logic Sensors Internet Task analysis

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Image-based traffic signal control via world models

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Frontiers of Information Technology & Electronic Engineering 2022年第12期23卷 1795-1813页

作者： Xingyuan DAI Chen ZHAO Xiao WANG Yisheng LV Yilun LIN Fei-Yue WANG The State Key Laboratory for Management and Control of Complea Systems Institute of AutomationChineseAcademyof SciencesBeijing100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China School of Artificial Intelligence Anhui UniversityHefei 230039China Shanghai AI Laboratory Shanghai 200232China

Traffic signal control is shifting from passive control to proactive control, which enables the controller to direct current traffic flow to reach its expected destinations. To this end, an effective prediction model is needed for signal controllers. What to predict, how to predict, and how to leverage the prediction for control policy optimization are critical problems for proactive traffic signal control. In this paper, we use an image that contains vehicle positions to describe intersection traffic states. Then, inspired by a model-based reinforcement learning method, DreamerV2,we introduce a novel learning-based traffic world model. The traffic world model that describes traffic dynamics in image form is used as an abstract alternative to the traffic environment to generate multi-step planning data for control policy optimization. In the execution phase, the optimized traffic controller directly outputs actions in real time based on abstract representations of traffic states, and the world model can also predict the impact of different control behaviors on future traffic conditions. Experimental results indicate that the traffic world model enables the optimized real-time control policy to outperform common baselines, and the model achieves accurate image-based prediction, showing promising applications in futuristic traffic signal control.

关键词： Traffic signal control Traffic prediction Traffic world model Reinforcement learning

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Optical Imaging of Epigenetic Modifications in Cancer:A Systematic Review

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Phenomics 2022年第2期2卷 88-101页

作者： Yang Du Pei Zhang Wei Liu Jie Tian CAS Key Laboratory of Molecular Imaging Beijing Key Laboratory of Molecular Imagingthe State Key Laboratory of Management and Control for Complex SystemsInstitute of AutomationChinese Academy of SciencesBeijing 100190China The University of Chinese Academy of Sciences Beijing 100049China Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education/Beijing) Department of Supportive Care Center and Day Oncology UnitPeking University Cancer Hospital and InstituteBeijing 100142China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of MedicineBeihang UniversityBeijing 100191China School of Life Science and Technology Xidian UniversityXi’an 710071ShaanxiChina

Increasing evidence has demonstrated that abnormal epigenetic modifications are strongly related to cancer ***,sensitive and specific detection of epigenetic modifications could markedly improve biological investigations and cancer precision medicine.A rapid development of molecular imaging approaches for the diagnosis and prognosis of cancer has been observed during the past few *** biomarkers unique to epigenetic modifications and targeted imaging probes have been characterized and used to discriminate cancer from healthy tissues,as well as evaluate therapeutic *** this study,we summarize the latest studies associated with optical molecular imaging of epigenetic modification targets,such as those involving DNA methylation,histone modification,noncoding RNA regulation,and chromosome remodeling,and further review their clinical application on cancer diagnosis and ***,we further propose the future direc-tions for precision imaging of epigenetic modification in *** by promising clinical and preclinical studies associated with optical molecular imaging technology and epigenetic drugs,the central role of epigenetics in cancer should be increasingly recognized and accepted.

关键词： Epigenetic modification Optical imaging Cancer Imaging probe

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Empirical policy optimization for n-player Markov games

arXiv

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arXiv 2021年

作者： Zhu, Yuanheng Zhao, Dongbin Zhao, Mengchen Li, Dong State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Huawei Noah's Ark Lab Beijing China

In single-agent Markov decision processes, an agent can optimize its policy based on the interaction with environment. In multi-player Markov games (MGs), however, the interaction is non-stationary due to the behaviors of other players, so the agent has no fixed optimization objective. In this paper, we treat the evolution of player policies as a dynamical process and propose a novel learning scheme for Nash equilibrium. The core is to evolve one's policy according to not just its current in-game performance, but an aggregation of its performance over history. We show that for a variety of MGs, players in our learning scheme will provably converge to a point that is an approximation to Nash equilibrium. Combined with neural networks, we develop the empirical policy optimization algorithm, that is implemented in a reinforcement-learning framework and runs in a distributed way, with each player optimizing its policy based on own observations. We use two numerical examples to validate the convergence property on small-scale MGs with n ≥ 2 players, and a pong example to show the potential of our algorithm on large games. © 2021, CC BY-NC-ND.

关键词： Computation theory

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A Novel Coiled Cable-Conduit-Driven Hyper-Redundant Manipulator for Remote Operating in Narrow Spaces

A Novel Coiled Cable-Conduit-Driven Hyper-Redundant Manipula...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Mingrui Luo Yunong Tian En Li Minghao Chen Cunfeng Kang Guodong Yang Min Tan School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Engineering Laboratory of Industrial Vision and Intelligent Equipment Technology Chinese Academy of Sciences Beijing China Beijing-University of Technology Beijing China

Operating in narrow spaces is an important challenge in the development of robots. Redundant manipulators are one way to solve this problem, but their mechanism design and control method still have much room for improvement. In this paper, we propose a coiled cable-conduit-driven hyper-redundant manipulator (C-CDHRM) with great slenderness and flexibility. In terms of mechanism design, it considers both compactness and operability. By imitating the structure and behavior of a constricting snake, it can be uncoiled sequentially from a coiled storage state, led by the head. In terms of control methods, we propose a multi-layer control system that can make remote operations more accurate and reliable. On the one hand, guiding, segmenting, and following the path overcome the planning ambiguity caused by redundancy. On the other hand, conduit transmission modeling and cable length correction overcome the nonlinear mapping of cable-driven joints and were verified in experiments. Through tests, the mobile integrated system composed of C-CDHRM has an excellent performance in operation precision and accuracy, ensuring safety and accessibility in narrow spaces. Finally, in field experiments, the inspection and cleaning of various types of electrical equipment have been successfully completed, showing excellent application prospects.

