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Deep learning radiomics of ultrasonography for comprehensively predicting tumor and axillary lymph node status after neoadjuvant chemotherapy in breast cancer patients: A multicenter study

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CANCER 2023年第3期129卷 356-366页

作者： Gu, Jionghui Tong, Tong Xu, Dong Cheng, Fang Fang, Chengyu He, Chang Wang, Jing Wang, Baohua Yang, Xin Wang, Kun Tian, Jie Jiang, Tian'an Zhejiang Univ Affiliated Hosp 1 Coll Med Dept Ultrasound Hangzhou Peoples R China Chinese Acad Sci Inst Automat CAS Key Lab Mol Imaging State Key Lab Management & Control Complex Syst Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Univ Chinese Acad Sci Zhejiang Canc Hosp Dept Ultrasound Canc Hosp Hangzhou Peoples R China Beihang Univ Beijing Adv Innovat Ctr Big Data Based Precis Med Sch Med & Engn Beijing Peoples R China Zhejiang Prov Key Lab Pulsed Elect Field Technol M Hangzhou Peoples R China Zhejiang Univ Affiliated Hosp 1 Coll Med Dept Ultrasound Hangzhou 310003 Peoples R China Chinese Acad Sci Inst Automat CAS Key Lab Mol Imaging State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Background: Neoadjuvant chemotherapy (NAC) can downstage tumors and axillary lymph nodes in breast cancer (BC) patients. However, tumors and axillary response to NAC are not parallel and vary among patients. This study aims to explore the feasibility of deep learning radiomics nomogram (DLRN) for independently predicting the status of tumors and lymph node metastasis (LNM) after NAC. Methods: In total, 484 BC patients who completed NAC from two hospitals (H1: 297 patients in the training cohort and 99 patients in the validation cohort;H2: 88 patients in the test cohort) were retrospectively enrolled. The authors developed two deep learning radiomics (DLR) models for personalized prediction of the tumor pathologic complete response (PCR) to NAC (DLR-PCR) and the LNM status (DLR-LNM) after NAC based on pre-NAC and after-NAC ultrasonography images. Furthermore, they proposed two DLRNs (DLRN-PCR and DLRN-LNM) for two different tasks based on the clinical characteristics and DLR scores, which were generated from both DLR-PCR and DLR-LNM. Results: In the validation and test cohorts, DLRN-PCR exhibited areas under the receiver operating characteristic curves (AUCs) of 0.903 and 0.896 with sensitivities of 91.2% and 75.0%, respectively. DLRN-LNM achieved AUCs of 0.853 and 0.863, specificities of 82.0% and 81.8%, and negative predictive values of 81.3% and 87.2% in the validation and test cohorts, respectively. The two DLRN models achieved satisfactory predictive performance based on different BC subtypes. Conclusions: The proposed DLRN models have the potential to accurately predict the tumor PCR and LNM status after NAC. In this study, we proposed two deep learning radiomics nomogram models based on pre-neoadjuvant chemotherapy (NAC) and preoperative ultrasonography images for independently predicting the status of tumor and axillary lymph node (ALN) after NAC. A more comprehensive assessment of the patient's condition after NAC can be achieved by predicting the status of

关键词： breast cancer deep learning lymph node metastasis neoadjuvant chemotherapy pathologic complete response treatment decision ultrasonography

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Neural-Dynamics-Based Active Steering control for Autonomous Vehicles with Noises 35

Neural-Dynamics-Based Active Steering Control for Autonomous...

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IEEE Intelligent Vehicles Symposium (IV)

作者： Ying Liufu Jin, Long Wang, Fei-Yue Lanzhou Univ Sch Informat Sci & Engn Lanzhou 730000 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100098 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

ISBN: (纸本)9798350348811;9798350348828

This paper introduces a neural-dynamics-based active steering control (NDASC) scheme developed under artificial systems, computational experiments, and parallel execution (ACP) framework, aimed at enhancing the stability and reliability of autonomous vehicles in noisy environments. Based on the Taylor expansion theorem, noises can be represented in the form of polynomials for the desired accuracy, and therefore polynomial noises can be viewed as a more generalized representation of noises. Then, the proposed NDASC scheme includes a model predictive active steering control (MPASC) strategy solved by a polynomial noise resilience neural dynamics (PNRND) model. Computational experiments parallelly implemented upon the CarSim-Simulink platform substantiate the effectiveness and robustness of the proposed NDASC scheme, providing significant theoretical and practical insights for control strategies of autonomous vehicles under various noisy environments.

