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arXiv 2023年

作者： Luijken, Kim Morzywolek, Pawel van Amsterdam, Wouter Cinà, Giovanni Hoogland, Jeroen Keogh, Ruth Krijthe, Jesse Magliacane, Sara van Ommen, Thijs Peek, Niels Putter, Hein van Smeden, Maarten Sperrin, Matthew Wang, Junfeng Weir, Daniala Didelez, Vanessa van Geloven, Nan Department of Epidemiology Julius Center for Health Sciences and Primary Care University Medical Center Utrecht Utrecht Netherlands Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent Belgium Department of Statistics University of Washington Seattle United States Department of Medical Informatics Amsterdam University Medical Centers Amsterdam Netherlands Institute for Logic Language and Computation University of Amsterdam Amsterdam Netherlands Pacmed Amsterdam Netherlands Department of Epidemiology and Data Science Amsterdam University Medical Centers Amsterdam Netherlands Department of Medical Statistics London School of Hygiene & Tropical Medicine Keppel Street London United Kingdom Pattern Recognition and Bio-Informatics Group EEMCS Delft University of Technology Delft Netherlands Amsterdam Machine Learning Lab University of Amsterdam Amsterdam Netherlands Department of Information and Computing Sciences Utrecht University Utrecht Netherlands Division of Informatics Imaging and Data Science Faculty of Biology Medicine and Health University of Manchester Manchester Academic Health Science Centre Manchester United Kingdom Department of Biomedical Data Sciences Leiden University Medical Center Leiden Netherlands Division of Pharmacoepidemiology and Clinical Pharmacology Department of Pharmaceutical Sciences Utrecht University Utrecht Netherlands Department of Biometry and Data Management Leibniz Institute for Prevention Research Epidemiology - BIPS Bremen Germany Faculty of Mathematics/Computer Science University of Bremen Bremen Germany

Prediction models are used amongst others to inform medical decisions on interventions. Typically, individuals with high risks of adverse outcomes are advised to undergo an intervention while those at low risk are advised to refrain from it. Standard prediction models do not always provide risks that are relevant to inform such decisions: e.g., an individual may be estimated to be at low risk because similar individuals in the past received an intervention which lowered their risk. Therefore, prediction models supporting decisions should target risks belonging to defined intervention strategies. Previous works on prediction under interventions assumed that the prediction model was used only at one time point to make an intervention decision. In clinical practice, intervention decisions are rarely made only once: they might be repeated, deferred and re-evaluated. This requires estimated risks under interventions that can be reconsidered at several potential decision moments. In the current work, we highlight key considerations for formulating estimands in sequential prediction under interventions that can inform such intervention decisions. We illustrate these considerations by giving examples of estimands for a case study about choosing between vaginal delivery and cesarean section for women giving birth. Our formalization of prediction tasks in a sequential, causal, and estimand context provides guidance for future studies to ensure that the right question is answered and appropriate causal estimation approaches are chosen to develop sequential prediction models that can inform intervention decisions. © 2023, CC BY.

关键词： Forecasting

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An algorithm of conversion between relational data and graph schema 2nd

An algorithm of conversion between relational data and graph...

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2nd International conference on Europe Middle East and North Africa information systems and Technologies to support Learning, EMENA-ISTL 2018

作者： Ait El Mouden, Zakariyaa Jakimi, Abdeslam Hajar, Moha Software Engineering and Information Systems Engineering Team Faculty of Sciences Technologies Errachidia Moulay Ismail University Meknes Morocco Operational Research and Computer Science Team Faculty of Sciences Technologies Errachidia Moulay Ismail University Meknes Morocco

ISBN: (纸本)9783030035761

This paper presents an approach of modeling a relational data by graphs using a specific open source format named Graph Exchange XML Format (GEXF), this approach can take us from a random data stored in a relational model database to a graph which models those data by nodes and edges to present the links between those data as a first step to the graph-oriented database. The advantage of this approach is that it can show as the whole stored data in a relational modal and with a simple look we can decide which data are more useful and which data can be ignored, using the visualization of the generated GEXF schema we can generate several graphs, where each SQL query in the input relational database generates a different graph. © Springer Nature Switzerland AG 2019.

