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Digitizing a face-to-face group fatigue management program: Exploring the views of people with multiple sclerosis and health care professionals via consultation groups and interviews

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JMIR Formative Research 2019年第2期3卷 e10951-e10951页

作者： Thomas, Sarah Pulman, Andy Thomas, Peter Collard, Sarah Jiang, Nan Dogan, Huseyin Smith, Angela Davies Hourihan, Susan Roberts, Fiona Kersten, Paula Pretty, Keith Miller, Jessica K. Stanley, Kirsty Gay, Marie-Claire Bournemouth University Clinical Research Unit Faculty of Health and Social Sciences Bournemouth University Bournemouth United Kingdom Faculty of Science and Technology Department of Computing and Informatics Bournemouth University Bournemouth United Kingdom Bristol and Avon Multiple Sclerosis Service North Bristol National Health Service Trust Bristol United Kingdom Therapy and Rehabilitation Service National Hospital for Neurology and Neurosurgery University College London Hospitals London United Kingdom Long Term Conditions Team Walton Centre National Health Service Trust Liverpool United Kingdom School of Health Sciences University of Brighton Brighton United Kingdom Department of Sociology University of Cambridge Cambridge United Kingdom Dorset Multiple Sclerosis Service Poole Hospital National Health Service Foundation Trust Poole United Kingdom Paris Nanterre University Paris France

Background: Fatigue is one of the most common and debilitating symptoms of multiple sclerosis (MS) and is the main reason why people with MS stop working early. The MS Society in the United Kingdom funded a randomized controlled trial of FACETS-a face-to-face group-based fatigue management program for people with multiple sclerosis (pwMS)-developed by members of the research team. Given the favorable trial results and to help with implementation, the MS Society supported the design and printing of the FACETS manual and materials and the national delivery of FACETS training courses (designed by the research team) for health care professionals (HCPs). By 2015 more than 1500 pwMS had received the FACETS program, but it is not available in all areas and a face-to-face format may not be suitable for, or appeal to, everyone. For these reasons, the MS Society funded a consultation to explore an alternative Web-based model of service delivery. Objective: The aim of this study was to gather views about a Web-based model of service delivery from HCPs who had delivered FACETS and from pwMS who had attended FACETS. Methods: Telephone consultations were undertaken with FACETS-trained HCPs who had experience of delivering FACETS (n=8). Three face-to-face consultation groups were held with pwMS who had attended the FACETS program: London (n=4), Liverpool (n=4), and Bristol (n=7). The interviews and consultation groups were digitally recorded and transcribed. A thematic analysis was undertaken to identify key themes. Toward the end of the study, a roundtable meeting was held to discuss outcomes from the consultation with representatives from the MS Society, HCPs, and pwMS. Results: Key challenges and opportunities of designing and delivering an integrated Web-based version of FACETS and maintaining user engagement were identified across 7 themes (delivery, online delivery, design, group, engagement, interactivity, and HCP relationships). Particularly of interest were themes related

关键词： FACETS Fatigue Fatigue management Mobile health Multiple sclerosis Telemedicine

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Combined search in dwarf spheroidal galaxies for branon dark matter annihilation signatures with the MAGIC telescopes

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Journal of Cosmology and Astroparticle Physics 2025年第3期2025卷 020-020页

