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DMHFR:Decoder with Multi-Head Feature Receptors for Tract Image Segmentation

computers, Materials & Continua 2025年第3期82卷 4841-4862页

作者： Jianuo Huang Bohan Lai Weiye Qiu Caixu Xu Jie He Department of Endoscopy Center Zhongshan Hospital(Xiamen)Fudan UniversityXiamen361015China School of Computing and Data Science Xiamen University MalaysiaSepang43900Malaysia School of Computer Science and Techonology Tongji UniversityShanghai200092China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou UniversityWuzhou543002China Xiamen Clinical Research Center for Cancer Therapy Xiamen361015China

The self-attention mechanism of Transformers,which captures long-range contextual information,has demonstrated significant potential in image ***,their ability to learn local,contextual relationships between pixels requires further *** methods face challenges in efficiently managing multi-scale fea-tures of different granularities from the encoder backbone,leaving room for improvement in their global representation and feature extraction *** address these challenges,we propose a novel Decoder with Multi-Head Feature Receptors(DMHFR),which receives multi-scale features from the encoder backbone and organizes them into three feature groups with different granularities:coarse,fine-grained,and full *** groups are subsequently processed by Multi-Head Feature Receptors(MHFRs)after feature capture and modeling *** include two Three-Head Feature Receptors(THFRs)and one Four-Head Feature Receptor(FHFR).Each group of features is passed through these MHFRs and then fed into axial transformers,which help the model capture long-range dependencies within the *** three MHFRs produce three distinct feature *** output from the FHFR serves as auxiliary auxiliary features in the prediction head,and the prediction output and their losses will eventually be *** results show that the Transformer using DMHFR outperforms 15 state of the arts(SOTA)methods on five public ***,it achieved significant improvements in mean DICE scores over the classic Parallel Reverse Attention Network(PraNet)method,with gains of 4.1%,2.2%,1.4%,8.9%,and 16.3%on the CVC-ClinicDB,Kvasir-SEG,CVC-T,CVC-ColonDB,and ETIS-LaribPolypDB datasets,respectively.

关键词： Medical image segmentation feature exploration feature aggregation deep learning multi-head feature receptor

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Post-Processing of Thresholding or Deep Learning Methods for Enhanced Tissue Segmentation of Whole-Slide Histopathological Images

Post-Processing of Thresholding or Deep Learning Methods for...

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18th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2025

作者： Marczyk, Michal Wrobel, Agata Merta, Julia Polanska, Joanna Department of Data Science and Engineering Silesian University of Technology Gliwice44-100 Poland Yale Cancer Center Yale School of Medicine New HavenCT06511 United States Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice44-100 Poland

Digital pathology allows for the efficient storage and advanced computational analysis of stained histopathological slides of various tissues. Tissue segmentation is a crucial first step of digital pathology aimed at eliminating background, pen markings, and other artifacts, reducing image size, and increasing the efficiency of further analysis. In most cases, color thresholding or deep learning models are used, but their effectiveness is reduced due to complex artifacts and huge color variations between slides. We propose a post processing method to increase the tissue segmentation performance of any initial segmentation algorithm. Using a set of 197 manually annotated histopathological images of breast cancer patients and 63 images of endometrial cancer patients, we tested our method with 3 thresholding techniques and 3 deep learning-based algorithms by calculating the Dice index, Jaccard index, precision, and recall. In both datasets, applying post processing increased precision and recall for thresholding methods and mostly precision for deep learning models. Overall, applying post-processing gave better tissue segmentation performance than initial segmentation methods, significantly increasing Dice and Jaccard indices. Our results proved that thanks to post-processing, the tissue segmentation pipeline is more robust to noises and artifacts commonly present in histopathological images. © 2025 by SCITEPRESS– science and Technology Publications, Lda.

关键词： Image segmentation

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Network Traffic Co-Movement Assessment via Oriented Basis Signal Processing and Ensemble Decision Trees 27

Network Traffic Co-Movement Assessment via Oriented Basis Si...

