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Bone Marrow Cell Segmentation Based on Improved U-Net 1

6th International Conference on Data Mining and Big Data, DMBD 2021

作者： Jin, Lingmin Yu, Zhaochai Fan, Haoyi Teng, Shenghua Li, Zuoyong College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao266590 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China School of Information Engineering Zhengzhou University Zhengzhou450001 China

ISBN: (数字)9789811674761

ISBN: (纸本)9789811674754

Automatic segmentation of bone marrow cells plays an important role in the diagnosis of many blood diseases such as anemia and leukemia. Due to the complex morphology and wide variety of bone marrow cells, their segmentation is still a challenging task. To improve the accuracy of bone marrow cell segmentation, we propose an end-to-end U-shaped network based on the pyramid residual convolution and the attention mechanism. Specifically, the standard convolution and dilated convolution are combined as its feature encoder, which designs a pyramid residual convolution block to extract multi-scale features. Then, the attention gating mechanism is introduced into each skip connection module for fusing the shallow and deep information. Finally, the proposed method combines convolution and deconvolution for feature decoding to achieve the final segmentation of bone marrow cells. Experiments with quantitative and qualitative comparisons are carried out on a self-built bone marrow smear dataset. Experimental results demonstrate the superiority of the proposed method over several state-of-the-art methods. © 2021, Springer Nature Singapore Pte Ltd.

关键词： image segmentation

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Simulation-based Method for Determing Parameters of Material Price Adjustment Model in Large-scale Hydropower Development in China

Simulation-based Method for Determing Parameters of Material...

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2021智慧城市与智能建造高端论坛

作者： Lijing Yang Zhenyuan Liu Xi Chen Yaodi Fan Hualin Chen School of Artificial Intelligence and Automation Huazhong University of Science and Technology Key Laboratory of Education Ministry for Image Processing and Intelligent Control Yalong River Hydropower Development Company LTD

In the construction of large-scale hydropower projects in China,enormous amounts of materials are *** by various factors,fluctuation of materials price is very common during the *** protect the benefits of the demander and the supplier,reasonable material price adjustment methods need to be set in the procurement bidding documents.A price adjustment model is established considering price fluctuation stratifying and risk sharing.A simulation-based method is proposed for determing parameters in the model,and the simulation process is optimized with the optimal computing budget allocation *** on the judgment of engineers and real data of a hydropower project in Sichuan,China,and simulation cases are generated and the experimental results are analyzed with the *** results show that the simulation-based method provides scientific and reasonable support for determing parameters of material price adjustment model,and the optimal computing budget allocation algorithm plays an optimizing role.

关键词： Large-scale Hydropower Development Material Price Adjustment Parameters Determing Simulation Optimal Computing Budget Allocation TOPSIS

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SliceProp: A Slice-Wise Bidirectional Propagation Model for Interactive 3D Medical image Segmentation

SliceProp: A Slice-Wise Bidirectional Propagation Model for ...

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Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Xin Xu Wenjing Lu Jiahao Lei Peng Qiu Hong-Bin Shen Yang Yang Department of Computer Science and Engineering Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China Department of Vascular Surgery Shanghai Ninth People's Hospital Shanghai Jiao Tong University School of Medicine Institute of Image Processing and Pattern Recognition and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai China

Interactive medical image segmentation methods have become increasingly popular in recent years. These methods combine manual labeling and automatic segmentation, reducing the workload of annotation while maintaining high accuracy. However, most current interactive segmentation frameworks are limited to 2D image data, and are not suitable for 3D image data due to the large size and high complexity of 3D data, as well as the challenges posed by information asymmetry and sparse annotation. In this paper, we propose SliceProp, an interactive segmentation framework that implements slice-wise Label Bidirectional Propagation (LBP) for 3D medical image segmentation. SliceProp extends the interactive 2D image segmentation algorithm to 3D image segmentation, and can handle 3D data with large size and high complexity. Moreover, equipped with a Backtracking Feedback Check (BFC) module, SliceProp effectively addresses the issues of information asymmetry and spatial sparse annotation in 3D medical image segmentation. Additionally, we adopt an uncertainty-based criterion to pri-oritize the slices to be refined interactively, which enhances the efficiency of the interaction process by enabling the model to focus on the regions with the most unreliable predictions. SliceProp is evaluated on two datasets and achieves promising results compared to state-of-the-art methods.

