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Evaluation of techniques for automated classification and artery quantification of the circle of Willis on TOF-MRA images: The CROWN challenge

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Medical image analysis 2025年 105卷 103650页

作者： Iris N Vos Ynte M Ruigrok Edwin Bennink Mireille R E Velthuis Barbara Paic Maud E H Ophelders Myrthe A D Buser Bas H M van der Velden Geng Chen Matthieu Coupet Félix Dumais Adrian Galdran Zhang Junyi Wei Liu Ting Ma Madhu S Nair Mathieu Naudin Preena K P Keerthi A S Pillai Pengcheng Shi Thierry Urruty Dai Yakang Kaiyuan Yang Fabio Musio Bjoern H Menze Birgitta K Velthuis Hugo J Kuijf Image Sciences Institute UMC Utrecht Utrecht The Netherlands. Electronic address: i.n.vos-6@umcutrecht.nl. Department of Neurology and Neurosurgery University Medical Center Utrecht Utrecht The Netherlands. Image Sciences Institute UMC Utrecht Utrecht The Netherlands. Department of Radiology University Medical Center Utrecht Utrecht The Netherlands. Princess Máxima Center for Pediatric Oncology 3584 CS Utrecht The Netherlands. Suzhou Institute of Biomedical Engineering and Technology Chinese Academy of Sciences Suzhou China Jinan Guoke Medical Technology Development Co. Ltd Jinan China. XLIM Laboratory University of Poitiers UMR CNRS 7252 Poitiers France I3M Common Laboratory CNRS-Siemens University and Hospital of Poitiers Poitiers France. Department of Computer Sciences Université de Sherbrooke Sherbrooke Canada. Universitat Pompeu Fabra Barcelona Spain. School of Electronic & Information Engineering Suzhou University of Science and Technology Suzhou China. Electronic & Information Engineering School Harbin Institute of Technology (Shenzhen) Shenzhen China. Artificial Intelligence & Computer Vision Lab Department of Computer Science Cochin University of Science and Technology (CUSAT) Kochi 682022 Kerala India. University Hospital CHU Poitiers 86000 France DACTIM-MIS/LMA Laboratory University of Poitiers UMR CNRS 7348 Poitiers 86000 France. Suzhou Institute of Biomedical Engineering and Technology Chinese Academy of Sciences Suzhou China. Department of Quantitative Biomedicine University of Zurich Zurich Switzerland.

Assessing risk factors for intracranial aneurysm (IA) development on images is crucial for early detection of high-risk cases. IAs often form at bifurcations within the circle of Willis (CoW), but manual assessment of these arteries is both time-consuming and susceptible to inconsistencies. Previous studies on imaging markers for IA development lack sufficient evidence for clinical implications, highlighting the need for automated methods to assess CoW morphology. No systematic approach currently exists to identify the best methodological strategies. To address this, we organized a scientific challenge to compare various techniques against a clinical reference standard. Participants were tasked with (1) automated classification of CoW anatomical variants and (2) automated prediction of CoW artery diameters and bifurcation angles. We provided 300 TOF-MRA scans for training and another 300 for testing, all manually annotated. Submissions were evaluated using balanced accuracy, mean absolute error, and Pearson correlation coefficient metrics. This paper provides a detailed analysis of the results from six participating teams. The findings show that various methods may be suitable for automated CoW assessment, but that these need further improvement to meet clinical standards. The challenge remains open for future submissions, offering a benchmark for new techniques.