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27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Mohebbi, Mohammad Reza Hassan Zada, Mohammad Javad Rostami-Shahrbabaki, Majid Doller, Mario Yamnenko, Iuliia University of Applied Sciences Josef Ressel Center Vision2Move Project Tirol Kufstein Austria University of Passau Department of Computer Science Germany Technical University of Munich Traffic Engineering and Control Germany National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Department of Electronic Devices and Systems Kyiv Ukraine

ISBN: (纸本)9798331505929

Accurate short-term network-wide traffic prediction is essential to guarantee high service quality in urban traffic control systems. Nevertheless, traffic state time series represent network-scale spatiotemporal co-movement patterns and location-specific features. Therefore, hybrid statistical machine learning (ml) algorithms could be utilized to accommodate the aforementioned characteristics. In this paper, a hybrid random forest (rf) and extreme gradient boosting tree (XGBoost) model is introduced for network-wide traffic prediction. Moreover, a multi-noise oriented basis wavelet transform (OBT) filter is employed to pre-process the original time series, and improve the predictive accuracy. The RF model captures the traffic co-movements and the XGBoost extracts the local information. Comparative analysis of the hybrid algorithm and deep learning-based benchmarks indicate better performance of the proposed methodology in hourly traffic state prediction of 30 loop detectors, located in the paris city center. Hence, the hybrid decision-tree-based mechanism is a useful framework for real-time network traffic forecasting offering fewer trainable (hyper-)parameters, and thus, lower computational cost. © 2024 IEEE.

关键词： Time series

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Transfer Route Recommendation for Metro Systems Based on Multi-source Data Fusion 24

Transfer Route Recommendation for Metro Systems Based on Mul...

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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Yuan, Tailan Xiong, Wen Yun, Lijun School of Information Science and Technology Yunnan Normal University Kunming China Engineering Research Center of Computer Vision and Intelligent Control Technology Department of Education of Yunnan Province Kunming China

ISBN: (纸本)9798350319934

As the network-structure complexity and the passenger volume continuously grow, how to accurately perform passenger flow guidance is a crucial issue for metro operators. Transfer route recommendation is a core approach for passenger flow guidance. Existing works are limited in obtaining route attributes (such as transfer walking time and waiting time, etc.). Furthermore, most of existing methods may not work well in the scenario of sudden passenger flow, because of their limited capability to accurately measure the real-time crowding. To address these issues, a data fusion based transfer route recommendation method (DFTRR) is proposed. Specifically, we first propose a spatio-temporal diversity-based method (STD) to measure the trajectory similarity. Based on that, we fuse two kinds of heterogeneous data, i.e., swiping records of smart cards and Wi-Fi signal records of mobile devices to extract a passenger's complete trip. And then we present the historical median travel time to alleviate the computation issue of route attributes. Meanwhile the mean crowding level is introduced to accurately measure the real-time crowding of routes. Finally, a generalized travel cost based on median travel time, number of transfers and mean crowding level is constructed for transfer route recommendation. The experimental results illustrate that the accuracy of STD algorithm is higher than the baseline 34.92% at maximum;DFTRR can offer the optimal transfer route according to passengers' preferences under sudden passenger flow. © 2022 IEEE.

关键词： Smart cards

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Two-Step Online Trajectory Planning of a Quadcopter in Indoor Environments with Obstacles

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IFAC-PapersOnLine 2023年第2期56卷 11002-11009页

作者： M. Zimmermann M.N. Vu F. Beck A. Nguyen A. Kugi Automation and Control Institute (ACIN) TU Wien Vienna Austria Center for Vision Automation and Control AIT Austrian Institute of Technology Vienna Austria Department of Computer Science University of Liverpool Liverpool England

This paper presents a two-step algorithm for online trajectory planning in indoor environments with unknown obstacles. In the first step, sampling-based path planning techniques such as the optimal Rapidly exploring Random Tree (RRT*) algorithm and the Line-of-Sight (LOS) algorithm are employed to generate a collision-free path consisting of multiple waypoints. Then, in the second step, constrained quadratic programming is utilized to compute a smooth trajectory that passes through all computed waypoints. The main contribution of this work is the development of a flexible trajectory planning framework that can detect changes in the environment, such as new obstacles, and compute alternative trajectories in real time. The proposed algorithm actively considers all changes in the environment and performs the replanning process only on waypoints that are occupied by new obstacles. This helps to reduce the computation time and realize the proposed approach in real time. The feasibility of the proposed algorithm is evaluated using the Intel Aero Ready-to-Fly (RTF) quadcopter in simulation and in a real-world experiment.