关键词： Autonomous vehicles polynomial noise resilience neural dynamics (PNRND) model predictive active steering control (MPASC) noises

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Continuous-Time Linear Parallel Output Regulation

Continuous-Time Linear Parallel Output Regulation

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2021 China Automation Congress, CAC 2021

作者： Li, Hongyang Wei, Qinglai Wang, Fei-Yue The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665426473

A novel parallel output regulation method is presented in this paper. The framework of the parallel output regulation problem is first introduced. Then, the existence of parallel controller is analyzed detailly in the situation that only the measurable information is obtained. Finally, the simulation example is given to show the performance of the presented method. © 2021 IEEE

关键词： Continuous time systems

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System Dynamics-Based Analysis on Factors Influencing Artificial Intelligence Talents Training

IEEE JOURNAL OF RADIO FREQUENCY IDENTIFICATION

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IEEE JOURNAL OF RADIO FREQUENCY IDENTIFICATION 2022年 6卷 753-757页

作者： Jing, Sifeng Liu, Xiwei Gong, Xiaoyan Zhao, Hongxia Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Qingdao Acad Intelligent Ind Inst Smart Educ Syst Qingdao 266044 Peoples R China

There are gaps between education supply and industrial demand of talents in intelligent era. Thus, the shortage of artificial intelligence(AI) talents becomes the top challenges facing China's companies. Hence it is essential to implement educational reform to improve quality of education supply talents. However, educational reform is affected by multiple and intertwined factors. This research, therefore, developed a system dynamics-based model of education reform to sort out relationships among all factors and predict the influence of several factors on AI talents output. Then, based on this model, series of experiments are carried out to find main factors affecting cultivation of AI talents. Results show that: 1)this model could accurately simulate trends of output ratio AI talents under different educational reform measures;2) Measures including teaching mode reform, improving the application rate of new teaching model, speeding up the construction of new disciplines in higher education, developing AI, improving ethics of AI, and strengthening the integration of AI and education could play significant roles in promoting cultivation of AI talents.

关键词： System dynamics education reform simulation artificial intelligent talents cultivation

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Exploring painting synthesis with diffusion models 1

Exploring painting synthesis with diffusion models

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1st IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2021

作者： Yi, Da Guo, Chao Bai, Tianxiang Institute of Automation Chinese Academy of Sciences The State Key Laboratory for Management and Control of Complex Systems Beijing China

ISBN: (纸本)9781665433372

As a significant composition of art, fine art painting is becoming a research hotspot in machine learning community. With unique aesthetic value, paintings have quite different representations from natural images, making them irreplaceable. Meanwhile, the lack of training data is common in painting-related machine learning tasks. Therefore, the synthesis of fine art painting is meaningful and challenging work. There are two main types of generative models for image synthesis: generative adversarial networks (GANs) and likelihood-based models. GAN-based models can obtain high-quality samples but usually sacrifice diversity and training stability. Diffusion models are a class of likelihood-based models and have recently been shown to achieve state-of-the-art quality on the image synthesis tasks. In this paper, we explore generating fine art paintings by using diffusion models. We carried out the experiments on the partial impression paintings from the Wikiart dataset. The results demonstrate that the diffusion model can generate high-quality samples, and it is easy to train to cover more target distribution than the GAN-based methods. © 2021 IEEE.

关键词： Generative adversarial networks

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FrameNet: Tabular Image Preprocessing Based on UNet and Adaptive Correction 21st

FrameNet: Tabular Image Preprocessing Based on UNet and Adap...

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21st International Conference on Image Analysis and Processing (ICIAP)

作者： Wang, Yufei Du, Chen Xiao, Baihua Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9783031064302;9783031064296

Detecting and recognizing objects in images with complex backgrounds and deformations is a challenging task. In this work, we propose FrameNet, while a deep table lines segmentation network based on our Res18UNet with an adaptive deformation correction algorithm for correcting the table lines. We use Itinerary/Receipt of E-ticket for Air Transport to evaluate our methods. The experiment results show that our Res18UNet can reduce the number of parameters and improve the speed of image segmentation without significantly reducing the segmentation accuracy, and our correction method can better correct the perspective deformation and some distorted tabular images with no dependence on pixel-level ground truth image. In addition, we also apply our model and method to VAT invoice dataset and prove that they also have better transfer ability.

关键词： Computer vision Deep learning Image rectification

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Interpretable Learning for Travel Behaviours in Cyber-Physical-Social-systems

Interpretable Learning for Travel Behaviours in Cyber-Physic...

