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The ACROBAT 2022 challenge: Automatic registration of breast cancer tissue

Medical Image Analysis 2024年 97卷 103257页

作者： Weitz, Philippe Valkonen, Masi Solorzano, Leslie Carr, Circe Kartasalo, Kimmo Boissin, Constance Koivukoski, Sonja Kuusela, Aino Rasic, Dusan Feng, Yanbo Pouplier, Sandra Sinius Sharma, Abhinav Eriksson, Kajsa Ledesma Robertson, Stephanie Marzahl, Christian Gatenbee, Chandler D. Anderson, Alexander R.A. Wodzinski, Marek Jurgas, Artur Marini, Niccolò Atzori, Manfredo Müller, Henning Budelmann, Daniel Weiss, Nick Heldmann, Stefan Lotz, Johannes Wolterink, Jelmer M. De Santi, Bruno Patil, Abhijeet Sethi, Amit Kondo, Satoshi Kasai, Satoshi Hirasawa, Kousuke Farrokh, Mahtab Kumar, Neeraj Greiner, Russell Latonen, Leena Laenkholm, Anne-Vibeke Hartman, Johan Ruusuvuori, Pekka Rantalainen, Mattias Department of Medical Epidemiology and Biostatistics Karolinska Insitutet Stockholm Sweden Institute of Biomedicine University of Turku Turku Finland Institute of Biomedicine University of Eastern Finland Kuopio Finland Department of Surgical Pathology Zealand University Hospital Roskilde Denmark Department of Oncology and Pathology Karolinska Institutet Stockholm Sweden Gestalt Diagnostics Spokane United States Department of Integrated Mathematical Oncology Moffitt Cancer Center Tampa United States Informatics Institute University of Applied Sciences Western Switzerland Switzerland Department of Measurement and Electronics AGH University of Kraków Poland Department of Computer Science University of Geneva Geneva Switzerland Department of Neuroscience University of Padova Italy Medical Faculty University of Geneva Switzerland Fraunhofer Institute for Digital Medicine MEVIS Lübeck Germany Department of Applied Mathematics Technical Medical Centre University of Twente Enschede Netherlands Multimodality Medical Imaging Technical Medical Centre University of Twente Enschede Netherlands Department of Electrical Engineering Indian Institute of Technology Bombay India Graduate School of Engineering Muroran Institute of Technology Hokkaido Japan Faculty of Medical Technology Niigata University of Health and Welfare Niigata Japan FORXAI Business Operations Konica Minolta Inc. Osaka Japan Department of Computing Science University of Alberta EdmontonAlberta United States Alberta Machine Intelligence Institute Edmonton Canada MedTechLabs BioClinicum Karolinska University Hospital Stockholm Sweden Faculty of Medicine and Health Technology Tampere University Tampere Finland

The alignment of tissue between histopathological whole-slide-images (WSI) is crucial for research and clinical applications. Advances in computing, deep learning, and availability of large WSI datasets have revolutionised WSI analysis. Therefore, the current state-of-the-art in WSI registration is unclear. To address this, we conducted the ACROBAT challenge, based on the largest WSI registration dataset to date, including 4,212 WSIs from 1,152 breast cancer patients. The challenge objective was to align WSIs of tissue that was stained with routine diagnostic immunohistochemistry to its H&E-stained counterpart. We compare the performance of eight WSI registration algorithms, including an investigation of the impact of different WSI properties and clinical covariates. We find that conceptually distinct WSI registration methods can lead to highly accurate registration performances and identify covariates that impact performances across methods. These results provide a comparison of the performance of current WSI registration methods and guide researchers in selecting and developing methods. © 2024

关键词： Tissue

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Approximate complex amplitude encoding algorithm and its application to data classification problems