关键词： Data visualization

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Complex-valued embeddings of generic proximity data

arXiv

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arXiv 2020年

作者： Münch, Maximilian Straat, Michiel Biehl, Michael Schleif, Frank-Michael University of Applied Sciences Würzburg-Schweinfurt Department of Computer Science and Business Information Systems WürzburgD-97074 Germany University of Groningen Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence P.O. Box 407 AK GroningenNL-9700 Netherlands

Proximities are at the heart of almost all machine learning methods. If the input data are given as numerical vectors of equal lengths, euclidean distance, or a Hilbertian inner product is frequently used in modeling algorithms. In a more generic view, objects are compared by a (symmetric) similarity or dissimilarity measure, which may not obey particular mathematical properties. This renders many machine learning methods invalid, leading to convergence problems and the loss of guarantees, like generalization bounds. In many cases, the preferred dissimilarity measure is not metric, like the earth mover distance, or the similarity measure may not be a simple inner product in a Hilbert space but in its generalization a Krein space. If the input data are non-vectorial, like text sequences, proximity-based learning is used or ngram embedding techniques can be applied. Standard embeddings lead to the desired fixed-length vector encoding, but are costly and have substantial limitations in preserving the original data’s full information. As an information preserving alternative, we propose a complex-valued vector embedding of proximity data. This allows suitable machine learning algorithms to use these fixed-length, complex-valued vectors for further processing. The complex-valued data can serve as an input to complex-valued machine learning algorithms. In particular, we address supervised learning and use extensions of prototype-based learning. The proposed approach is evaluated on a variety of standard benchmarks and shows strong performance compared to traditional techniques in processing non-metric or non-psd proximity data. Copyright © 2020, The Authors. All rights reserved.

关键词： Embeddings

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Fiber nonlinearity mitigation via the parzen window classifier for dispersion managed and unmanaged links 21

Fiber nonlinearity mitigation via the parzen window classifi...

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21st International Conference on Transparent Optical Networks, ICTON 2019

作者： Amari, Abdelkerim Lin, Xiang Dobre, Octavia A. Venkatesan, Ramachandran Alvarado, Alex Information and Communication Theory Lab Signal Processing Systems Group Department of Electrical Engineering Eindhoven University of Technology Netherlands Faculty of Engineering and Applied Science Memorial University St. John's Canada

ISBN: (纸本)9781728127798

Machine learning techniques have recently received significant attention as promising approaches to deal with the optical channel impairments, and in particular, the nonlinear effects. In this work, a machine learning-based classification technique, known as the Parzen window (PW) classifier, is applied to mitigate the nonlinear effects in the optical channel. The PW classifier is used as a detector with improved nonlinear decision boundaries more adapted to the nonlinear fiber channel. Performance improvement is observed when applying the PW in the context of dispersion managed and dispersion unmanaged systems. © 2019 IEEE.

关键词： Nonlinear optics

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Digital technologies in heritage conservation. Methods of teaching and learning this ***. degree, unique in Germany 1st

Digital technologies in heritage conservation. Methods of te...

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1st International and Interdisciplinary Conference on Digital Environments for Education, Arts and Heritage, EARTH 2018

作者： Hess, M. Schlieder, C. Troi, A. Huth, O. Jagfeld, M. Hindmarch, J. Henrich, A. Institute for Archaeology Heritage Science and Art History Humanities Faculty University of Bamberg Am Zwinger Bamberg96047 Germany Faculty of Information System and Applied Computer Sciences University of Bamberg An der Spinnerei Bamberg96047 Germany Faculty of Design Coburg University of Applied Sciences and Arts Am Hofbrauhaus 1 Coburg96450 Germany

ISBN: (纸本)9783030122393

A new master’s degree commenced in 2017 at the University of Bamberg. The purpose of the ***. Digital Technologies in Heritage Conservation is to impart theoretical and practical knowledge and develop competence in critical assessment and object-oriented solutions. The aims, curriculum and methods of teaching will be discussed in this paper. © Springer Nature Switzerland AG 2019.

关键词： Curricula

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Correction to: Integrated approach of federated learning with transfer learning for classification and diagnosis of brain tumor

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BMC medical imaging 2024年第1期24卷 161页

作者： Eid Albalawi Mahesh T R Arastu Thakur V Vinoth Kumar Muskan Gupta Surbhi Bhatia Khan Ahlam Almusharraf Department of Computer Science College of Computer Science and Information Technology King Faisal University Hofuf 31982 Saudi Arabia. Department of Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore 562112 India. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore 632014 India. School of Science Engineering and Environment University of Salford M5 4WT Manchester UK. s.khan138@salford.ac.uk. Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Lebanon. s.khan138@salford.ac.uk. Department of Business Administration College of Business and Administration Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia.

