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Few-shot learning based histopathological image classification of colorectal cancer

Intelligent Medicine 2024年第4期4卷 256-267页

作者： Rui Li Xiaoyan Li Hongzan Sun Jinzhu Yang Md Rahaman Marcin Grzegozek Tao Jiang Xinyu Huang Chen Li Key Laboratory of Intelligent Computing in Medical Image Ministry of EducationNortheastern UniversityShenyangLiaoning 110167China Cancer Hospital China Medical UniversityShenyangLiaoning 110122China Shengjing Hospital China Medical UniversityShenyang.Liaoning 110000China Institute for Medical Informatics University of LuebeckGermany Department of Knowledge Engineering University of Economics in KatowicePoland School of Intelligent Medicine Chengdu University of Traditional Chinese MedicineChengduSichuan 610075China International Joint Institute of Robotics and Intelligent Systems Chengdu University of Information TechnologyChengduSichuan 610225China School of Computer Science and Engineering University of New South WalesSydneyNSW 2052Australia

Background Colorectal cancer is a prevalent and deadly disease worldwide,posing significant diagnostic *** histopathologic image classification is often inefficient and *** some histopathologists use computer-aided diagnosis to improve efficiency,these methods depend heavily on exten-sive data and specific annotations,limiting their *** address these challenges,this paper proposes a method based on few-shot *** This study introduced a few-shot learning approach that combines transfer learning and contrastive learning to classify colorectal cancer histopathology images into benign and malignant *** model comprises modules for feature extraction,dimensionality reduction,and classification,trained using a combi-nation of contrast loss and cross-entropy *** this paper,we detailed the setup of hyperparameters:n-way,κ-shot,β,and the creation of support,query,and test *** Our method achieved over 98% accuracy on a query dataset with 35 samples per category using only 10 training samples per *** documented the model’s loss,accuracy,and the confusion matrix of the ***,we employed the t-SNE algorithm to analyze and assess the model’s classification *** The proposed model may demonstrate significant advantages in accuracy and minimal data depen-dency,performing robustly across all tested n-way,κ-shot *** consistently achieved over 93% accuracy on comprehensive test datasets,including 1916 samples,confirming its high classification accuracy and strong generalization *** research could advance the use of few-shot learning in medical diagnostics and also lays the groundwork for extending it to deal with rare,difficult-to-diagnose cases.

关键词： Colorectal cancer Few-shot learning Transfer learning Contrastive learning Histopathological images Benign and malignant categories

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Accurate Parameter Estimation of Time-varying Nuclear Radiation Field with Mobile Sensors

Accurate Parameter Estimation of Time-varying Nuclear Radiat...

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第40届中国控制会议

作者： Bo Chen Zhicheng Li Zaiyue Yang Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Department of Mechanical and Energy Engineering and the Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Academy for Advanced Interdisciplinary Studies Southern University of Science and Technology

In this paper, the optimization strategy of mobile sensors and the parameter estimation of time-varying nuclear radiation field are investigated. Based on the measurement model with white Gaussian noise, Fisher information matrix is derived as an information metric. At each time step, finding the best mobile sensor coordinates that contain the amount of maximal information in the 4-Dimension space is formulated as a constrained nonlinear optimization problem, which is able to minimize the trace of Cramer-Rao Lower Bound. Further, an efficient heuristic algorithm called Augmented Lagrangian Genetic Algorithm is adopted to solve the above optimization problem. Then, the parameters of nuclear radiation field are estimated by a maximum likelihood estimator. The proposed sensor coordinates moving strategy has better estimation performance than random coordinates strategy, which is demonstrated via simulation results.

关键词： Optimal sensor coordinates Fisher information matrix(FIM) Cramer-Rao Lower Bound(CRLB) Augmented Lagrangian Genetic Algorithm(ALGA) Maximum likelihood estimate(MLE)

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Single image dehazing via combining the prior knowledge and CNNs

Single image dehazing via combining the prior knowledge and ...

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第33届中国控制与决策会议

作者： Yuwen Li Chaobing Zheng Shiqian Wu Wangming Xu Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology The Institute of Robotics and Intelligent Systems School of Information Science and EngineeringWuhan University of Science and Technology

Aiming at the existing single image haze removal algorithms,which are based on prior knowledge and assumptions,subject to many limitations in practical applications,and could suffer from noise and halo *** end-to-end system is proposed in this paper to reduce defects by combining the prior knowledge and deep learning *** haze image is decomposed into the base layer and detail layers through a weighted guided image filter(WGIF) firstly,and the airlight is estimated from the base ***,the base layer image is passed to the efficient deep convolutional network for estimating the transmission *** restore object close to the camera completely without amplifying noise in sky or heavily hazy scene,an adaptive strategy is proposed based on the value of the transmission *** the transmission map of a pixel is small,the base layer of the haze image is used to recover a haze-free image via atmospheric scattering model,***,the haze image is *** show that the proposed method achieves superior performance over existing methods.