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Vision-based Autonomous Landing of UAV on Dynamic Apron

Vision-based Autonomous Landing of UAV on Dynamic Apron

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IEEE International Conference on Unmanned Systems (ICUS)

作者： Pengyu Yue Jing Xin Zhijie Mao Yongchang Lu Youmin Zhang Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an P.R.China Department of Mechanical Industrial & Aerospace Engineering Concordia University Montreal QC Canada

Aiming at the landing problem of Unmanned Aerial Vehicle (UAV) without landmarks and global coordinates, this paper proposes an efficient vision-based UAV autonomous landing solution. Firstly, image information is obtained from the bottom visual sensor of the UAV, and the YOLOV5 object detection method is employed to determine the initial position of the dynamic apron. Subsequently, an improved dynamic apron tracking method based on Spatio-Temporal Transformer is designed. This method updates an initial template through target detection algorithm and utilizes multi-source perceptual data for confidence evaluation and dynamic template updates. It outputs the distribution estimation of the predicted bounding box corner coordinates, and achieves the high reliability continuous positional smoothing estimation and dynamic apron loss re-detection based on UAV vision. Finally, the UAV calculates the horizontal control quantity based on the horizontal relative position between the UAV and the apron obtained. The vertical control quantity is calculated based on the vertical relative position between the UAV and the apron. The vertical relative position is estimated using laser ranging and visual odometry techniques. Based on the results of horizontal tracking control and vertical regulation, velocity commands for the UAV in all three directions are generated, enabling the UAV to achieve autonomous landing on the dynamic apron. This paper achieves precise landing for dynamic apron in real-world environments. The experimental results demonstrate that this method avoids the limitations of labeled data and global information on autonomous landing of UAV. Even under GPS-denied conditions, the UAV can generate control commands based on visual data and achieve autonomous landing for randomly moving apron.

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Sliding Mode control for Quadrotor-Slung Load Transportation System with State Constraints*

Sliding Mode Control for Quadrotor-Slung Load Transportation...

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IEEE International Conference on Cyborg and Bionic Systems (CBS)

作者： Feng Ding Chong Sun Yong Ai Jian Huang School of Computer Science South-Central Minzu University Hubei Provincial Engineering Research Center for Intelligent Management of Manufacturing Enterprises Wuhan Hubei China Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and automation Huazhong University of Science and Technology Wuhan Hubei China

Quadrotor-slung load transportation is an important way to transport goods, but the swing of the load bring a great deal of challenges to the stability of the system. In this paper, a barrier Lyapunov function based sliding mode controller is proposed to limit the load swing, which also satisfies the speed limitation of the quadrotor. An inner-outer loop control strategy is proposed, where the outer one is the virtual position controller which guarantees the stability of the quadrotor while eliminating the load swing, and the inner one is the attitude controller which driver the actual force to the position controller to ensure the effectiveness of the system. Specifically, the dynamics of quadrotor-slung load transportation system is reconstructed as a cascade form. Then, a barrier Lyapunov function based sliding mode controller is designed to ensure the trajectory tracking errors uniformly bounded with state constraints valid. Finally, comparative simulation results illustrate the effectiveness of the proposed method.

关键词： Attitude control Trajectory tracking Simulation Force Dynamics Transportation Stability analysis

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A general end-to-end diagnosis framework for manufacturing systems

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National Science Review 2020年第2期7卷 418-429页

作者： Ye Yuan Guijun Ma Cheng Cheng Beitong Zhou Huan Zhao Hai-Tao Zhang Han Ding School of Artificial Intelligence and Automation MOE Key Lab of Intelligent Control and Image Processing Huazhong University of Science and Technology State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology School of Mechanical Science and Engineering Huazhong University of Science and Technology

The manufacturing sector is envisioned to be heavily influenced by artificial-intelligence-based technologies with the extraordinary increases in computational power and data volumes. A central challenge in the manufacturing sector lies in the requirement of a general framework to ensure satisfied diagnosis and monitoring performances in different manufacturing applications. Here, we propose a general data-driven,end-to-end framework for the monitoring of manufacturing systems. This framework, derived from deep-learning techniques, evaluates fused sensory measurements to detect and even predict faults and wearing conditions. This work exploits the predictive power of deep learning to automatically extract hidden degradation features from noisy, time-course data. We have experimented the proposed framework on 10 representative data sets drawn from a wide variety of manufacturing applications. Results reveal that the framework performs well in examined benchmark applications and can be applied in diverse contexts,indicating its potential use as a critical cornerstone in smart manufacturing.

关键词： manufacturing systems deep learning diagnosis and monitoring

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CA-MLIF: Cross-Attention and Multimodal Low-Rank Interaction Fusion Framework for Tumor Prognostic Prediction 39

CA-MLIF: Cross-Attention and Multimodal Low-Rank Interaction...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： An, Yajun Chen, Jiale Lin, Huan Liu, Zhenbing Feng, Siyang Zhang, Hualong Lan, Rushi Liu, Zaiyi Pan, Xipeng School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China School of Artificial Intelligence and Big Data Chongqing College of Finance and Economics Chongqing402160 China Southern Medical University Guangzhou510080 China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin541004 China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangzhou510080 China

ISBN: (纸本)157735897X

Cancer is a leading cause of death worldwide due to its aggressive nature and complex variability. Accurate prognosis is therefore challenging but essential for guiding personalized treatment and follow-up. Previous research often relied on single data sources, missing the opportunity to combine various types of patient information for more comprehensive survival predictions. To address these challenges, we propose a two-stage fusion method named Cross-Attention and Multimodal Low-Rank Interaction Fusion Framework (CA-MLIF). In the first stage, we propose a CA mechanism for real-time feature updates and cross-modal mutual learning to capture rich semantic information. In the second stage, we design a novel multimodal low-rank interaction fusion method for survival prediction. Specifically, we present modal attention mechanism (MAM) for feature filtration, low-rank multimodal fusion (LMF) for model complexity reduction, and optimal weight concatenation (OWC) for maximizing feature integration. Extensive experiments on two public datasets TCGA-GBMLGG and TCGA-KIRC, as well as a multi-center in-house lung adenocarcinoma (LUAD) dataset validate the effectiveness of CA-MLIF, which demonstrate that our method outperforms existing approaches in survival prediction under both pathology-gene fusion and CT-pathology fusion scenarios. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Learning to rank