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2022 Australian and New Zealand control Conference

作者： Qi, Hao Ye, Peijun Shandong Jiaotong Univ Jinan 250357 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

ISBN: (数字)9781665498876

ISBN: (纸本)9781665498876

Interpretable learning is important for understanding human behavioral patterns in Cyber-Physical-Social-systems (CPSS). It facilitates smart decision-makings of intelligent algorithms so that the management of such human-machine hybrid systems can be efficient and optimal. Unlike the big data driven transportation management, this paper introduces a new interpretable learning method using fuzzy logic to semantically extract travel behaviors. Computational experiments based on actual traffic data indicate that our method is able to generate explicit rules, and these rules can be used to predict traffic patterns very well.

关键词： Deep Learning Interpretability Cyber-Physical-Social-System Travel Behavior

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Event-Triggered Energy Optimization of Wireless Sensor Networks 19

Event-Triggered Energy Optimization of Wireless Sensor Netwo...

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19th IEEE International Conference on Networking, Sensing and control, ICNSC 2022

作者： Liu, Lu Song, Ruizhuo Wei, Qinglai School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Institute of Automation Chinese Academy of Sciences The State Key Laboratory of Management and Control for Complex Systems Beijing China

ISBN: (纸本)9781665472432

Aiming at limited communication and energy re-sources in wireless sensor networks (WSN s), this paper proposes an energy management scheme of WSNs via adaptive dynamic programming (ADP) based on event-triggered mecha-nism (ETM). The optimal control strategy obtained by iteration can schedule the sensor nodes and make the nodes switch between working and sleeping situations, thus improving the energy utilization and extending the service life of the energy-constrained WSNs. Firstly, the mathematical model of WSNs is established, and the state is estimated by extended Kalman filter (EKF) algorithm to improve the measurement accuracy. Then, ADP solves the designed value function to achieve the scheduling plan. On the premise of system stability, ETM is applied to activate the controller on demand, which can reduce communication burden and save WSNs energy consumption. Finally, the simulation experiment reveals that the proposed algorithm can reduce the unnecessary triggering times of the controller effectively while ensuring the requirements, and avoid data congestion and interaction resource waste. © 2022 IEEE.

关键词： Energy utilization

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LMI-based robust adaptive neural network control for Euler-Bernoulli beam with uncertain parameters and disturbances

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INTERNATIONAL JOURNAL OF control 2022年第1期95卷 1-10页

作者： Xing, Xueyan Yang, Hongjun Liu, Jinkun Wang, Shuquan Beihang Univ Sch Automat Sci & Elect Engn Beijing Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Technol & Engn Ctr Space Utilizat Beijing Peoples R China

This paper is concerned with the stabilisation problem of an Euler-Bernoulli beam with uncertain parameters and disturbances. To correctly represent the beam's behaviour, the partial differential equations model is utilised for the control design of the beam without missing any high-order mode information. Then the linear matrix inequalities (LMIs) method is applied to the robust adaptive neural network control design to cope with systematic uncertainties and stabilise the beam system with disturbance compensation. Through resolving LMIs, feasible sets of designed control parameters can be effectively obtained without model linearisation. Finally, numerical simulations are done to validate the effectiveness of the proposed control.

关键词： Distributed parameter systems boundary control LMIs robust adaptive neural network control

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An FM*-Based Comprehensive Path Planning System for Robotic Floating Garbage Cleaning

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION systems 2022年第12期23卷 23821-23830页

作者： Kong, Shihan Wu, Zhengxing Qiu, Changlin Tian, Manjun Yu, Junzhi Peking Univ Coll Engn Dept Adv Mfg & Robot BIC ESATState Key Lab Turbulence & Complex Syst Beijing 100871 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Minist Publ Secur PRC Res Inst 1 Beijing 100048 Peoples R China

A heuristic fast marching (FM*)-based comprehensive path planning system involving task allocation, initial planning, and replanning is presented for the robotic floating garbage cleaning mission. There are three primary contributions in this paper. First, to tackle the invalidation of the Euclidean distance metric in the obstacle environment, the task allocation is modeled as a travelling salesman problem (TSP) employing the FM*-based distance metric in order to obtain an optimal travel sequence. Second, to meet the maneuverability constraint from the surface robot and avoid the collision, a Gaussian filter is employed to adjust the curvature radius of the generated path. Third, for an efficient replanning, a neural network-based replanning point generator with the input of garbage movement vector is provided to strike a compromise for the distance cost and the computational burden. Moreover, a case study and a virtual obstacle experiment in the laboratory water tank demonstrate the feasibility of the proposed comprehensive path planning system. This work lays a firm foundation for the development of intelligent equipment for aquatic environment protection.

关键词： Task analysis Resource management Cleaning Path planning Robots Measurement Planning Autonomous vehicles path planning fast marching method task allocation replanning

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