arXiv

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arXiv 2022年

作者： Mitsuda, Naoki Ichimura, Tatsuhiro Nakaji, Kouhei Suzuki, Yohichi Tanaka, Tomoki Raymond, Rudy Tezuka, Hiroyuki Onodera, Tamiya Yamamoto, Naoki Sumitomo Mitsui Trust Bank Ltd. 1-4-1 Marunouchi Chiyoda-ku Tokyo100-8233 Japan Quantum Computing Center Keio University 3-14-1 Hiyoshi Kohoku-ku Kanagawa Yokohama223-8522 Japan Department of Chemistry University of Toronto TorontoONM5G 1Z8 Canada 1-1-1 Umezono Ibaraki Tsukuba305-8568 Japan Mitsubishi UFJ Financial Group Inc. MUFG Bank Ltd. 2-7-1 Marunouchi Chiyoda-ku Tokyo100-8388 Japan IBM Quantum IBM Japan 19-21 Nihonbashi Hakozaki-cho Chuo-ku Tokyo103-8510 Japan Department of Computer Science University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-0033 Japan Advanced Research Laboratory Technology Infrastructure Center Sony Group Corporation Technology Platform 1-7-1 Konan Minato-ku Tokyo108-0075 Japan Graduate School of Science and Technology Keio University 3-14-1 Hiyoshi Kohoku-ku Kanagawa Yokohama223- 8522 Japan Department of Applied Physics and Physico-Informatics Keio University Hiyoshi 3-14-1 Kohoku-ku Kanagawa Yokohama223-8522 Japan

Quantum computing has a potential to accelerate data processing efficiency, especially in machine learning, by exploiting special features such as the quantum interference. The major challenge in this application is that, in general, the task of loading a classical data vector into a quantum state requires an exponential number of quantum gates. The approximate amplitude encoding (AAE) method, which uses a variational means to approximately load a given real-valued data vector into the amplitude of a quantum state, was recently proposed as a general approach to this problem mainly for near-term devices. However, AAE cannot load a complex-valued data vector, which narrows its application range. In this work, we extend AAE so that it can handle a complex-valued data vector. The key idea is to employ the fidelity distance as a cost function for optimizing a parametrized quantum circuit, where the classical shadow technique is used to efficiently estimate the fidelity and its gradient. We apply this algorithm to realize the complex-valued-kernel binary classifier called the compact Hadamard classifier, and then we present a numerical experiment showing that it enables classification of the Iris dataset and credit card fraud detection. Copyright © 2022, The Authors. All rights reserved.

关键词： Cost functions

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Placental Vessel Segmentation and Registration in Fetoscopy: Literature Review and MICCAI FetReg2021 Challenge Findings

arXiv

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arXiv 2022年

作者： Bano, Sophia Casella, Alessandro Vasconcelos, Francisco Qayyum, Abdul Benzinou, Abdesslam Mazher, Moona Meriaudeau, Fabrice Lena, Chiara Cintorrino, Ilaria Anita De Paolis, Gaia Romana Biagioli, Jessica Grechishnikova, Daria Jiao, Jing Bai, Bizhe Qiao, Yanyan Bhattarai, Binod Gaire, Rebati Raman Subedi, Ronast Vazquez, Eduard Plotka, Szymon Lisowska, Aneta Sitek, Arkadiusz Attilakos, George Wimalasundera, Ruwan David, Anna L. Paladini, Dario Deprest, Jan De Momi, Elena Mattos, Leonardo S. Moccia, Sara Stoyanov, Danail Department of Computer Science University College London United Kingdom Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Italy The BioRobotics Institute Department of Excellence in Robotics and AI Scuola Superiore Sant'Anna Italy Fetal Medicine Unit Elizabeth Garrett Anderson Wing University College London Hospital United Kingdom EGA Institute for Women's Health Faculty of Population Health Sciences University College London United Kingdom Department of Development and Regeneration University Hospital Leuven Belgium Department of Fetal and Perinatal Medicine Istituto Giannina Gaslini Italy ENIB UMR CNRS 6285 LabSTICC 29238 France Department of Computer Engineering and Mathematics University Rovira i Virgili Spain ImViA Laboratory University of Bourgogne Franche-Comté France Physics Department Lomonosov Moscow State University Russia Fudan University China Medical Computer Vision and Robotics Group Department of Mathematical and Computational Sciences University of Toronto Canada Co. Ltd China NepAL Applied Mathematics and Informatics Institute for Research Nepal Redev Technology United Kingdom Sano Center for Computational Medicine Poland Quantitative Healthcare Analysis Group Informatics Institute University of Amsterdam Amsterdam Netherlands Center for Advanced Medical Computing and Simulation Massachusetts General Hospital Harvard Medical School BostonMA United States