关键词： Circle of Willis Classification MR angiography Quantification

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Compressive Sensing Sparse Sampling Method for Composite Material Based on Principal Component Analysis

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Transactions of Nanjing University of Aeronautics and Astronautics 2018年第2期35卷 282-289页

作者： Sun Yajie Gu Feihong Ji Sai Wang Lihua Jiangsu Engineering Centre of Network Monitoring Nanjing University of Information Science and Technology Nanjing 210044 P. R. China Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology Nanjing University of Information Science and Technology Nanjing 210044 P. R. China School of Computer and Software Nanjing University of Information Science and Technology Nanjing 210044 P. R. China School of Information and Control Nanjing University of Information Science and Technology Nanjing 210014 P. R. China

Signals can be sampled by compressive sensing theory with a much less rate than those by traditional Nyquist sampling theorem,and reconstructed with high probability,only when signals are sparse in the time domain or a transform *** signals are not sparse in real world,but can be expressed in sparse form by some kind of sparse *** used sparse transformations will lose some information,because their transform bases are generally *** this paper,we use principal component analysis for data reduction,and select new variable with low dimension and linearly correlated to the original variable,instead of the original variable with high dimension,thus the useful data of the original signals can be included in the sparse signals after dimensionality reduction with maximize ***,the loss of data can be reduced as much as possible,and the efficiency of signal reconstruction can be ***,the composite material plate is used for the experimental *** experimental result shows that the sparse representation of signals based on principal component analysis can reduce signal distortion and improve signal reconstruction efficiency.

关键词： principal component analysis compressive sensing sparse representation signal reconstruction

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A novel convolution neural network for background segmentation recognition 4th

A novel convolution neural network for background segmentati...

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4th International Conference on Cloud Computing and Security, ICCCS 2018

作者： Fang, Wei Ding, Yewen Zhang, Feihong School of Computer and Software Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (纸本)9783030000059

The convolution neural network for image classification is an application of deep learning on image processing. Convolutional neural networks have the advantage of being able to convolve directly with image pixels and extract image features from image pixels. This approach is closer to the treatment of the human brain’s visual system. However, up to now, it is impossible to achieve 100% classification accuracy regardless of any kind of convolutional neural network models. At the same time, we also found that sometimes the picture background will also affect the recognition effect of the neural network on the picture, and after removing the picture background, it can correctly recognize the same picture. Therefore, we design a model based on the convolutional neural network to remove the image background for some specific images (such as selfies, animals, flowers, etc.) here, and then the image after processing will be identified and classified. Experiments show that the proposed method can maintain a high level of integrity of the target to be detected in the image after removing the background (here the indicator is Intersection Over Union, IOU);moreover, through multiple classification models verify that the classification accuracy of some background-removed pictures is significantly higher than that of pictures without any treatment. © Springer Nature Switzerland AG 2018.

关键词： Image classification

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CRNN Based Jersey-Bib Number/Text Recognition in Sports and Marathon Images

CRNN Based Jersey-Bib Number/Text Recognition in Sports and ...

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International Conference on Document Analysis and Recognition

作者： Sauradip Nag Raghavendra Ramachandra Palaiahnakote Shivakumara Umapada Pal Tong Lu Mohan Kankanhalli Department of Computer Science & Engineering Kalyani Government Engineering College Kalyani India Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Norway Faculty of Computer System and Information Technology University of Malaya Malaysia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India National Key Lab for Novel Software Technology Nanjing University China Department of computer science National University of Singapore Singapore

The primary challenge in tracing the participants in sports and marathon video or images is to detect and localize the jersey/Bib number that may present in different regions of their outfit captured in cluttered environment conditions. In this work, we proposed a new framework based on detecting the human body parts such that both Jersey Bib number and text is localized reliably. To achieve this, the proposed method first detects and localize the human in a given image using Single Shot Multibox Detector (SSD). In the next step, different human body parts namely, Torso, Left Thigh, Right Thigh, that generally contain a Bib number or text region is automatically extracted. These detected individual parts are processed individually to detect the Jersey Bib number/text using a deep CNN network based on the 2-channel architecture based on the novel adaptive weighting loss function. Finally, the detected text is cropped out and fed to a CNN-RNN based deep model abbreviated as CRNN for recognizing jersey/Bib/text. Extensive experiments are carried out on the four different datasets including both bench-marking dataset and a new dataset. The performance of the proposed method is compared with the state-of-the-art methods on all four datasets that indicates the improved performance of the proposed method on all four datasets.