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Advancing respiratory disease diagnosis: A deep learning and vision transformer-based approach with a novel X-ray dataset

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computers in biology and medicine 2025年 194卷 110501页

作者： Amer Alghadhban Rabie A Ramadan Meshari Alazmi Electrical Engineering Department College of Engineering University of Ha'il Ha'il 55476 Saudi Arabia. Electronic address: a.alghadhban@uoh.edu.sa. College of Economics Management & Information Systems Department of Information Systems Nizwa University Nizwa Sultanate of Oman Department of Computer Engineering Faculty of Engineering Cairo University Giza Egypt. Electronic address: rabie@***. College of Computer Science and Engineering University of Ha'il P.O.Box 2440 Ha'il 55424 Saudi Arabia. Electronic address: ms.alazmi@uoh.edu.sa.

With the increasing prevalence of respiratory diseases such as pneumonia and COVID-19, timely and accurate diagnosis is critical. This paper makes significant contributions to the field of respiratory disease classification by utilizing X-ray images and advanced machine learning techniques such as deep learning (DL) and Vision Transformers (ViT). First, the paper systematically reviews the current diagnostic methodologies, analyzing the recent advancement in DL and ViT techniques through a comprehensive analysis of the review articles published between 2017 and 2024, excluding short reviews and overviews. The review not only analyses the existing knowledge but also identifies the critical gaps in the field as well as the lack of diversity of the comprehensive and diverse datasets for training the machine learning models. To address such limitations, the paper extensively evaluates DL-based models on publicly available datasets, analyzing key performance metrics such as accuracy, precision, recall, and F1-score. Our evaluations reveal that the current datasets are mostly limited to the narrow subsets of pulmonary diseases, which might lead to some challenges, including overfitting, poor generalization, and reduced possibility of using advanced machine learning techniques in real-world applications. For instance, DL and ViT models require extensive data for effective learning. The primary contribution of this paper is not only the review of the most recent articles and surveys of respiratory diseases and DL models, including ViT, but also introduces a novel, diverse dataset comprising 7867 X-ray images from 5263 patients across three local hospitals, covering 49 distinct pulmonary diseases. The dataset is expected to enhance DL and ViT model training and improve the generalization of those models in various real-world medical image scenarios. By addressing the data scarcity issue, this paper paves the for more reliable and robust disease classification, improving clin

关键词： computer-aided diagnosis Deep learning Machine learning Medical diagnostic imaging Respiratory diseases

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Guidelines for public database submission of uncultivated virus genome sequences for taxonomic classification (vol 41, pg 898, 2023)

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NATURE BIOTECHNOLOGY 2023年第9期41卷 1346-1346页

作者： Adriaenssens, Evelien M. M. Roux, Simon Brister, J. Rodney Karsch-Mizrachi, Ilene Kuhn, Jens H. Varsani, Arvind Yigang, Tong Reyes, Alejandro Lood, Cedric Lefkowitz, Elliot J. Sullivan, Matthew B. B. Edwards, Robert A. A. Simmonds, Peter Rubino, Luisa Sabanadzovic, Sead Krupovic, Mart Dutilh, Bas E. E. Quadram Institute Bioscience Norwich Research Park Rosalind Franklin Road Norwich UK United States Department of Energy Joint Genome Institute Lawrence Berkeley National Laboratory Berkeley CA USA National Center for Biotechnology Information National Library of Medicine National Institutes of Health Bethesda MD USA Integrated Research Facility at Fort Detrick National Institute of Allergy and Infectious Diseases National Institutes of Health Fort Detrick Frederick MD USA The Biodesign Center for Fundamental and Applied Microbiomics Arizona State University Tempe AZ USA Structural Biology Research Unit Department of Clinical Laboratory Sciences University of Cape Town Cape Town South Africa Beijing Advanced Innovation Center for Soft Matter Science and Engineering College of Life Science and Technology Beijing University of Chemical Technology Beijing China Max Planck Tandem Group in Computational Biology Department of Biological Sciences Universidad de los Andes Bogotá Colombia Centre of Microbial and Plant Genetics Department of Microbial and Molecular Systems KU Leuven Leuven Belgium Laboratory of Gene Technology Department of Biosystems KU Leuven Leuven Belgium Institute of Biodiversity Faculty of Biological Sciences Cluster of Excellence Balance of the Microverse Friedrich Schiller University Jena Jena Germany Department of Microbiology University of Alabama at Birmingham Birmingham AL USA Department of Microbiology The Ohio State University Columbus OH USA Department of Civil Environmental and Geodetic Engineering The Ohio State University Columbus OH USA Center of Microbiome Science The Ohio State University Columbus OH USA College of Science and Engineering Flinders University Bedford Park South Australia Australia Nuffield Department of Medicine University of Oxford South Parks Road Oxford UK Consiglio Nazionale delle Ricerche Istituto per la Protezione Sostenibile delle Piante Bari Italy Department of Biochemistry Molecular Bio