Fetoscopy laser photocoagulation is a widely adopted procedure for treating Twin-to-Twin Transfusion Syndrome (TTTS). The procedure involves photocoagulation pathological anastomoses to restore a physiological blood exchange among twins. The procedure is particularly challenging, from the surgeon's side, due to the limited field of view, poor manoeuvrability of the fetoscope, poor visibility due to amniotic fluid turbidity, and variability in illumination. These challenges may lead to increased surgery time and incomplete ablation of pathological anastomoses, resulting in persistent TTTS. Computer-assisted intervention (CAI) can provide TTTS surgeons with decision support and context awareness by identifying key structures in the scene and expanding the fetoscopic field of view through video mosaicking. Research in this domain has been hampered by the lack of high-quality data to design, develop and test CAI algorithms. Through the Fetoscopic Placental Vessel Segmentation and Registration (FetReg2021) challenge, which was organized as part of the MICCAI2021 Endoscopic Vision (EndoVis) challenge, we released the first large-scale multi-center TTTS dataset for the development of generalized and robust semantic segmentation and video mosaicking algorithms with a focus on creating drift-free mosaics from long duration fetoscopy videos. For this challenge, we released a dataset of 2060 images, pixel-annotated for vessels, tool, fetus and background classes, from 18 in-vivo TTTS fetoscopy procedures and 18 short video clips of an average length of 411 frames for developing placental scene segmentation and frame registration for mosaicking techniques. Seven teams participated in this challenge and their model performance was assessed on an unseen test dataset of 658 pixel-annotated images from 6 fetoscopic procedures and 6 short clips. The challenge provided an opportunity for creating generalized solutions for fetoscopic scene understanding and mosaicking. In this paper,

关键词： Pixels

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Investigation of the hyperchaos and control in the fractional order financial system with profit margin

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Partial Differential Equations in Applied Mathematics 2024年 9卷

作者： Johansyah, Muhamad Deni Sambas, Aceng Qureshi, Sania Zheng, Song Abed-Elhameed, Tarek M. Vaidyanathan, Sundarapandian Sulaiman, Ibrahim Mohammed Department of Mathematics Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Campus Besut Terengganu 22200 Malaysia Department of Mechanical Engineering Universitas Muhammadiyah Tasikmalaya Tamansari Gobras Tasikmalaya 46196 Indonesia Department of Computer Science and Mathematics Lebanese American University Beirut 13-5053 Lebanon Department of Mathematics Near East University Mersin 99138 Turkey Department of Basic Sciences and Related Studies Mehran University of Engineering and Technology Jamshoro 72060 Pakistan School of Data Science Zhejiang University of Finance and Economics Hangzhou 310018 China Institute of Mathematics and Interdisciplinary Sciences Zhejiang University of Finance and Economics 310018 China Department of Mathematics Faculty of Science Assiut University Assiut 71516 Egypt Centre for Control Systems Vel Tech University Avadi Tamil Nadu Chennai 600062 India Institute of Strategic Industrial Decision Modelling School of Quantitative Sciences Universiti Utara Malaysia Sintok Bukit Kayu Hitam Kedah 06010 Malaysia Faculty of Education and Arts Sohar University Sohar 311 Oman

This research study proposes a novel hyperchaotic finance system with profit margin and then utilizes the Adomian Decomposition Method (ADM) to tackle the solution of the associated Caputo-derivative based fractional-order hyperchaotic finance system with profit margin. Numerical simulations and analyses, such as the evaluation of Lyapunov exponents (LE), the creation of bifurcation diagrams (BD), the complexity analysis (CA), and the 0-1 test, are used to get a full picture of the system. The results of our study demonstrate the occurrence of periodic, chaotic, and hyperchaotic dynamics in the system. Furthermore, the overall criteria for the existence and the uniqueness of the exact solutions for Caputo fractional-order models are presented. In addition, we present a control methodology for the fractional-order hyperchaotic financial system utilizing the Laplace transform and linear feedback control. Significantly, our research showcases a noteworthy correlation between the analytical results and numerical simulations, emphasizing the soundness and effectiveness of our methodology. © 2024 The Author(s)

关键词： Chaotic system Financial system Fractional order Hyperchaos

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RETRACTED: Affirmative data analytics based data processing method for 6G wireless network applications

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Transactions on Emerging Telecommunications Technologies 2022年第10期35卷

作者： Muath Jarrah Ahmed Abu-Khadrah Hamza Alrababah Abdul Syukor Bin Mohamad Jaya Zakaria N. M. Alqattan School of Information Technology Skyline University College Sharjah UAE Department of Computer Science University Malaysia of Computer Science & Engineering (UNIMY) Cyberjaya Selangor Malaysia College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia Fakulti Teknologi Maklumat dan Komunikasi University Teknikal Malaysia Melaka Melaka Malaysia National Advanced IPv6 Centre of Excellence (NAv6) University Sains Malaysia Penang Malaysia Department of Computer Science Kulliyyah of Information and Communication Technology International Islamic University Malaysia (IIUM) Kuala Lumpur Malaysia

