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Multi-Class Segmentation of Aortic Branches and Zones in Computed Tomography Angiography: The AortaSeg24 Challenge

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arXiv 2025年

作者： Imran, Muhammad Krebs, Jonathan R. Sivaraman, Vishal Balaji Zhang, Teng Kumar, Amarjeet Ueland, Walker R. Fassler, Michael J. Huang, Jinlong Sun, Xiao Wang, Lisheng Shi, Pengcheng Rokuss, Maximilian Baumgartner, Michael Kirchhof, Yannick Maier-Hein, Klaus H. Isensee, Fabian Liu, Shuolin Han, Bing Nguyen, Bong Thanh Shin, Dong-Jin Ji-Woo, Park Choi, Mathew Uhm, Kwang-Hyun Ko, Sung-Jea Lee, Chanwoong Chun, Jaehee Kim, Jin Sung Zhang, Minghui Zhang, Hanxiao You, Xin Gu, Yun Pan, Zhaohong Liu, Xuan Liang, Xiaokun Tiefenthaler, Markus Almar-Munoz, Enrique Schwab, Matthias Kotyushev, Mikhail Epifanov, Rostislav Wodzinski, Marek Müller, Henning Qayyum, Abdul Mazher, Moona Niederer, Steven A. Wang, Zhiwei Yang, Kaixiang Ren, Jintao Korreman, Stine Sofia Gao, Yuchong Zeng, Hongye Zheng, Haoyu Zheng, Rui Yue, Jinghua Zhou, Fugen Liu, Bo Cosman, Alexander Liang, Muxuan Zhao, Chang Upchurch, Gilbert R. Ma, Jun Zhou, Yuyin Cooper, Michol A. Shao, Wei Department of Medicine University of Florida GainesvilleFL32611 United States Department of Surgery University of Florida GainesvilleFL32611 United States Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States Department of Computer Information Science and Engineering University of Florida GainesvilleFL32611 United States Shanghai Jiao Tong University Shanghai China Electronic & Information Engineering School Harbin Institute of Technology [Shenzhen Shenzhen China Division of Medical Image Computing German Cancer Research Center [DKFZ Heidelberg Germany CO. LTD Beijing China MedAI Seoul Korea Republic of Korea University Seoul Korea Republic of Department of Radiation Oncology Yonsei Cancer Center Heavy Ion Therapy Research Institute Yonsei University College of Medicine Seoul Korea Republic of Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Medical University of Innsbruck Innsbruck Austria Novosibirsk State University Novosibirsk Russia AGH University of Krakow Krakow Poland Sierre Switzerland University of Geneva Geneva Switzerland The Sense Innovation and Research Center Sion Switzerland National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Hubei Wuhan China Aarhus University Department of Clinical Medicine Nordre Palle Juul-Jensens Blvd. 11 Aarhus8200 Denmark Shanghaitech University Pudong Shanghai China Image Processing Center Beihang University Beijing China Department of Computer Science and Engineering University of California Santa CruzCA95064 United States Department of Biostatistics University of Florida GainesvilleFL32611 United States University Health Network ON Canada Agronomy Department University of F

Multi-class segmentation of the aorta in computed tomography angiography (CTA) scans is essential for diagnosing and planning complex endovascular treatments for patients with aortic dissections. However, existing methods reduce aortic segmentation to a binary problem, limiting their ability to measure diameters across different branches and zones. Furthermore, no open-source dataset is currently available to support the development of multi-class aortic segmentation methods. To address this gap, we organized the AortaSeg24 MICCAI Challenge, introducing the first dataset of 100 CTA volumes annotated for 23 clinically relevant aortic branches and zones. This dataset was designed to facilitate both model development and validation. The challenge attracted 121 teams worldwide, with participants leveraging state-of-the-art frameworks such as nnU-Net and exploring novel techniques, including cascaded models, data augmentation strategies, and custom loss functions. We evaluated the submitted algorithms using the Dice Similarity Coefficient (DSC) and Normalized Surface Distance (NSD), highlighting the approaches adopted by the top five performing teams. This paper presents the challenge design, dataset details, evaluation metrics, and an in-depth analysis of the top-performing algorithms. The annotated dataset, evaluation code, and implementations of the leading methods are publicly available to support further research. All resources can be accessed at https://***. © 2025, CC BY-NC-SA.

关键词： Computerized tomography

Back-end-of-line CMOS-compatible diode fabrication with pure boron deposition down to 50 °C

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Back-end-of-line CMOS-compatible diode fabrication with pure...