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MULTI-VIEWPOINT AND MULTI-EVALUATION WITH FELICITOUS INDUCTIVE BIAS BOOST MACHINE ABSTRACT REASONING ABILITY

arXiv

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arXiv 2022年

作者： Wei, Qinglai Chen, Diancheng Yuan, Beiming State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

Great endeavors have been made to study AI's ability in abstract reasoning, along with which different versions of RAVEN's progressive matrices (RPM) are proposed as benchmarks. Previous works give inkling that without sophisticated design or extra meta-data containing semantic information, neural networks may still be indecisive in making decisions regarding the RPM problems, after relentless training. Evidenced by thorough experiments, we show that, neural networks embodied with felicitous inductive bias, intentionally design or serendipitously match, can solve the RPM problems efficiently, without the augment of any extra meta-data. Our work also reveals that multiviewpoint with multi-evaluation is a key learning strategy for successful reasoning. Nevertheless, we also point out the unique role of meta-data by showing that a pre-training model supervised by the meta-data leads to a RPM solver with better performance. Source code can be found in https://***/QinglaiWeiCASIA/RavenSolver. Copyright © 2022, The Authors. All rights reserved.

关键词： Matrix algebra

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Parallel Reinforcement Learning-Based Energy Efficiency Improvement for a Cyber-Physical System

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IEEE/CAA Journal of Automatica Sinica 2020年第2期7卷 617-626页

作者： Teng Liu Bin Tian Yunfeng Ai Fei-Yue Wang Department of Automotive Engineering Chongqing UniversityChongqing 400044 the Vehicle Intelligence Pioneers Inc. Qingdao 266109China the State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China the School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 101408China

As a complex and critical cyber-physical system(CPS),the hybrid electric powertrain is significant to mitigate air pollution and improve fuel *** management strategy(EMS)is playing a key role to improve the energy efficiency of this *** paper presents a novel bidirectional long shortterm memory(LSTM)network based parallel reinforcement learning(PRL)approach to construct EMS for a hybrid tracked vehicle(HTV).This method contains two *** high-level establishes a parallel system first,which includes a real powertrain system and an artificial ***,the synthesized data from this parallel system is trained by a bidirectional LSTM *** lower-level determines the optimal EMS using the trained action state function in the model-free reinforcement learning(RL)*** is a fully data-driven and learning-enabled approach that does not depend on any prediction and predefined ***,real vehicle testing is implemented and relevant experiment data is collected and *** results validate that the proposed EMS can achieve considerable energy efficiency improvement by comparing with the conventional RL approach and deep RL.

关键词： Bidirectional long short-term memory(LSTM)network cyber-physical system(CPS) energy management parallel system reinforcement learning(RL)

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Restoring After Central Nervous System Injuries:Neural Mechanisms and Translational Applications of Motor Recovery

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Neuroscience Bulletin 2022年第12期38卷 1569-1587页

作者： Zhengrun Gao Zhen Pang Yiming Chen Gaowei Lei Shuai Zhu Guotao Li Yundong Shen Wendong Xu Department of Hand Surgery Huashan HospitalFudan UniversityShanghai 200040China The National Clinical Research Center for Aging and Medicine Fudan UniversityShanghai 200040China Department of Hand and Upper Extremity Surgery Jing’an District Central HospitalFudan UniversityShanghai 200040China Institutes of Brain Science Fudan UniversityShanghai 200040China State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science Fudan UniversityShanghai 200040China Institute of Automation State Key Laboratory of Management and Control for Complex SystemsChinese Academy of SciencesBeijing 100190China Co-innovation Center of Neuroregeneration Nantong UniversityNantong 226000China Research Unit of Synergistic Reconstruction of Upper and Lower Limbs after Brain Injury Chinese Academy of Medical SciencesShanghai 200400China

Central nervous system(CNS)injuries,including stroke,traumatic brain injury,and spinal cord injury,are leading causes of long-term *** is estimated that more than half of the survivors of severe unilateral injury are unable to use the denervated *** studies have focused on neuroprotective interventions in the affected hemisphere to limit brain lesions and neurorepair measures to promote ***,the ability to increase plasticity in the injured brain is restricted and difficult to ***,over several decades,researchers have been prompted to enhance the compensation by the unaffected *** experiments have revealed that regrowth of ipsilateral descending fibers from the unaffected hemisphere to denervated motor neurons plays a significant role in the restoration of motor *** addition,several clinical treatments have been designed to restore ipsilateral motor control,including brain stimulation,nerve transfer surgery,and brain–computer interface ***,we comprehensively review the neural mechanisms as well as translational applications of ipsilateral motor control upon rehabilitation after CNS injuries.

关键词： Stroke Traumatic brain injury Spinal cord injury Brain-computer interface system Neuroplasticity Ipsilateral motor control Axon regrowth

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