关键词： quadcopter motion planning optimal rapidly exploring random tree (RRT*) constrained quadratic programming differential flatness trajectory planning

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Privacy-protected object detection through trustworthy image fusion

Privacy-protected object detection through trustworthy image...

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作者： Zhang, Chao Zhang, Jinmei Yun, Lijun Zhang, Jun Su, Junbo Yunnan Key Laboratory of Optoelectronic Information Technology Yunnan Normal University Kunming China School of Physics and Electronic Information Yunnan Normal University Kunming China Engineering Research Center of Computer Vision and Intelligent Control Technology Department of Education of Yunnan Province Yunnan Normal University Kunming China Kunming Institute of Physics Ordnance Science and Research Academy of China Kunming China

The neural network-based technologies have emerged as a potent method for image fusion, object detection, and other computer vision tasks as the rapid development of deep learning. Multi-band infrared images, in particular, capture a more extensive range of radiation details and information compared to conventional single-band infrared images. Consequently, the fusion of multi-band infrared images can provide more features for object detection. However, it is crucial to consider that infrared images may contain sensitive information, potentially leading to privacy concerns. Ensuring datasets privacy protection plays a crucial role in the fusion and tracking process. To address both the need for improved detection performance and the necessity for privacy protection in the infrared environment, we proposed a procedure for object detection based on multi-band infrared image datasets and utilized the transfer learning technique to migrate knowledge learned from external infrared data to internal infrared data, thereby training the infrared image fusion model and detection model. The procedure consists of several steps: (1) data preprocessing of multi-band infrared images, (2) multi-band infrared image fusion, and (3) object detection. Standard evaluation metrics for image fusion and object detection ensure the authenticity of the experiments. The comprehensive validation experiments demonstrate the effectiveness of the proposed procedure in object detection tasks. Furthermore, the transfer learning can train our datasets and update the model without exposing the original data. This aspect of transfer learning is particularly beneficial for maintaining the privacy of multi-band infrared images during the fusion and detection processes. © 2024 John Wiley & Sons Ltd.

关键词： Object detection

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Test-Time Adaptation with Robust Dual-stream Perturbation for Stable Agent Deployment in Dynamic Scenarios

Test-Time Adaptation with Robust Dual-stream Perturbation fo...

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International Conference on computer Supported Cooperative Work in Design

作者： Haoyu Xiong Chengfu Yang School of Information Science and Technology Yunnan Normal University Kunming China Department of Education of Yunnan Province Engineering Research Center of Computer Vision and Intelligent Control Technology Kunming China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Test-Time Adaptation (TTA) aims to provide a deployed agent capable of adapting to the target domain distribution using only unlabeled test data. Most existing TTA methods have achieved success under mild conditions, such as independently sampled data from a single or multiple static domains. However, these attempts may cause the deployed agent to fail in dynamic scenarios, where the test environment undergoes continuous changes over time. Previous TTA methods attempt to improve the quality of pseudo-labels by designing data augmentation strategies based on domain information. However, this violates the TTA paradigm, i.e., no characteristics of the dataset can be known in advance. Motivated by this, we propose a Robust Dual-stream Perturbation Test-time adaptation approach, called RDPT, to further stabilize the deployed agent from two aspects: (1) boost the robustness to noisy samples by encouraging the model to be consistent with its original image-level perturbation samples; (2) dynamically filtering samples with high entropy values, taking into account the discrepancy between the original and perturbation samples. Extensive experiments on the CIFAR10C and CIFAR100C benchmarks demonstrate the effectiveness of our method in dynamic scenarios, which is also easy to implement and deploy. 1

关键词： Adaptation models Filtering Federated learning Perturbation methods computational modeling Predictive models Data augmentation

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Network Traffic Co-Movement Assessment via Oriented Basis Signal Processing and Ensemble Decision Trees

Network Traffic Co-Movement Assessment via Oriented Basis Si...