作者： Abe, S. Abhir, J. Abhishek, A. Acciari, V.A. Aguasca-Cabot, A. Agudo, I. Aniello, T. Ansoldi, S. Arbet Engels, A. Arcaro, C. Asano, K. Baack, D. Babić, A. Barres de Almeida, U. Barrio, J.A. Batković, I. Bautista, A. Baxter, J. Becerra González, J. Bednarek, W. Bernardini, E. Bernete, J. Berti, A. Besenrieder, J. Bigongiari, C. Biland, A. Blanch, O. Bonnoli, G. Bošnjak, Ž. Bronzini, E. Burelli, I. Campoy-Ordaz, A. Carosi, R. Carretero-Castrillo, M. Castro-Tirado, A.J. Cerasole, D. Ceribella, G. Chai, Y. Cifuentes, A. Colombo, E. Contreras, J.L. Cortina, J. Covino, S. Da Vela, P. Dazzi, F. De Angelis, A. De Lotto, B. de Menezes, R. Delgado, J. Delgado Mendez, C. Di Pierro, F. Di Tria, R. Di Venere, L. Dominis Prester, D. Donini, A. Dorner, D. Doro, M. Elsaesser, D. Escudero, J. Fariña, L. Fattorini, A. Foffano, L. Font, L. Fröse, S. Fukami, S. Fukazawa, Y. García López, R.J. Garczarczyk, M. Gasparyan, S. Gaug, M. Giesbrecht Paiva, J.G. Giglietto, N. Giordano, F. Gliwny, P. Gradetzke, T. Grau, R. Green, D. Green, J.G. Günther, P. Hadasch, D. Hahn, A. Hassan, T. Heckmann, L. Herrera Llorente, J. Hrupec, D. Imazawa, R. Ishio, K. Jiménez Martínez, I. Jormanainen, J. Kankkunen, S. Kayanoki, T. Kerszberg, D. Kluge, G.W. Kobayashi, Y. Kouch, P.M. Kubo, H. Kushida, J. Láinez, M. Lamastra, A. Leone, F. Lindfors, E. Lombardi, S. Longo, F. López-Coto, R. López-Moya, M. López-Oramas, A. Loporchio, S. Lorini, A. Lyard, E. Majumdar, P. Makariev, M. Maneva, G. Manganaro, M. Mangano, S. Mannheim, K. Mariotti, M. Martínez, M. Martínez-Chicharro, M. Mas-Aguilar, A. Mazin, D. Menchiari, S. Mender, S. Miceli, D. Miener, T. Miranda, J.M. Mirzoyan, R. Molero González, M. Molina, E. Mondal, H.A. Moralejo, A. Morcuende, D. Nakamori, T. Nanci, C. Neustroev, V. Nievas Rosillo, M. Nigro, C. Nikolić, L. Nishijima, K. Njoh Ekoume, T. Nozaki, S. Okumura, A. Otero-Santos, J. Paiano, S. Paneque, D. Paoletti, R. Paredes, J.M. Peresano, M. Persic, M. Pihet, M. Pirola, G. Podobnik, F. Prada Moroni, P.G. Prandini, E. Principe, G. Rhode, W. Ribó, M. R Japanese MAGIC Group Institute for Cosmic Ray Research (ICRR) The University of Tokyo Chiba Kashiwa 277-8582 Japan ETH Zürich Zürich CH-8093 Switzerland Università di Siena INFN Pisa Siena I-53100 Italy Institut de Física d'Altes Energies (IFAE) The Barcelona Institute of Science and Technology (BIST) Bellaterra Barcelona E-08193 Spain Universitat de Barcelona ICCUB IEEC-UB Barcelona E-08028 Spain Instituto de Astrofísica de Andalucía-CSIC Glorieta de la Astronomía s/n Granada 18008 Spain National Institute for Astrophysics (INAF) Rome I-00136 Italy Università di Udine INFN Trieste Udine I-33100 Italy Max-Planck-Institut für Physik Garching D-85748 Germany Università di Padova INFN Padova I-35131 Italy Technische Universität Dortmund Dortmund D-44221 Germany Croatian MAGIC Group University of Zagreb Faculty of Electrical Engineering and Computing (FER) Zagreb 10000 Croatia Centro Brasileiro de Pesquisas Físicas (CBPF) URCA RJ Rio de Janeiro 22290-180 Brazil IPARCOS Institute EMFTEL Department Universidad Complutense de Madrid Madrid E-28040 Spain Instituto de Astrofísica de Canarias Dpto. de Astrofísica Universidad de La Laguna La Laguna Tenerife E-38200 Spain University of Lodz Faculty of Physics and Applied Informatics Department of Astrophysics Lodz 90-236 Poland Centro de Investigaciones Energéticas Medioambientales y Tecnológicas Madrid E-28040 Spain Departament de Física CERES-IEEC Universitat Autònoma de Barcelona Bellaterra E-08193 Spain Università di Pisa INFN Pisa Pisa I-56126 Italy INFN MAGIC Group INFN Sezione di Bari Dipartimento Interateneo di Fisica dell'Università e del Politecnico di Bari Bari I-70125 Italy Department for Physics and Technology University of Bergen Norway INFN MAGIC Group INFN Sezione di Torino Università degli Studi di Torino Torino I-10125 Italy Croatian MAGIC Group University of Rijeka Faculty of Physics Rijeka 51000 Croatia Universität Würzburg Würzburg D-97074 Germany Japanes