关键词： Text recognition Sports Image recognition Hip Thigh Torso Biological system modeling

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An automatic biomedical ontology meta-matching technique

Journal of Network Intelligence

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Journal of Network Intelligence 2019年第3期4卷 109-113页

作者： Xue, Xingsi Yang, Haiyan Zhang, Jie Zhang, Jing Chen, Dongxu College of Information Science and Engineering Intelligent Information Processing Research Center Fujian Key Lab for Automotive Electronics and Electric Drive Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology No.3 Xueyuan Road University Town Minhou Fuzhou CityFujian Province350118 China College of Information Science and Engineering Fujian University of Technology No.33 Xuefu South Road University Town Minhou Fuzhou CityFujian Province350118 China School of Computer Science and Engineering Yulin Normal University No.299 Education Middle Road Yulin CityGuanxi Province537000 China School of Computing Ulster University Jordanstown BT370QB United Kingdom Fujian Medical University Union Hospital No.29 Xinquan Road Gulou Fuzhou CityFujian Province350001 China

Biomedical ontology matching aims at determining the heterogeneous biomed-ical concepts, and bridging the semantic gap between heterogeneous biomedical ontologies. The foundation of a biomedical ontology matching technique is the Biomedical Concept Similarity Measure (BCSM), which calculates the similarity value between two biomed-ical concepts. Since various BCSMs have different advantages, usually several BCSMs are aggregated together to improve the result’s confidence. How to tune the aggregating weights to ensure the quality of the alignment is called biomedical ontology meta-matching problem, which is a challenge in the ontology matching domain. Currently, researchers mainly focus on how to tune the aggregating weights for various similarity measures to improve the quality of the ontology alignments. However, the ignorance of the effects brought about by different biomedical concept mapping’s preference on different similarity measures significantly reduces the alignment’s quality. To overcome this drawback, in this work, we formally define the biomedical ontology meta-matching problem, and then present an Ordered Weighted Average (OWA) based approach to automatically aggregate various biomedical concept similarity measures. In our method, the aggregating weights determined for each concept mapping is associated with the ordered position of the similarity value instead of a particular concept similarity measure. The experiment utilizes the large biomed track provided by Ontology Alignment Evaluation Initiative (OAEI) to test our proposal’s performance, and the experimental results show the effectiveness of our proposal. © 2019, Taiwan Ubiquitous information. All rights reserved.

关键词： Semantics

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Learning data-adaptive non-parametric kernels

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2020年第1期21卷 8590-8628页

作者： Fanghui Liu Xiaolin Huang Chen Gong Jie Yang Li Li Department of Electrical Engineering ESAT-STADIUS KU Leuven Belgium Institute of Image Processing and Pattern Recognition Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology China and Department of Computing Hong Kong Polytechnic University Hong Kong SAR China Department of Automation BNRist Tsinghua University China

In this paper, we propose a data-adaptive non-parametric kernel learning framework in margin based kernel methods. In model formulation, given an initial kernel matrix, a data-adaptive matrix with two constraints is imposed in an entry-wise scheme. Learning this data-adaptive matrix in a formulation-free strategy enlarges the margin between classes and thus improves the model flexibility. The introduced two constraints are imposed either exactly (on small data sets) or approximately (on large data sets) in our model, which provides a controllable trade-off between model flexibility and complexity with theoretical demonstration. In algorithm optimization, the objective function of our learning framework is proven to be gradient-Lipschitz continuous. Thereby, kernel and classifier/regressor learning can be efficiently optimized in a unified framework via Nesterov's acceleration. For the scalability issue, we study a decomposition-based approach to our model in the large sample case. The effectiveness of this approximation is illustrated by both empirical studies and theoretical guarantees. Experimental results on various classification and regression benchmark data sets demonstrate that our non-parametric kernel learning framework achieves good performance when compared with other representative kernel learning based algorithms.