关键词： single image haze removal Weighted guided image filtering prior knowledge Convolutional Neural Networks

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Distributed Vehicle Platoon Control with Leader Selection Strategy

Distributed Vehicle Platoon Control with Leader Selection St...

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第40届中国控制会议

作者： Zikai Ouyang Zhicheng Li Shibo Chen Zaiyue Yang Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Department of Mechanical and Energy Engineering and the Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Academy for Advanced Interdisciplinary Studies Southern University of Science and Technology

This paper investigates the control problem of vehicle platoon with leader selection strategy. The current research on vehicle platoon control is based on the leader-follower communication structure. Under such structure, each follower tracks the state of its front vehicle to maintain a proper distance and form a platoon. However, the disadvantage of this fixed communication structure is that followers are short of capacity of resisting disturbance when the first vehicle(FV) of platoon is disturbed. To solve this problem, a vehicle platoon model where each follower is able to take any front vehicle as its leader is proposed. Based on this model, the corresponding controller and leader selection strategy are designed. That is, first, a controller considering the string stability of vehicle platoon with various communication topologies is designed;then, each follower changes its connecting vehicle in the communication link to reduce the impact of disturbance on FV when FV suddenly slows down or speeds up. The simulation results show the effectiveness of the control method and the strategy.

关键词： Vehicle platoon leader selection strategy distributed control

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Catch Planner: Catching High-Speed Targets in the Flight

arXiv

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arXiv 2023年

作者： Yu, Huan Wang, Pengqin Wang, Jin Ji, Jialin Zheng, Zhi Tu, Jie Lu, Guodong Meng, Jun Zhu, Meixin Shen, Shaojie Gao, Fei The State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou310027 China The Engineering Research Center for Design Engineering and Digital Twin of Zhejiang Province School of Mechanical Engineering Zhejiang University Hangzhou310027 China Robotics Institute of Zhejiang University Hangzhou310027 China Hong Kong The State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou310027 China Huzhou Institute of Zhejiang University Huzhou313000 China School of Electrical Engineering Zhejiang University Hangzhou310027 China The Hong Kong University of Science and Technology Guangzhou China

Catching high-speed targets in the flight is a complex and typical highly dynamic task. However, existing methods require manual setting of catching height or time, resulting in lacks of adaptability and flexibility and cannot deal with multiple targets. To bridge this gap, we propose a planning-with-decision scheme called Catch Planner. For sequential decision making, a lightweight policy search method based on deep reinforcement learning is proposed. It is jointly trained with the motion planning and decoupled from physics to speed up training. For motion planning, we propose a trajectory optimization method that jointly optimizes the highly coupled catching time and terminal state. The core is the flexible-terminal constraint transcription. It converts the three unique constraints of catching into differentiable metrics, including equality constraints for terminal position and time, and inequality constraints that enable reasonable terminal position offset and attitude relaxation. In addition, sparse parameterization based on MINCO class considers both dynamic feasibility and collision avoidance constraints. As a result, a generally constrained quadrotor planning problem is transformed into an unconstrained optimization that can be solved reliably and efficiently. We also propose an online iterative optimization method for predicting differentiable trajectories of targets. Catch Planner provides a new paradigm for the combination of learning and planning, where all algorithms can be run in real time onboard at 100hz. Extensive experiments are carried out in real-world and simulated scenes to verify the robustness and expansibility when facing a variety of high-speed flying targets. Copyright © 2023, The Authors. All rights reserved.

关键词： Decision making

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Optimal path planning using CAMIS: A Continuous Anisotropic Model for Inclined Surfaces

arXiv

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arXiv 2021年

作者： Sánchez-Ibáñez, J. Ricardo Pérez-Del-Pulgar, Carlos J. Serón, Javier García-Cerezo, Alfonso The Space Robotics Laboratory Department of Systems Engineering and Automation Universidad de Málaga C/Ortiz Ramos s/n Málaga29071 Spain

The optimal traverse of irregular terrains made by ground mobile robots heavily depends on the adequacy of the cost models used to plan the path they follow. The criteria to define optimality may be based on minimizing energy consumption and/or preserving the robot stability. This entails the proper assessment of anisotropy to account for the robot driving on top of slopes with different directions. To fulfill this demand, this paper presents the Continuous Anisotropic Model for Inclined Surfaces, a cost model compatible with anisotropic path planners like the bi-directional Ordered Upwind Method. This model acknowledges how the orientation of the robot with respect to any slope determines its energetic cost, considering the action of gravity and terramechanic effects such as the slippage. Moreover, the proposed model can be tuned to define a trade-off between energy minimization and Roll angle reduction. The results from two simulation tests demonstrate how, to find the optimal path in scenarios containing slopes, in certain situations the use of this model can be more advantageous than relying on isotropic cost functions. Finally, the outcome of a field experiment involving a skid-steering robot that drives on top of a real slope is also discussed. Copyright © 2021, The Authors. All rights reserved.