Massive brane fluctuations, called branons, behave as weakly interacting massive particles, which is one of the most favored class of candidates to fulfill the role of the dark matter (DM), an elusive kind of matter beyond the Standard Model. We present a multi-target search in dwarf spheroidal galaxies for branon DM annihilation signatures with a total exposure of 354 hours with the ground-based gamma-ray telescope system MAGIC. This search led to the most constraining limits on branon DM in the sub-TeV and multi-TeV DM mass range. Our most stringent limit on the thermally-averaged annihilation cross-section (at 95% confidence level) corresponds to ⟨σv⟩ ≃ 1.9 × 10^-24 cm³s^-1 at a branon mass of ∼ 1.5 TeV. © 2025 The Author(s)

关键词： cosmology of theories beyond the SM dark matter experiments gamma ray experiments

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Predicting breast tumor proliferation from whole-slide images: The TUPAC16 challenge

arXiv

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arXiv 2018年

作者： Veta, Mitko Heng, Yujing J. Stathonikos, Nikolas Bejnordi, Babak Ehteshami Beca, Francisco Wollmann, Thomas Rohr, Karl Shah, Manan A. Wang, Dayong Rousson, Mikael Hedlund, Martin Tellez, David Ciompi, Francesco Zerhouni, Erwan Lanyi, David Viana, Matheus Kovalev, Vassili Liauchuk, Vitali Phoulady, Hady Ahmady Qaiser, Talha Graham, Simon Rajpoot, Nasir Sjöblom, Erik Molin, Jesper Paeng, Kyunghyun Hwang, Sangheum Park, Sunggyun Jia, Zhipeng Chang, Eric I-Chao Xu, Yan Beck, Andrew H. Van Diest, Paul J. Pluim, Josien P.W. Medical Image Analysis Group Eindhoven University of Technology Eindhoven Netherlands Department of Pathology Beth Israel Deaconess Medical Center Harvard Medical School BostonMA United States Department of Pathology University Medical Center Utrecht Utrecht Netherlands Diagnostic Image Analysis Group Radboud University Medical Center Nijmegen Netherlands Department of Pathology Stanford University School of Medicine Biomedical Computer Vision Group University of Heidelberg BIOQUANT IPMB DKFZ Heidelberg Germany Harker School San Jose United States ContextVision AB Linköping Sweden Foundations of Cognitive Computing IBM Research Zürich Rüschlikon Switzerland Visual Analytics and Insights IBM Research Brazil Saõ Paulo Brazil Biomedical Image Analysis Department United Institute of Informatics Minsk Belarus Department of Computer Science and Engineering University of South Florida TampaFL United States Department of Computer Science University of Warwick Warwick United Kingdom Research Sectra Linköping Sweden Lunit Inc. Seoul Korea Republic of Institute for Interdisciplinary Information Sciences Tsinghua University Beijing China Microsoft Research Beijing China Biology and Medicine Engineering Beihang University Beijing China IBM Research Brazil