关键词： support vector machines non-parametric kernel learning gradient-Lipschitz continuous

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Quantum Algorithm for Appointment Scheduling

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Journal of Quantum Computing 2019年第2期1卷 111-118页

作者： Wenbin Yu Yinsong Xu Wenjie Liu Alex Xiangyang Liu Baoyu Zheng Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science&TechnologyNanjing210044China Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science&TechnologyNanjing210044China School of Computer and Software Nanjing University of Information Science&TechnologyNanjing210044China Department of Computer Science and Engineering Michigan State UniversityEast LansingMI 48824-1266USA National Engineering Research Center for Communications and Network Technology Nanjing University of Posts and TelecommunicationsNanjing210003China

Suppose a practical scene that when two or more parties want to schedule anappointment, they need to share their calendars with each other in order to make itpossible. According to the present result the whole communication cost to solve thisproblem should be their calendars’ length by using a classical algorithm. In this work, weinvestigate the appointment schedule issue made by N users and try to accomplish it inquantum information case. Our study shows that the total communication cost will bequadratic times smaller than the conventional case if we apply a quantum algorithm in theappointment-scheduling problem.

关键词： Quantum algorithm appointment scheduling quantum speed-up

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Cross-media user profiling with joint textual and social user embedding 27

Cross-media user profiling with joint textual and social use...

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27th International Conference on Computational Linguistics, COLING 2018

作者： Wang, Jingjing Li, Shoushan Jiang, Mingqi Wu, Hanqian Zhou, Guodong NLP Lab. School of Computer Science and Technology Soochow University China Collaborative Innovation Center of Novel Software Technology and Industrialization School of Computer Science and Engineering Southeast University China

ISBN: (纸本)9781948087506

In realistic scenarios, a user profiling model (e.g., gender classification or age regression) learned from one social media might perform rather poorly when tested on another social media due to the different data distributions in the two media. In this paper, we address cross-media user profiling by bridging the knowledge between the source and target media with a uniform user embedding learning approach. In our approach, we first construct a cross-media user-word network to capture the relationship among users through the textual information and a modified cross-media user-user network to capture the relationship among users through the social information. Then, we learn user embedding by jointly learning the heterogeneous network composed of above two networks. Finally, we train a classification (or regression) model with the obtained user embeddings as input to perform user profiling. Empirical studies demonstrate the effectiveness of the proposed approach to two cross-media user profiling tasks, i.e., cross-media gender classification and cross-media age regression. © 2018 COLING 2018 - 27th International Conference on Computational Linguistics, Proceedings. All rights reserved.

关键词： Embeddings

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Server-Aided Multi-Secret Sharing Scheme for Weak Computational Devices

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computers, Materials & Continua 2018年第9期56卷 401-414页

作者： En Zhang Xintao Duan Siuming Yiu Junbin Fang Zoe L.Jiang Tsz HonYuen Jie Peng College of Computer and Information Engineering Henan Normal UniversityXinxiang453007China Lab of Intelligence Business&Internet of Things of Henan Province Xinxiang453007China Department of Computer Science University of Hong KongHong KongChina Department of Optoelectronic Engineering Jinan UniversityGuangzhou510632China School of Computer Science and Technology Harbin nstitute of Technology Shenzhen Graduate SchoolShenzhen518055China Huawei Singapore

In the setting of(t,n)threshold secret sharing,at least t parties can reconstruct the secret,and fewer than t parties learn nothing about the ***,to achieve fairness,the existing secret sharing schemes either assume a trusted party exists or require running multi-round,which is not practical in a real *** addition,the cost of verification grows dramatically with the number of participants and the communication complexity is O(t),if there is not a trusted combiner in the reconstruction *** this work,we propose a fair server-aided multi-secret sharing scheme for weak computational *** malicious behavior of clients or server providers in the scheme can be verified,and the server provider learns nothing about the secret shadows and the *** other secret sharing schemes,our scheme does not require interaction among users and can work in asynchronous mode,which is suitable for mobile networks or cloud computing environments since weak computational mobile devices are not always ***,in the scheme,the secret shadow is reusable,and expensive computation such as reconstruction computation and homomorphic verification computation can be outsourced to the server provider,and the users only require a small amount of computation.