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Computationally Expensive High-Dimensional Multiobjective Optimization via Surrogate-Assisted Reformulation and Decomposition

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IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： Pan, Linqiang Lin, Jianqing Wang, Handing He, Cheng Tan, Kay Chen Jin, Yaochu School of Artificial Intelligence and Automation Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China Huazhong University of Science and Technology Wuhan China School of Artificial Intelligence Xidian University Xi’an China School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan China Department of Computing The Hong Kong Polytechnic University Hong Kong Hong Kong School of Engineering Trustworth and General Artificial Intelligence Laboratory Westlake University Hangzhou China

In recent decades, various surrogate-assisted evolutionary algorithms (SAEAs) have been proposed to solve computationally expensive multiobjective optimization problems (EMOPs). Nevertheless, designing an SAEA to handle high-dimensional EMOPs and balance convergence, diversity, and computational complexity remains challenging. Here, we propose a two-phase SAEA (TP-SAEA), which follows the idea of convergence first and diversity second, for solving high-dimensional EMOPs. In Phase I, a surrogate-assisted problem reformulation method is proposed to fast-track the Pareto optimal set in association with some reference solutions. Specifically, the high-dimensional EMOP is reformulated into an expensive single-objective one with low-dimensional decision space. Then, the surrogate-assisted optimization is utilized to obtain well-converged solutions. In Phase II, the high-dimensional EMOP is decomposed into two subproblems to explore subregions of the decision space that can effectively promote the diversity of the solutions. The two subproblems are optimized independently via surrogate-assisted optimization, aiming to push the population towards different regions of the Pareto optimal front. Experiments are conducted on EMOPs with 100 to 500 decision variables compared with four state-of-the-art SAEAs. The proposed TP-SAEA obtains well-converged and diverse solutions with only 509 real function evaluations. Moreover, its superiority is examined in six real-world instances with up to 12,000 decision variables. IEEE

关键词： Iron

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Cross-Modality Person Re-Identification based on Shared Feature Attention Aggregation Algorithm 13

Cross-Modality Person Re-Identification based on Shared Feat...

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13th International Conference on information Science and Technology, ICIST 2023

作者： Wang, Jiong Zhang, Nian Xie, Linjuan Xiong, Jiang Song, XiaoLing Key Laboratory of Intelligent Information Processing and Control of Chongqing Municipal Institutions of Higher Education Chongqing Three Gorges University Chongqing40044 China Department of Electrical and Computer Engineering University of the District of Columbia WashingtonDC20008 United States College of Mathematics and Statistics Chongqing Three Gorges University Chongqing40044 China College of Computer Science and Engineerings Chongqing Three Gorges University Chongqing40044 China

ISBN: (纸本)9798350313925

Person re-identification in the visible-infrared spectrum (VI-ReID) is a challenging task due to significant difference of modalities, making it difficult to extract salient features across modalities. To solve this problem, in this paper, we propose a novel Cross-Modality Shared Feature Attention Aggregation algorithm (cm-SFAA) for VI-ReID. In the cm-SFAA approach, we first design a Random Channel Alternation Erasure Network to better learn robust features against occlusions and noise, as well as diverse feature representations under different channel combinations. This enhances the generalization ability of the model. Furthermore, a Local Feature Aggregation module is introduced in cm-SFAA, which utilizes an adaptive weighting mechanism to aggregate features from different levels and domains, thereby improving the performance of the model. Extensive experiments on two mainstream datasets SYSU-MM01 and RegDB show that cm-SFAA outperforms several baseline methods and acquires state-of-the-art performance. The source code of our proposed cm-SFAA is available at https://***/wdqlndl/cm-SFAA. © 2023 IEEE.

关键词： Computer vision

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A modified iterative algorithm for solving split equality problems

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2021年第3期83卷 45-54页

作者： Fu, Yuanmin Zhu, Li-Jun Wei, Haicheng School of Mathematics and Information Science North Minzu University Yinchuan750021 China The Key Laboratory of Intelligent Information and Big Data Processing of NingXia Province North Minzu University Yinchuan750021 China Health Big Data Research Institute of North Minzu University Yinchuan750021 China Advanced Intelligent Perception Control Technology Innovative Team of NingXia North Minzu University Yinchuan750021 China

In this paper, we investigate the split equality problems in Hilbert spaces. We propose an iterative algorithm with self-adaptive step-size for solving split equality prob-lems. Convergent analysis of the suggested algorithm is proved under some suitable conditions. © 2021, Politechnica University of Bucharest. All rights reserved.

关键词： Iterative methods

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