European Conference on Solid-State Device Research (ESSDERC)

作者： Tihomir Knežević Tomislav Suligoj Xingyu Liu Lis K. Nanver Ahmed Elsayed Jan F. Dick Joerg Schulze Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia Faculty of Electrical Engineering Mathematics & Computer Science University of Twente Enschede The Netherlands Institute for Semiconductor Engineering University of Stuttgart Stuttgart Germany

Pure boron deposited on silicon for the formation of p + n-like junctions was studied for deposition temperatures down to 50 °C. The commonly used chemical-vapor deposition method was compared to molecular beam epitaxy with respect to the electrical characteristics and the boron-layer compactness as evaluated by etch tests, ellipsometry and atomic force microscopy. Electrically, the important parameters are minority carrier electron injection into the p-type region and the sheet resistance along the boron-to-silicon interface which appear to be independent of deposition method for temperatures down to 300 °C. Only with molecular beam epitaxy did we succeed in producing substantial layers for the lower temperatures down to 50 °C. Also, at this very low temperature, p + n-like diodes were formed, but the suppression of electron injection was less efficient than at the higher temperatures. From simulations, assuming that the attractive electrical behavior is due to a monolayer of fixed negative charge at the interface, the concentration of holes needed to explain the I-V characteristics is estimated to be 1.4×10 11 cm -2 for 50 °C deposition and 1.1×1013 cm-2 for 400 °C.

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Chinese E-romance: Analyzing and visualizing 7.92 million Alibaba Valentine's day purchases

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arXiv 2020年

作者： Wang, Yongzhen Liu, Xiaozhong Ju, Yingnan Börner, Katy Lin, Jun Sun, Changlong Si, Luo Wise Lab Institute of Science of Science and S and T Management Faculty of Humanities and Social Sciences Dalian University of Technology Dalian116024 China Computer Science Department Worcester Polytechnic Institute WorcesterMA01609 United States School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States Alibaba Damo Academy Hangzhou310030 China

The days that precede Valentine's Day are characterized by extensive gift shopping activities all across the globe. In China, where much shopping takes place online, there has been an explosive growth in e-commerce sales during Valentine's Day over the recent years. This exploratory study investigates the extent to which each product category and each shopper group can exhibit romantic love within China's emarket throughout the 2 weeks leading up to 2019 Valentine's Day. Massive data from Alibaba, the biggest e-commerce retailer worldwide, are utilized to formulate an innovative romance index (RI) to quantitatively measure e-romantic values for products and shoppers. On this basis, millions of shoppers, along with their millions of products purchased around Valentine's Day, are analyzed as a case study to demonstrate their love consumption and romantic gift-giving. The results of the analysis are then illustrated to help understand Chinese e-romance based on the perspectives of different product categories and shopper groups. This empirical information visualization also contributes to improving the segmentation, targeting, and positioning of China's e-market for Valentine's Day. Copyright © 2020, The Authors. All rights reserved.

关键词： Electronic commerce

The role of asymptomatic individuals in the COVID-19 pandemic via complex networks

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arXiv 2020年

作者： Stella, Leonardo Martínez, Alejandro Pinel Bauso, Dario Colaneri, Patrizio Department of Computing College of Science & Engineering University of Derby Kedleston Road DE22 1GB United Kingdom Jan C. Willems Center for Systems and Control ENTEG Faculty of Science and Engineering University of Groningen Netherlands Dipartimento di Ingegneria University of Palermo Italy Dipartimento di Elettronica e Informazione Politecnico di Milano Italy

Background Recent seroprevalence studies have tried to estimate the real number of asymptomatic cases affected by COVID-19. It is of paramount importance to understand the impact of these infections in order to prevent a second wave. This study aims to model the interactions in the population by means of a complex network and to shed some light on the effectiveness of localised control measures in Italy in relation to the school opening in mid-September. Methods The formulation of an epidemiological predictive model is given: the advantage of using this model lies in that it discriminates between asymptomatic and symptomatic cases of COVID-19 as the interactions with these two categories of infected individuals are captured separately, allowing for a study on the impact of asymptomatic cases. This model is then extended to a structured nonhomogeneous version by means of the Watts-Strogatz complex network, which is adopted widely to model societal interactions as it holds the small world property. Finally, a case study on the situation in Italy is given: first the homogeneous model is used to compare the official data with the data of the recent seroprevalence study from Istat;second, in view of the return to school in mid-September, a study at regional level is conducted. Findings The proposed model captures an aspect of COVID-19 which is crucial in controlling the further spread of the contagion and in preventing a second wave: the interactions with undetected cases who have therefore not been isolated, mostly asymptomatic or paucisymptomatic, namely 1-2 symptoms without anosmia or ageusia, and their impact on latent infections. The evolution of the pandemic is well captured by model fitting with official data from the Protezione Civile, both at a national level as well as regional level. The case study provides insight on the potential effects of localised restrictions, without the coordination at a national level. The results of this study highlight the importanc