Tumor proliferation is an important biomarker indicative of the prognosis of breast cancer patients. Assessment of tumor proliferation in a clinical setting is a highly subjective and labor-intensive task. Previous efforts to automate tumor proliferation assessment by image analysis only focused on mitosis detection in predefined tumor regions. However, in a realworld scenario, automatic mitosis detection should be performed in whole-slide images (WSIs) and an automatic method should be able to produce a tumor proliferation score given a WSI as input. To address this, we organized the TUmor Proliferation Assessment Challenge 2016 (TUPAC16) on prediction of tumor proliferation scores from WSIs. The challenge dataset consisted of 500 training and 321 testing breast cancer histopathology WSIs. In order to ensure fair and independent evaluation, only the ground truth for the training dataset was provided to the challenge participants. The first task of the challenge was to predict mitotic scores, i.e., to reproduce the manual method of assessing tumor proliferation by a pathologist. The second task was to predict the gene expression based PAM50 proliferation scores from the WSI. The best performing automatic method for the first task achieved a quadratic-weighted Cohen's kappa score of κ = 0.567, 95% CI [0.464, 0.671] between the predicted scores and the ground truth. For the second task, the predictions of the top method had a Spearman's correlation coefficient of r = 0.617, 95% CI [0.581 0.651] with the ground truth. This was the first comparison study that investigated tumor proliferation assessment from WSIs. The achieved results are promising given the difficulty of the tasks and weakly-labeled nature of the ground truth. However, further research is needed to improve the practical utility of image analysis methods for this task. Copyright © 2018, The Authors. All rights reserved.

关键词： Diseases

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Accuracy and Convergence Analysis of uFA-FastSLAM for Robot and Landmarks Position Estimation

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Journal of Physics: Conference Series 2021年第1期2129卷

作者： R J Musridho H Hasan H Haron D Gusman M A Mohammad Department of Informatics Engineering Faculty of Engineering Universitas Pahlawan Tuanku Tambusai Bangkinang Kota 28412 Indonesia. School of Computing Faculty of Engineering Universiti Teknologi Malaysia Johor Bahru 81310 Malaysia. Applied Industrial Analytics Research Group Johor Bahru 81310 Malaysia. iKohza Software Engineering for Industrial Revolution Malaysia-Japan International Institute of Technology (MJIIT) Universiti Teknologi Malaysia Kuala Lumpur 54100 Malaysia.

In autonomous mobile robots, Simultaneous Localization and Mapping (SLAM) is a demanding and vital topic. One of two primary solutions of SLAM problem is FastSLAM. In terms of accuracy and convergence, FastSLAM is known to degenerate over time. Previous work has hybridized FastSLAM with a modified Firefly Algorithm (FA), called unranked Firefly Algorithm (uFA), to optimize the accuracy and convergence of the robot and landmarks position estimation. However, it has not shown the performance of the accuracy and convergence. Therefore, this work is done to present both mentioned performances of FastSLAM and uFA-FastSLAM to see which one is better. The result of the experiment shows that uFA-FastSLAM has successfully improved the accuracy (in other words, reduced estimation error) and the convergence consistency of FastSLAM. The proposed uFA-FastSLAM is superior compared to conventional FastSLAM in estimation of landmarks position and robot position with 3.30 percent and 7.83 percent in terms of accuracy model respectively. Furthermore, the proposed uFA-FastSLAM also exhibits better performances compared to FastSLAM in terms of convergence consistency by 93.49 percent and 94.20 percent for estimation of landmarks position and robot position respectively.