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International Conference on Intelligent Transportation

作者： Mohammad Reza Mohebbi Mohammad Javad Hassan Zada Majid Rostami-Shahrbabaki Mario Döller Iuliia Yamnenko Josef Ressel Center Vision2Move project University of Applied Sciences Kufstein Tirol Austria Department of Computer Science University of Passau Germany Chair of Traffic Engineering and Control Technical University of Munich Germany Department of Electronic Devices and Systems National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” Kyiv Ukraine

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

关键词： Wavelet transforms Radio frequency Accuracy Machine learning algorithms Urban areas Signal processing algorithms Detectors Prediction algorithms Decision trees Random forests

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Traffic Signal control for Large-Scale Urban Traffic Networks: Real-World Experiments using vision-based Sensors

Traffic Signal Control for Large-Scale Urban Traffic Network...

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International Conference on control and Automation (ICCA)

作者： Jiho Park Tong Liu Chieh Wang Hong Wang Qichao Wang Zhong-Ping Jiang Department of Electrical and Computer Engineering Control and Networks Lab Tandon School of Engineering New York University Brooklyn NY USA Oak Ridge National Laboratory Oak Ridge TN USA National Renewable Energy Laboratory Computational Science Center Golden CO USA

ISBN: (数字)9798350354409

ISBN: (纸本)9798350354416

Effective control of traffic signals plays a critical role in ensuring smooth vehicle flow in urban areas. Expertly engineered traffic signal controllers can considerably minimize travel delays and enhance sustainability. In this paper, the team proposes the Model Predictive control (MPC) traffic signal control strategy using real-time traffic flow data from a vision-based camera as feedback information. Also, a realistic signal timing plan that considers National Electrical Manufacturers Association (NEMA) constraints has been developed to be applied to real-world scenarios. The primary aim is to reduce the number of vehicles across all links in the controlled area, thereby optimizing traffic flow and reducing energy consumption. To validate the proposed method, several real-life experiments were conducted at 24 intersections in Chattanooga, Tennessee, by collaborating with traffic field engineers. These experiments demonstrated significant performance improvements in comparison to the existing method.

关键词： Energy consumption Urban areas Decentralized control Data processing Real-time systems Sensors Delays

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Extracting Spatial High Utility Co-location Patterns Based on Fuzzy Feature Clusters 9th

Extracting Spatial High Utility Co-location Patterns Based o...

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9th China National Conference on Big Data and Social Computing, BDSC 2024

作者： Jin, Peijie Wang, Xiaoxuan Xiong, Wen Wang, Lizhen Gao, Song School of Information Science and Technology Yunnan Normal University Kunming650500 China Engineering Research Center of Computer Vision and Intelligent Control Technology Department of Education of Yunnan Province Kunming650500 China Dianchi College Yunnan University Kunming650500 China School of Software Yunnan University Kunming650500 China

ISBN: (纸本)9789819758029

Sets of spatial features whose instances frequently appear together in nearby areas are regarded as spatial co-location patterns (SCPs). Spatial high utility co-location pattern(SHUCP), which is an extended research of SCPs, can better reflect the high quality aggregation phenomenon among spatial features. Utility participation index (UPI) is a commonly used adaptive utility measurement in most of the existing methods. Unfortunately, UPI does not take into account the impacts of fuzziness and overlap of proximity relationships on the calculation of utility. Additionally, UPI mainly relies on the computation of the instance layer and does not satisfy the downward closure property. In this paper, we combines fuzzy set theory to establish fuzzy neighbor relationships and discuss the issue of instance overlap between neighbor relationships. Then, a new calculation method for the utility index between spatial features has been design with considering fuzzy contribution score of instances. Using Fuzzy Chameleon Clustering algorithm (FCC) based on fuzzy utility index, this paper further extracts SHUCPs from different high utility fuzzy feature clusters. Experiments are conducted on three synthetic data sets and two real data sets to prove the rationality and effectiveness of the proposed algorithm. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Fuzzy set theory

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