关键词： Complex networks

Single-Shot Secure Quantum Network Coding for General Multiple Unicast Network with Free One-Way Public Communication

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arXiv 2020年

作者： Kato, Go Owari, Masaki Hayashi, Masahito NTT Communication Science Laboratories NTT Corporation Japan Department of Computer Science Faculty of Informatics Shizuoka University Japan Graduate School of Mathematics Nagoya University Japan Shenzhen Institute for Quantum Science and Engineering Southern University of Science and Technology Shenzhen518055 China Quantum Computing Peng Cheng Laboratory Shenzhen518000 China Centre for Quantum Technologies National University of Singapore Singapore

It is natural in a quantum network system that multiple users intend to send their quantum message to their respective receivers, which is called a multiple unicast quantum network. We propose a canonical method to derive a secure quantum network code over a multiple unicast quantum network from a secure classical network code. Our code correctly transmits quantum states when there is no attack. It also guarantees the secrecy of the transmitted quantum state even with the existence of an attack when the attack satisfies a certain natural condition. In our security proof, the eavesdropper is allowed to modify wiretapped information dependently on the previously wiretapped messages. Our protocol guarantees the secrecy by utilizing one-way classical information transmission (public communication) in the same direction as the quantum network although the verification of quantum information transmission requires two-way classical communication. Our secure network code can be applied to several networks including the butterfly network. Copyright © 2020, The Authors. All rights reserved.

关键词： Network coding

Emerging app issue identification via online joint sentiment-topic tracing

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arXiv 2020年

作者： Gao, Cuiyun Zeng, Jichuan Wen, Zhiyuan Lo, David Xia, Xin King, Irwin Lyu, Michael R. Department of Computer Science and Engineering Chinese University of Hong Kong Shatin Hong Kong Department of Computing Hong Kong Polytechnic university Kowloon Hong Kong School of Information Systems Singapore Management University Singapore Faculty of Information Technology Monash University Australia

Millions of mobile apps are available in app stores, such as Apple’s App Store and Google Play. For a mobile app, it would be increasingly challenging to stand out from the enormous competitors and become prevalent among users. Good user experience and well-designed functionalities are the keys to a successful app. To achieve this, popular apps usually schedule their updates frequently. If we can capture the critical app issues faced by users in a timely and accurate manner, developers can make timely updates, and good user experience can be ensured. There exist prior studies on analyzing reviews for detecting emerging app issues. These studies are usually based on topic modeling or clustering techniques. However, the short-length characteristics and sentiment of user reviews have not been considered. In this paper, we propose a novel emerging issue detection approach named MERIT to take into consideration the two aforementioned characteristics. Specifically, we propose an Adaptive Online Biterm Sentiment-Topic (AOBST) model for jointly modeling topics and corresponding sentiments that takes into consideration app versions. Based on the AOBST model, we infer the topics negatively reflected in user reviews for one app version, and automatically interpret the meaning of the topics with most relevant phrases and sentences. Experiments on popular apps from Google Play and Apple’s App Store demonstrate the effectiveness of MERIT in identifying emerging app issues, improving the state-of-the-art method by 22.3% in terms of F1-score. In terms of efficiency, MERIT can return results within acceptable time. Copyright © 2020, The Authors. All rights reserved.

关键词： User profile

On the Philosophical, Cognitive and Mathematical Foundations of Symbiotic Autonomous Systems (SAS)

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arXiv 2021年

作者： Wang, Yingxu Karray, Fakhri Kwong, Sam Plataniotis, Konstantinos N. Leung, Henry Hou, Ming Tunstel, Edward Rudas, Imre J. Trajkovic, Ljiljana Kaynak, Okyay Kacprzyk, Janusz Zhou, Mengchu Smith, Michael H. Chen, Philip Patel, Shushma University of Calgary Canada FIEEE Dept. of Electrical & Computer Engineering University of Waterloo ON Canada FIEEE Department of Computer Science City University of Hong Kong Hong Kong FIEEE Dept. of Electrical & Computer Engineering University of Toronto ON Canada SMIEEE Toronto Research Centre DRDC Canada FIEEE Autonomous & Intelligent Systems Dept. Raytheon Technologies Research Center United States Óbuda University Budapest Hungary FIEEE School of Engineering Science Simon Fraser University BurnabyBC Canada FIEEE Bogazici University Bebek Istanbul Turkey FIEEE Systems Research Institute Polish Academy of Sciences Warsaw Poland FIEEE Dept. of Computer Science New Jersey Institute of Technology NJ United States SMIEEE Furaxa Inc. OrindaCA United States FIEEE School of Computer Science and Engineering South China University of Technology Guangzhou China FBCS Faculty of Computing Engineering and Media De Montfort University LeicesterLE1 9BH United Kingdom