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Modern computing: Vision and Challenges

arXiv

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arXiv 2024年

作者： Gill, Sukhpal Singh Wu, Huaming Patros, Panos Ottaviani, Carlo Arora, Priyansh Pujol, Victor Casamayor Haunschild, David Parlikad, Ajith Kumar Cetinkaya, Oktay Lutfiyya, Hanan Stankovski, Vlado Li, Ruidong Ding, Yuemin Qadir, Junaid Abraham, Ajith Ghosh, Soumya K. Song, Houbing Herbert Sakellariou, Rizos Rana, Omer Rodrigues, Joel J.P.C. Kanhere, Salil S. Dustdar, Schahram Uhlig, Steve Ramamohanarao, Kotagiri Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Center for Applied Mathematics Tianjin University Tianjin China Raygun Performance Monitoring Wellington New Zealand Department of Computer Science York Centre for Quantum Technologies University of York York United Kingdom Microsoft Schiphol Netherlands Distributed Systems Group Vienna University of Technology Vienna Austria Detecon International GmbH Munich Germany Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Department of Engineering Science University of Oxford Oxford United Kingdom Department of Computer Science University of Western Ontario London Canada Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Institute of Science and Engineering Kanazawa University Japan Tecnun School of Engineering University of Navarra Spain Department of Computer Science and Engineering College of Engineering Qatar University Doha Qatar Bennett University Greater Noida India Machine Intelligence Research Labs AuburnWA United States Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Baltimore United States Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Department of Computer Science College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia COPELABS Lusófona University Lisbon Portugal Sydney Australia The University of Melbourne Victoria Australia Laboratory School of Computing and Information Systems The University of Melbourne Australia

Over the past six decades, the computing systems field has experienced significant transformations, profoundly impacting society with transformational developments, such as the Internet and the commodification of computing. Underpinned by technological advancements, computer systems, far from being static, have been continuously evolving and adapting to cover multifaceted societal niches. This has led to new paradigms such as cloud, fog, edge computing, and the Internet of Things (IoT), which offer fresh economic and creative opportunities. Nevertheless, this rapid change poses complex research challenges, especially in maximizing potential and enhancing functionality. As such, to maintain an economical level of performance that meets ever-tighter requirements, one must understand the drivers of new model emergence and expansion, and how contemporary challenges differ from past ones. To that end, this article investigates and assesses the factors influencing the evolution of computing systems, covering established systems and architectures as well as newer developments, such as serverless computing, quantum computing, and on-device AI on edge devices. Trends emerge when one traces technological trajectory, which includes the rapid obsolescence of frameworks due to business and technical constraints, a move towards specialized systems and models, and varying approaches to centralized and decentralized control. This comprehensive review of modern computing systems looks ahead to the future of research in the field, highlighting key challenges and emerging trends, and underscoring their importance in cost-effectively driving technological progress. © 2024, CC BY.

关键词： Obsolescence

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Determination of risk factors influencing binge eating behaviour among university students

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AIP Conference Proceedings 2024年第1期2895卷

作者： Shaidatul Akma Huzid Nurain Ibrahim Zalina Zahid Siti Aida Sheikh Hussin Nor Azura Md Ghani Ali Selamat FGV Prodata Systems Sdn Bhd Wisma FGV Jalan Raja Laut 50350 Kuala Lumpur Malaysia College of Computing Informatics and Media Universiti Teknologi MARA 40450 Shah Alam Selangor Malaysia Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Kompleks Al-Khawarizmi Universiti Teknologi MARA 40450 Shah Alam Selangor Malaysia Malaysia-Japan International International Institute of Technology (MJIIT) Universiti Teknologi Malaysia Kuala Lumpur 54100 Malaysia Media and Games Center of Excellence (MagicX) Faculty of Engineering School of Computing Universiti Teknologi Malaysia Johor Baharu 81310 Malaysia