Sixth generation (6G) wireless network infrastructure makes use of terahertz communication interfaces and latency service. The growth of real-time applications and service support increases the data handling capacity and processing requirements. The data processing rate must be comparatively high with the available resources to meet the users' quality of service (QoS) requirements. This article proposes an affirmative data analytics (ADA) method to improve data processing consistency in 6G wireless networks. The consistency of the ADA method relies on the 6G service features in the service-rendering environment. The affirmation process is provided using support vector machine (SVM) learning to achieve consistency in handling diverse-attributed data. Then the attribute and association between data and services are achieved with best-fit processing time and minimum complexity. The performance of the proposed ADA is verified for heterogeneous service applications in a wireless network using the metrics analysis time (723.629 ms), complexity (11.311%), and response time (1.034 s).

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Scientific discovery in the age of artificial intelligence

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NATURE 2023年第7978期621卷 E33-E33页

作者： Wang, Hanchen Fu, Tianfan Du, Yuanqi Gao, Wenhao Huang, Kexin Liu, Ziming Chandak, Payal Liu, Shengchao Van Katwyk, Peter Deac, Andreea Anandkumar, Anima Bergen, Karianne Gomes, Carla P. Ho, Shirley Kohli, Pushmeet Lasenby, Joan Leskovec, Jure Liu, Tie-Yan Manrai, Arjun Marks, Debora Ramsundar, Bharath Song, Le Sun, Jimeng Tang, Jian Velickovic, Petar Welling, Max Zhang, Linfeng Coley, Connor W. Bengio, Yoshua Zitnik, Marinka Department of Engineering University of Cambridge Cambridge UK Department of Computing and Mathematical Sciences California Institute of Technology Pasadena CA USA NVIDIA Santa Clara CA USA Department of Computational Science and Engineering Georgia Institute of Technology Atlanta GA USA Department of Computer Science Cornell University Ithaca NY USA Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Department of Computer Science Stanford University Stanford CA USA Department of Physics Massachusetts Institute of Technology Cambridge MA USA Harvard-MIT Program in Health Sciences and Technology Cambridge MA USA Mila – Quebec AI Institute Montreal Quebec Canada Université de Montréal Montreal Quebec Canada HEC Montréal Montreal Quebec Canada CIFAR AI Chair Toronto Ontario Canada Department of Earth Environmental and Planetary Sciences Brown University Providence RI USA Data Science Institute Brown University Providence RI USA Center for Computational Astrophysics Flatiron Institute New York NY USA Department of Astrophysical Sciences Princeton University Princeton NJ USA Department of Physics Carnegie Mellon University Pittsburgh PA USA Department of Physics and Center for Data Science New York University New York NY USA Google DeepMind London UK Department of Computer Science and Technology University of Cambridge Cambridge UK Microsoft Research Beijing China Department of Biomedical Informatics Harvard Medical School Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA Harvard Data Science Initiative Cambridge MA USA Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University Cambridge MA USA Department of Systems Biology Harvard Medical School Boston MA USA Deep Forest Sciences Palo Alto CA USA BioMap Beijing China Mo

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Disentangling high-order mechanisms and high-order behaviours in complex systems

arXiv

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arXiv 2022年

作者： Rosas, Fernando E. Mediano, Pedro A.M. Luppi, Andrea I. Varley, Thomas F. Lizier, Joseph T. Stramaglia, Sebastiano Jensen, Henrik J. Marinazzo, Daniele Data Science Institute Imperial College London United Kingdom Centre for Psychedelic Research Imperial College London United Kingdom Centre for Complexity Science Imperial College London United Kingdom Department of Psychology University of Cambridge United Kingdom Department of Psychology Queen Mary University of London United Kingdom University Division of Anaesthesia University of Cambridge United Kingdom Department of Clinical Neurosciences University of Cambridge United Kingdom Leverhulme Centre for the Future of Intelligence University of Cambridge United Kingdom Department of Psychological & Brain Sciences Indiana University BloomingtonIN United States School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Centre for Complex Systems Faculty of Engineering The University of Sydney Australia Dipartimento Interateneo di Fisica Università Degli Studi di Bari Aldo Moro Italy INFN Sezione di Bari Italy Department of Mathematics Imperial College London United Kingdom Institute of Innovative Research Tokyo Institute of Technology Japan Department of Data Analysis Ghent University Belgium

Battiston et al. (arXiv:2110.06023) provide a comprehensive overview of how investigations of complex systems should take into account interactions between more than two elements, which can be modelled by hypergraphs and studied via topological data analysis. Following a separate line of enquiry, a broad literature has developed information-theoretic tools to characterize high-order interdependencies from observed data. While these could seem to be competing approaches aiming to address the same question, in this correspondence we clarify that this is not the case, and that a complete account of higher-order phenomena needs to embrace both. Copyright © 2022, The Authors. All rights reserved.