Symbiotic Autonomous Systems (SAS) are advanced intelligent and cognitive systems exhibiting autonomous collective intelligence enabled by coherent symbiosis of human-machine interactions in hybrid societies. Basic research in the emerging field of SAS has triggered advanced general AI technologies functioning without human intervention or hybrid symbiotic systems synergizing humans and intelligent machines into coherent cognitive systems. This work presents a theoretical framework of SAS underpinned by the latest advances in intelligence, cognition, computer, and system sciences. SAS are characterized by the composition of autonomous and symbiotic systems that adopt bio-brain-social-inspired and heterogeneously synergized structures and autonomous behaviors. This paper explores their cognitive and mathematical foundations. The challenge to seamless human-machine interactions in a hybrid environment is addressed. SAS-based collective intelligence is explored in order to augment human capability by autonomous machine intelligence towards the next generation of general AI, autonomous computers, and trustworthy mission-critical intelligent systems. Emerging paradigms and engineering applications of SAS are elaborated via an autonomous knowledge learning system that symbiotically works between humans and cognitive robots. © 2021, CC BY-NC-ND.

关键词： Cognitive systems

Analysis Of Drug Data Mining With Clustering Technique Using K-Means Algorithm

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Journal of Physics: Conference Series 2021年第1期1908卷

作者： April Lia Hananto Priati Assiroj Bayu Priyatna Nurhayati Ahmad Fauzi Aviv Yuniar Rahman Shofa Shofiah Hilabi Faculty of Computing Universiti Teknologi Malaysia Skudai Johor Malaysia Faculty of Technology and Computer Science Universitas Buana Perjuangan Karawang Indonesia Master of Computer Science Universitas Budi Luhur Jakarta Indonesia Department of Informatics Engineering Universitas Widyagama Malang Indonesia

Data processing is very important in the development of information technology. Almost all fields of work have information data. Data can be used to help analysis in work. At present, health information data is very important to be processed in order to help medical personnel to make decisions. So that the results of the right decision to help patients. Lately, drug data has been misused for information eliminating a depressed patient without a doctor's prescription with a total data of 53766. The results shown are very large. So it requires very much attention from the government. As a result of the deviation of information and applied to the patient will result in death. Therefore, research needs to be conducted to group data on drug data. The source of research data is obtained from the UCI Machine Learning Repository Education website. The method proposed in this research is data mining. This solution can help researchers in the analysis of these data. One technique in data mining with clustering is using the K-means algorithm. The variables used are drug name, condition, useful count. The first research results can classify three categories consisting of using the highest drugs, using medium drugs and using lace drugs. Then the accuracy of the data is obtained with condition 99.45% valid records 53471, drug name 100% with valid records 53766, useful count 100% with valid records 53766.

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IO-VNBD: Inertial and Odometry Benchmark Dataset for Ground Vehicle Positioning

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arXiv 2020年

作者： Onyekpe, Uche Palade, Vasile Kanarachos, Stratis Szkolnik, Alicja Institute for Future Transport and Cities Coventry University Coventry United Kingdom Faculty of Engineering and Computing Coventry University Coventry United Kingdom Research Center for Data Science Coventry University Coventry United Kingdom Coventry University Gulson Road Coventry United Kingdom

Low-cost Inertial Navigation Sensors (INS) can be exploited for a reliable solution for tracking autonomous vehicles in the absence of GPS signals. However, position errors grow exponentially over time due to noises in the sensor measurements. The lack of a public and robust benchmark dataset has however hindered the advancement in the research, comparison and adoption of recent machine learning techniques such as deep learning techniques to learn the error in the INS for a more accurate positioning of the vehicle. In order to facilitate the benchmarking, fast development and evaluation of positioning algorithms, we therefore present the first of its kind large-scale and information-rich inertial and odometry focused public dataset called IO-VNBD (Inertial Odometry Vehicle Navigation Benchmark Dataset). The vehicle tracking dataset was recorded using a research vehicle equipped with ego-motion sensors on public roads in the United Kingdom, Nigeria, and France. The sensors include a GPS receiver, inertial navigation sensors, wheel-speed sensors amongst other sensors found in the car, as well as the inertial navigation sensors and GPS receiver in an Android smartphone sampling at 10 Hz. A diverse number of driving scenarios were captured such as traffic congestion, round-abouts, hard-braking, etc. on different road types (e.g. country roads, motorways, etc.) and with varying driving patterns. The dataset consists of a total driving time of about 40 h over 1,300 km for the vehicle extracted data and about 58 h over 4,400 km for the smartphone recorded data. We hope that this dataset will prove valuable in furthering research on the correlation between vehicle dynamics and dependable positioning estimation based on vehicle ego-motion sensors, as well as other related studies. © 2020, CC BY-NC-SA.

关键词： Global positioning system