In Malaysia, binge eating is a significant issue because free food is so readily available. As a result, many Malaysians tend to overeat and have binge eating disorder symptoms. Teenagers and university students are typically affected. There are limited studies on binge eating behaviour in Malaysia, especially among university students. Therefore, Fisher Exacts Test was used in this study’s initial attempt to determine the relationship between the factors and binge eating behaviour. The Ordinal Logistic regression model was then used in this study to identify factors that significantly influence student binge eating behaviours. The two-stage stratified sampling method was used for selecting the sample, involving 339 undergraduate and postgraduate students from Faculty of Computer and Mathematical Sciences (FSKM) at UiTM Shah Alam. The questionnaire was distributed and analysed using Fisher’s Exact test and Ordinal Logistic Regression. Fisher’s Exact test revealed that binge eating levels are associated with age, depression, anxiety, physical appearance, and educations among FSKM students. According to Ordinal logistic regression, only age and physical appearance are significantly affecting binge eating levels. In conclusion, the teenager students are more prone to show severe binge eating behaviours. Additionally, those who are very self-conscious about their bodies are more prone to experience severe binge eating.

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Packaging, containerization, and virtualization of computational omics methods: Advances, challenges, and opportunities

arXiv

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arXiv 2022年

作者： Alser, Mohammed Waymost, Sharon Ayyala, Ram Lawlor, Brendan Abdill, Richard J. Rajkumar, Neha LaPierre, Nathan Brito, Jaqueline Ribeiro-Dos-Santos, André M. Firtina, Can Almadhoun, Nour Sarwal, Varuni Eskin, Eleazar Hu, Qiyang Strong, Derek Kim, Byoung-Do Abedalthagafi, Malak S. Mutlu, Onur Mangul, Serghei Department of Information Technology and Electrical Engineering ETH Zürich Zürich8092 Switzerland Department of Computer Science UCLA Los AngelesCA90095 United States Department of Translational Biomedical Informatics University of Southern California Los AngelesCA90089 United States Department of Clinical Pharmacy University of Southern California Los AngelesCA90089 United States Department of Computer Science Munster Technological University CorkT12 P928 Ireland Department of Genetics Cell Biology and Development University of Minnesota MinneapolisMN55417 United States Department of Bioengineering UCLA Los AngelesCA90095 United States Institute for Systems Genetics NYU Grossman School of Medicine New YorkNY10016 United States Department of Computational Medicine University of California Los AngelesCA90095 United States Department of Human Genetics University of California Los AngelesCA90095 United States Office of Advanced Research Computing UCLA Los AngelesCA90095 United States Center for Advanced Research Computing University of Southern California Los AngelesCA90089 United States Genomics Research Department Saudi Human Genome Project King Fahad Medical City King Abdulaziz City for Science and Technology Riyadh Saudi Arabia King Salman Center for Disability Research Riyadh12512 Saudi Arabia Riyadh Saudi Arabia

Omics software tools have reshaped the landscape of modern biology and become an essential component of biomedical research. The increasing dependence of biomedical scientists on these powerful tools creates a need for easier installation and greater usability. Packaging, virtualization, and containerization are different approaches to satisfy this need by wrapping omics tools in additional software that makes the omics tools easier to install and use. Here, we systematically review practices across prominent packaging, virtualization, and containerization platforms. We outline the challenges, advantages, and limitations of each approach and some of the most widely used platforms from the perspectives of users, software developers, and system administrators. We also propose principles to make packaging, virtualization, and containerization of omics software more sustainable and robust to increase the reproducibility of biomedical and life science research. © 2022, CC BY.

关键词： Containers

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Benchmarking the CoW with the TopCoW Challenge: Topology-Aware Anatomical Segmentation of the Circle of Willis for CTA and MRA