关键词： Anisotropy

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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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Special Cases in the Attitude Determination of Three-Vehicle Heterogeneous Formations

Special Cases in the Attitude Determination of Three-Vehicle...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Pedro Cruz Pedro Batista Laboratory for Robotics and Engineering Systems Institute for Systems and Robotics Portugal Instituto Superior Técnico Universidade de Lisboa Portugal

ISBN: (数字)9781728159539

ISBN: (纸本)9781728159546

This paper considers the attitude determination problem of a three-vehicle heterogeneous formation, whose line of sight is constrained. By some reason, such as large distance, two of the vehicles, the deputies, cannot measure the direction of each other's position. The deputies can only measure the relative direction relative to the chief, which is the remaining vehicle in the formation. The special cases of the problem are described in terms of the number of solutions related to each configuration. In general, there is only one solution, but infinite or even two ambiguous solutions are possible, respectively in the degenerate and ambiguous configurations. These arise due to lack of information or symmetry in the data, respectively. Furthermore, the subset of special configurations is a zero measure set of the global set of configurations, which is an important property of the problem. Simulations that test and validate the described configurations are shown as well, which also help to understand what effects the quality of the solution obtained according to a special case proximity metric.

关键词： Position measurement Robot sensing systems Semiconductor device measurement Space vehicles Rotation measurement Quaternions Navigation

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Magnetic-Actuated Flexible Instruments with Enhanced Bending Capability through Magnetic Distribution Optimization

Magnetic-Actuated Flexible Instruments with Enhanced Bending...

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IEEE International Conference on Automation Science and engineering (CASE)

作者： Yuanrui Huang Runyu Ma Jian Hu Mingcong Chen Jian Chen Hongbin Liu School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China School of General Engineering Beihang University Beijing China Centre of AI and Robotics (CAIR) Hong Kong Institute of Science & Innovation Chinese Academy of Sciences

Magnetically-actuated flexible surgical robot has promising applications for ultra-narrow and tortuous orifices. However, maximizing its bending capability and manipulability remains a significant hurdle for clinical use. In this study, we introduce an objective function that can accurately predict a catheter's bending angle with given numbers of internal permanent magnets (IPMs) under various magnetic fields through iterative calculation. The optimal magnets distribution that yields the maximal controllable bending angle, could be determined by optimizing this objective function. The effectiveness of this method is validated through simulations and experiments. Additionally, we propose a simple control scheme to control the bending angle of the magnetically-actuated catheter based on the piecewise constant curvature model. The experimental results demonstrate that the proposed method significantly improves the motion accuracy of the catheter. The catheter with the optimized magnet distribution achieved superior tracking performance with an Integral Absolute Error (IAE) of 1.08 rad·s and maximal controllable bending angle of 150°.

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Low Power and Temperature-Resilient Compute-In-Memory Based on Subthreshold-FeFET

arXiv

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arXiv 2023年

作者： Zhou, Yifei Huang, Xuchu Yang, Jianyi Ni, Kai Amrouch, Hussam Zhuo, Cheng Yin, Xunzhao Zhejiang University Hangzhou China Department of Electrical Engineering University of Notre Dame Notre Dame United States AI Processor Design Technical University of Munich TUM School of Computation Information and Technology Munich Institute of Robotics and Machine Intelligence Munich Germany Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province Hangzhou China

Compute-in-memory (CiM) is a promising solution for addressing the challenges of artificial intelligence (AI) and the Internet of Things (IoT) hardware such as"memory wall" issue. Specifically, CiM employing nonvolatile memory (NVM) devices in a crossbar structure can efficiently accelerate multiply-accumulation (MAC) computation, a crucial operator in neural networks among various AI models. Low power CiM designs are thus highly desired for further energy efficiency optimization on AI models. Ferroelectric FET (FeFET), an emerging device, is attractive for building ultra-low power CiM array due to CMOS compatibility, high ION/IOFF ratio, etc. Recent studies have explored FeFET based CiM designs that achieve low power consumption. Nevertheless, subthreshold-operated FeFETs, where the operating voltages are scaled down to the subthreshold region to reduce array power consumption, are particularly vulnerable to temperature drift, leading to accuracy degradation. To address this challenge, we propose a temperature-resilient 2T-1FeFET CiM design that performs MAC operations reliably at subthreahold region from 0°C to 85°C, while consuming ultra-low power. Benchmarked against the VGG neural network architecture running the CIFAR-10 dataset, the proposed 2T-1FeFET CiM design achieves 89.45% CIFAR-10 test accuracy. Compared to previous FeFET based CiM designs, it exhibits immunity to temperature drift at an 8-bit wordlength scale, and achieves better energy efficiency with 2866 TOPS/W. Copyright © 2023, The Authors. All rights reserved.

关键词： Energy efficiency

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