关键词： Secret sharing server-aided non-interactive fairness

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Foundation models and intelligent decision-making: Progress, challenges, and perspectives

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Innovation 2025年第6期6卷 100948页

作者： Huang, Jincai Xu, Yongjun Wang, Qi Wang, Qi [Cheems] Liang, Xingxing Wang, Fei Zhang, Zhao Wei, Wei Zhang, Boxuan Huang, Libo Chang, Jingru Ma, Liantao Ma, Ting Liang, Yuxuan Zhang, Jie Guo, Jian Jiang, Xuhui Fan, Xinxin An, Zhulin Li, Tingting Li, Xuefei Shao, Zezhi Qian, Tangwen Sun, Tao Diao, Boyu Yang, Chuanguang Yu, Chenqing Wu, Yiqing Li, Mengxian Zhang, Haifeng Zeng, Yongcheng Zhang, Zhicheng Zhu, Zhengqiu Lv, Yiqin Li, Aming Chen, Xu An, Bo Xiao, Wei Bai, Chenguang Mao, Yuxing Yin, Zhigang Gui, Sheng Su, Wentao Zhu, Yinghao Gao, Junyi He, Xinyu Li, Yizhou Jin, Guangyin Ao, Xiang Zhai, Xuehao Tan, Haoran Yun, Lijun Shi, Hongquan Li, Jun Fan, Changjun Huang, Kuihua Harrison, Ewen Leung, Victor C.M. Qiu, Sihang Dong, Yanjie Zheng, Xiaolong Wang, Gang Zheng, Yu Wang, Yuanzhuo Guo, Jiafeng Wang, Lizhe Cheng, Xueqi Wang, Yaonan Yang, Shanlin Fu, Mengyin Fei, Aiguo Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Department of Automation Tsinghua University Beijing100084 China Huazhong University of Science and Technology Wuhan430074 China School of Automation Beijing Institute of Technology Beijing100081 China School of Information Science and Engineering Dalian Polytechnic University Dalian116034 China National Engineering Research Center for Software Engineering Peking University Beijing100871 China Department of Oral Implantology Peking University School and Hospital of Stomatology Beijing100081 China Guangzhou511453 China College of Information and Electrical Engineering China Agricultural University Beijing100083 China IDEA Research International Digital Economy Academy Shenzhen518057 China Institute of Automation Chinese Academy of Sciences Beijing100190 China The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China College of Science National University of Defense Technology Changsha410073 China Center for Systems and Control College of Engineering Peking University Beijing100871 China Gaoling School of Artificial Intelligence Renmin University of China Beijing100872 China College of Systems Engineering National University of Defense Technology Changsha410073 China Laboratory for Big Data and Decision National University of Defense Technology Changsha410073 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore School of Electrical Engineering Chongqing University Chongqing400044 China Department of Mathematics The University of Hong Kong Hong Kong SAR999077 China School of Food Science and Technology Dalian Polytechnic University Dalian116034 China Centre for Medical Informatics University of Edinburgh EdinburghEH16 4UX United Kingdom Department of Stomatology Peking Union Medical College Hospital Chinese Academ

Intelligent decision-making (IDM) is a cornerstone of artificial intelligence (AI) designed to automate or augment decision processes. Modern IDM paradigms integrate advanced frameworks to enable intelligent agents to make effective and adaptive choices and decompose complex tasks into manageable steps, such as AI agents and high-level reinforcement learning. Recent advances in multimodal foundation-based approaches unify diverse input modalities—such as vision, language, and sensory data—into a cohesive decision-making process. Foundation models (FMs) have become pivotal in science and industry, transforming decision-making and research capabilities. Their large-scale, multimodal data-processing abilities foster adaptability and interdisciplinary breakthroughs across fields such as healthcare, life sciences, and education. This survey examines IDM's evolution, advanced paradigms with FMs and their transformative impact on decision-making across diverse scientific and industrial domains, highlighting the challenges and opportunities in building efficient, adaptive, and ethical decision systems. © 2025 The Authors

关键词： Reinforcement learning

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