arXiv

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arXiv 2023年

作者： Yang, Kaiyuan Musio, Fabio Ma, Yihui Juchler, Norman Paetzold, Johannes C. Al-Maskari, Rami Höher, Luciano Li, Hongwei Bran Hamamci, Ibrahim Ethem Sekuboyina, Anjany Shit, Suprosanna Huang, Houjing Prabhakar, Chinmay de la Rosa, Ezequiel Waldmannstetter, Diana Kofler, Florian Navarro, Fernando Menten, Martin Ezhov, Ivan Rueckert, Daniel Vos, Iris Ruigrok, Ynte Velthuis, Birgitta Kuijf, Hugo Hämmerli, Julien Wurster, Catherine Bijlenga, Philippe Westphal, Laura Bisschop, Jeroen Colombo, Elisa Baazaoui, Hakim Makmur, Andrew Hallinan, James Wiestler, Bene Kirschke, Jan S. Wiest, Roland Montagnon, Emmanuel Letourneau-Guillon, Laurent Galdran, Adrian Galati, Francesco Falcetta, Daniele Zuluaga, Maria A. Lin, Chaolong Zhao, Haoran Zhang, Zehan Ra, Sinyoung Hwang, Jongyun Park, Hyunjin Chen, Junqiang Wodzinski, Marek Müller, Henning Shi, Pengcheng Liu, Wei Ma, Ting Yalçin, Cansu Hamadache, Rachika E. Salvi, Joaquim Llado, Xavier Estrada, Uma Maria Lal-Trehan Abramova, Valeriia Giancardo, Luca Oliver, Arnau Liu, Jialu Huang, Haibin Cui, Yue Lin, Zehang Liu, Yusheng Zhu, Shunzhi Patel, Tatsat R. Tutino, Vincent M. Orouskhani, Maysam Wang, Huayu Mossa-Basha, Mahmud Zhu, Chengcheng Rokuss, Maximilian R. Kirchhoff, Yannick Disch, Nico Holzschuh, Julius Isensee, Fabian Maier-Hein, Klaus Sato, Yuki Hirsch, Sven Wegener, Susanne Menze, Bjoern Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Center for Computational Health Zurich University of Applied Sciences Zurich Switzerland Department of Neuroradiology University Hospital of Zurich Zurich Switzerland Department of Neurosurgery Zhongnan Hospital of Wuhan University Wuhan China Department of Computing Imperial College London London United Kingdom Helmholtz Center Munich Germany Athinoula A. Martinos Center for Biomedical Imaging Harvard Medical School Boston United States School of Medicine Technical University of Munich Munich Germany Department of Informatics Technical University of Munich Munich Germany Helmholtz AI Helmholtz Munich Munich Germany Image Sciences Institute UMC Utrecht Utrecht Netherlands Department of Neurology UMC Utrecht Utrecht Netherlands Department of Radiology UMC Utrecht Utrecht Netherlands Department of Clinical Neurosciences Division of Neurosurgery Geneva University Hospitals Geneva Switzerland Department of Neurology University Hospital of Zurich Zurich Switzerland Department of Physiology University of Toronto Toronto Canada Department of Neurosurgery University Hospital of Zurich Zurich Switzerland Department of Diagnostic Imaging National University Hospital Singapore Department of Diagnostic and Interventional Neuroradiology University Hospital Berne University of Berne Berne Switzerland Montreal Canada Universitat Pompeu Fabra Barcelona Spain EURECOM Biot France Institute of Medical Technology Peking University Health Science Center Beijing China Hangzhou Genlight Medtech Co. Ltd. Hangzhou China Department of Artificial Intelligence Sungkyunkwan University Seoul Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Seoul Korea Republic of Shanghai MediWorks Precision Instruments Co. Ltd. Shanghai China Institute of Informatics HES-SO Valais-Wallis Switzerland Department of Measurement and Electronics AGH University