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Fully three-dimensional sound speed-corrected multi-wavelength photoacoustic breast tomography

arXiv

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arXiv 2023年

作者： Dantuma, M. Lucka, F. Kruitwagen, S.C. Javaherian, A. Alink, L. Pompe van Meerdervoort, R.P. Nanninga, M. Op't Root, T.J.P.M. De Santi, B. Budisky, J. Bordovsky, G. Coffy, E. Wilm, M. Kasponas, T. Aarnink, S.H. de Geus-Oei, L.F. Brochin, F. Martinez, T. Michailovas, A. Muller Kobold, W. Jaros, J. Veltman, J. Cox, B. Manohar, S. Multi-Modality Medical Imaging group TechMed Centre University of Twente Enschede Netherlands Computational Imaging Centrum Wiskunde & Informatica Amsterdam Netherlands Department of Computer Science University College London London United Kingdom Medisch Spectrum Twente Hospital Enschede Netherlands Department of Medical Physics and Biomedical Engineering University College London London United Kingdom P.A. Imaging R&D B.V. Enschede Netherlands Department of Computer Systems Faculty of Information Technology Brno University of Technology Brno Czech Republic Imasonic SAS Voray-sur-l'Orgnon France Ekspla UAB Vilnius Lithuania Biomedical Photonic Imaging group University of Twente Enschede Netherlands Department of Radiology Leiden University Medical Center Leiden Netherlands Center for Physical Science and Technology Vilnius Lithuania Department of Radiology Ziekenhuisgroep Twente Hengelo Netherlands

Photoacoustic tomography is a contrast agent-free imaging technique capable of visualizing blood vessels and tumor-associated vascularization in breast tissue. While sophisticated breast imaging systems have been recently developed, there is yet much to be gained in imaging depth, image quality and tissue characterization capability before clinical translation is possible. In response, we have developed a hybrid photoacoustic and ultrasound-transmission tomographic system (PAM3). The photoacoustic component has for the first time three-dimensional multi-wavelength imaging capability, and implements substantial technical advancements in critical hardware and software subsystems. The ultrasound component enables for the first time, a three-dimensional sound speed map of the breast to be incorporated in photoacoustic reconstruction to correct for inhomogeneities, enabling accurate target recovery. The results demonstrate the deepest photoacoustic breast imaging to date (48 mm),with a more uniform field of view than hitherto, and an isotropic spatial resolution that rivals that of Magnetic Resonance Imaging. The in vivo performance achieved, and the diagnostic value of interrogating angiogenesis-driven optical contrast as well as tumor mass sound speed contrast, gives confidence in the system's clinical potential. © 2023, CC BY.

关键词： Photoacoustic effect

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Corrigendum to “LightSOD: Towards lightweight and efficient network for salient object detection” [J. Comput. Vis. Imag. Underst. 249 (2024) 104148]

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computer Vision and Image Understanding 2025年 252卷

作者： Thien-Thu Ngo Hoang Ngoc Tran Md. Delowar Hossain Eui-Nam Huh School of Information Technology Center for Applied Intelligent Systems Research Halmstad University Kristian IV:s väg 3 Halmstad 30118 Halland Sweden Department Computer Science and Engineering KyungHee University Global Campus Deogyeong-daero Yongin-si 17104 Gyeonggi-do Republic of Korea Department of Software Engineering FPT University 600 Nguyen Van Cu Street Ninh Kieu 94000 Can Tho Viet Nam Department of Computer Science and Engineering Hajee Mohammad Danesh Science & Technology University Basherhat Dinajpur 5200 Rangpur Division Bangladesh

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