关键词： Large scale systems

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Correction: VER-Net: a hybrid transfer learning model for lung cancer detection using CT scan images

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BMC medical imaging 2024年第1期24卷 128页

作者： Anindita Saha Shahid Mohammad Ganie Pijush Kanti Dutta Pramanik Rakesh Kumar Yadav Saurav Mallik Zhongming Zhao Department of Computing Science and Engineering IFTM University Moradabad Uttar Pradesh India. AI Research Centre Department of Analytics School of Business Woxsen University Hyderabad Telangana 502345 India. School of Computer Applications and Technology Galgotias University Greater Noida Uttar Pradesh 203201 India. pijushjld@yahoo.co.in. Department of Computer Science & Engineering MSOET Maharishi University of Information Technology Lucknow Uttar Pradesh India. Department of Environmental Health Harvard T. H. Chan School of Public Health Boston MA USA. Center for Precision Health McWilliams School of Biomedical Informatics The University of Texas Health Science Center at Houston Houston TX 77030 USA. zhongming.zhao@uth.tmc.edu.

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An iris segmentation technique using black hole search method and canny edge detector

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AIP Conference Proceedings 2024年第1期2867卷

作者： Sani Sufyan Bature Danlami Muktar Sapi’ee Jamel Ibrahim M. Sulaiman Sukono Sukono Aceng Sambas Department of Computer Science Yusuf Maitama Sule University Kano Nigeria Faculty of Computer Science and Information Technology Universiti Tun Hussien On Malaysia Johor Malaysia Institute of Strategic Industrial Decision Modelling (ISIDM) School of Quantitative Sciences Universiti Utara Malaysia UUM Sintok 06010 Kedah Malaysia Department of Mathematics Universitas Padjadjaran Sumedang Indonesia Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Campus Besut 22200 Terengganu Malaysia Department of Mechanical Engineering Universitas Muhammadiyah Tasikmalaya Tasikmalaya Indonesia

Biometric is the most fundamental and efficient technique used in terms of recognizing individuals uniquely. As we have two types of biometric; one on physical and the other in terms behavioral characteristics. Iris segmentation is a crucial stage in iris recognition systems since it determines the accuracy of all future phases and can be segmented using a variety of techniques. There are different iris segmentation techniques, but the most widely used ones are the circular Hough transform and Daugman’s algorithm which are commonly employed in commercial and research problems involving iris recognition systems. In iris recognition system, the time it took to detect the iris and the accuracy with which it was located are both important aspects. Due to low in terms of accuracy we have suggested a different approach of iris segmentation based on the black hole search method and canny edge detector in this research. The proposed approach is tested using CASIA and MMU Database iris images. This technique is effective in terms of the time it takes to detect an eye’s iris, as it directly utilizes intensity and shape. Also, we can detect the iris of an eye much faster than the standard technique with the help of image processing.

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RETRACTED ARTICLE: Utilizing Index-Based Periodic High Utility Mining to Study Frequent Itemsets

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Arabian Journal for Science and Engineering 2021年第3期48卷 4123-4123页

作者： Setiawan, Roy Le, Dac-Nhuong Rajan, Regin Subramani, Thirukumaran Sharma, Dilip Kumar Ponnam, Vidya Sagar Kumar, Kailash Akbar Batcha, Syed Musthafa Dadheech, Pankaj Sengan, Sudhakar Department Management Universitas Kristen Petra Jawa Timur Indonesia School of Computer Science/Institute of Research and Development Duy Tan University Danang Vietnam Department of Computer Science and Engineering Adhiyamaan College of Engineering Hosur India Department of Computer Science and Engineering KL University Vijayawada India Jaypee University of Engineering and Technology Guna India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India College of Computing and Informatics Saudi Electronic University Riyadh Kingdom of Saudi Arabia Department of Information Technology M. Kumarasamy College of Engineering Karur India Department of Computer Science and Engineering Swami Keshvanand Institute of Technology Management and Gramothan (SKIT) Jaipur India Department of Computer Science and Engineering PSN College of Engineering and Technology Tirunelveli India

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