The Circle of Willis (CoW) is an important network of arteries connecting major circulations of the brain. Its vascular architecture is believed to affect the risk, severity, and clinical outcome of serious neuro-vascular diseases. However, characterizing the highly variable CoW anatomy is still a manual and time-consuming expert task. The CoW is usually imaged by two angiographic imaging modalities, magnetic resonance angiography (MRA) and computed tomography angiography (CTA), but there exist limited public datasets with annotations on CoW anatomy, especially for CTA. Therefore we organized the TopCoW Challenge in 2023 with the release of an annotated CoW dataset. The TopCoW dataset was the first public dataset with voxel-level annotations for thirteen possible CoW vessel components, enabled by virtual-reality (VR) technology. It was also the first large dataset with paired MRA and CTA from the same patients. TopCoW challenge formalized the CoW characterization problem as a multiclass anatomical segmentation task with an emphasis on topological metrics. We invited submissions worldwide for the CoW segmentation task, which attracted over 140 registered participants from four continents. The top performing teams managed to segment many CoW components to Dice scores around 90%, but with lower scores for communicating arteries and rare variants. There were also topological mistakes for predictions with high Dice scores. Additional topological analysis revealed further areas for improvement in detecting certain CoW components and matching CoW variant topology accurately. TopCoW represented a first attempt at benchmarking the CoW anatomical segmentation task for MRA and CTA, both morphologically and topologically. © 2023, CC BY-NC-ND.

关键词： Large datasets

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The ACROBAT 2022 Challenge: Automatic Registration Of Breast Cancer Tissue

arXiv

引用

arXiv 2023年

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 EdmontonAB Canada 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 establish the current state-of-the-art in WSI registration and guide researchers in selecting and developing methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Tissue

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Approximate amplitude encoding in shallow parameterized quantum circuits and its application to financial market indicators

引用

Physical Review Research 2022年第2期4卷 023136-023136页

作者： Kouhei Nakaji Shumpei Uno Yohichi Suzuki Rudy Raymond Tamiya Onodera Tomoki Tanaka Hiroyuki Tezuka Naoki Mitsuda Naoki Yamamoto Graduate School of Science and Technology Keio University 3-14-1 Hiyoshi Kohoku-ku Yokohama Kanagawa 223- 8522 Japan Quantum Computing Center Keio University 3-14-1 Hiyoshi Kohoku-ku Yokohama Kanagawa 223-8522 Japan Mizuho Research & Technologies Ltd. 2-3 Kanda-Nishikicho Chiyoda-ku Tokyo 101-8443 Japan IBM Quantum IBM Research-Tokyo 19-21 Nihonbashi Hakozaki-cho Chuo-ku Tokyo 103-8510 Japan Mitsubishi UFJ Financial Group Inc. and MUFG Bank Ltd. 2-7-1 Marunouchi Chiyoda-ku Tokyo 100-8388 Japan Sony Group Corporation 1-7-1 Konan Minato-ku Tokyo 108-0075 Japan Sumitomo Mitsui Trust Bank Ltd. 1-4-1 Marunouchi Chiyoda-ku Tokyo 100-8233 Japan Department of Applied Physics and Physico-Informatics Keio University Hiyoshi 3-14-1 Kohoku-ku Yokohama 223-8522 Japan

Efficient methods for loading given classical data into quantum circuits are essential for various quantum algorithms. In this paper, we propose an algorithm called Approximate Amplitude Encoding that can effectively load all the components of a given real-valued data vector into the amplitude of quantum state, while the previous proposal can load only the absolute values of those components. The key of our algorithm is to variationally train a shallow parameterized quantum circuit, using the results of two types of measurement: the standard computational-basis measurement plus the measurement in the Hadamard-transformed basis, introduced in order to handle the sign of the data components. The variational algorithm changes the circuit parameters so as to minimize the sum of two costs corresponding to those two measurement basis, both of which are given by the efficiently computable maximum mean discrepancy. We also consider the problem of constructing the singular value decomposition entropy via the stock market data set to give a financial market indicator; a quantum algorithm (the variational singular value decomposition algorithm) is known to produce a solution faster than classical, which yet requires the sign-dependent amplitude encoding. We demonstrate, with an in-depth numerical analysis, that our algorithm realizes loading of time series of real stock prices on quantum state with small approximation error, and thereby it enables constructing an indicator of the financial market based on the stock prices.

关键词： Machine learning Quantum algorithms Quantum gates

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