检索结果-内蒙古大学图书馆

Real-Valued Group Testing for Quantitative Molecular Assays 26th

学校读者我要写书评

暂无评论

Real-Valued Group Testing for Quantitative Molecular Assays

26th International Conference on Research in Computational Molecular Biology, RECOMB 2022

作者： Saeedi, Seyran Serrano, Myrna Yang, Dennis G. Brooks, J. Paul Buck, Gregory A. Arodz, Tomasz Department of Computer Science College of Engineering Virginia Commonwealth University RichmondVA United States Department of Microbiology and Immunology School of Medicine Virginia Commonwealth University RichmondVA United States Department of Mathematics College of Arts and Sciences Drexel University PhiladelphiaPA United States Department of Information Systems School of Business Virginia Commonwealth University RichmondVA United States Department of Electrical and Computer Engineering University of California Santa BarbaraCA United States Center for Microbiome Engineering and Data Analysis Virginia Commonwealth University RichmondVA United States

Combinatorial group testing and compressed sensing both focus on recovering a sparse vector of dimensionality n from a much smaller number m © 2022, The Author(s), under exclusive license to Springer Nature Switz... 详细信息

ISBN: (纸本)9783031047480

关键词： SARS

Bounding the sum of the largest signless Laplacian eigenvalues of a graph

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Abiad, Aida de Lima, Leonardo Kalantarzadeh, Sina Mohammadi, Mona Oliveira, Carla Department of Mathematics and Computer Science Eindhoven University of Technology Netherlands Department of Mathematics: Analysis Logic and Discrete Mathematics Ghent University Belgium Department of Mathematics and Data Science Vrije Universiteit Brussel Belgium Graduate Program in Mathematics Federal University of Parana Curitiba Brazil Sharif University of Technology Iran Department of Mathematical National School of Statistical Sciences Rio de Janeiro Brazil

关键词： Eigenvalues and eigenfunctions

An efficient Monte Carlo scheme for Zakai equations

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Beck, Christian Becker, Sebastian Cheridito, Patrick Jentzen, Arnulf Neufeld, Ariel Department of Mathematics ETH Zurich Switzerland School of Data Science Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China Applied Mathematics: Institute for Analysis and Numerics Faculty of Mathematics and Computer Science University of Münster Germany Division of Mathematical Sciences School of Physical and Mathematical Sciences Nanyang Technological University Singapore

In this paper we develop a numerical method for efficiently approximating solutions of certain Zakai equations in high dimensions. The key idea is to transform a given Zakai SPDE into a PDE with random coefficients. We show that under suitable regularity assumptions on the coefficients of the Zakai equation, the corresponding random PDE admits a solution random field which, for almost all realizations of the random coefficients, can be written as a classical solution of a linear parabolic PDE. This makes it possible to apply the Feynman–Kac formula to obtain an efficient Monte Carlo scheme for computing approximate solutions of Zakai equations. The approach achieves good results in up to 25 dimensions with fast run *** Codes 65C05, 65M75, 60H15, 62M20 Copyright © 2022, The Authors. All rights reserved.

关键词： Numerical methods

Scaling nnU-Net for CBCT Segmentation 2nd

学校读者我要写书评

暂无评论

Scaling nnU-Net for CBCT Segmentation

2nd International ToothFairy2: Multi-Structure Segmentation in CBCT Volumes, ToothFairy 2024, 3D Teeth Landmarks Detection Challenge, 3DTeethLand 2024, Semi-supervised Teeth Segmentation, STS 2024 held in conjunction with the 27th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2024

作者： Isensee, Fabian Kirchhoff, Yannick Kraemer, Lars Rokuss, Maximilian Ulrich, Constantin Maier-Hein, Klaus H. Heidelberg Germany Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe/Heidelberg Germany Medical Faculty Heidelberg Heidelberg University Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany

ISBN: (纸本)9783031889769

This paper presents our approach to scaling the nnU-Net framework for multi-structure segmentation on Cone Beam Computed Tomography (CBCT) images, specifically in the scope of the ToothFairy2 Challenge. We leveraged the nnU-Net ResEnc L model, introducing key modifications to patch size, network topology, and data augmentation strategies to address the unique challenges of dental CBCT imaging. Our method achieved a mean Dice coefficient of 0.9253 and HD95 of 18.472 on the test set, securing a mean rank of 4.6 and with it the first place in the ToothFairy2 challenge. The source code is publicly available, encouraging further research and development in the field. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image segmentation

Back to the Future: Challenges of Sparse and Irregular Medical Image Time Series

学校读者我要写书评

暂无评论

Back to the Future: Challenges of Sparse and Irregular M...

Workshop on Longitudinal Disease Tracking and Modeling with Medical Images and data, LDTM 2024, 5th International Workshop on Multiscale Multimodal Medical Imaging, MMMI 2024, 1st Workshop on Machine Learning for Multimodal/-sensor Healthcare data, ML4MHD2024 and Workshop on Multimodal Learning and Fusion Across Scales for Clinical Decision Support, ML-CDS 2024 held in conjunction with the 27th International Conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2024

作者： Disch, Nico Albert Peretzke, Robin Roy, Saikat Ulrich, Constantin Zimmerer, David Stiefelhagen, Rainer Kleesiek, Jens Maier-Hein, Klaus Division of Medical Image Computing German Cancer Research Center Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Faculty of Mathematics and Computer Science University of Heidelberg Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany Karlsruhe Institute of Technology Karlsruhe Germany University Hospital Essen Essen Germany University Hospital Essen West German Cancer Center Essen Essen Germany Medical Faculty Heidelberg University of Heidelberg Heidelberg Germany

ISBN: (纸本)9783031845246

In longitudinal medical image analysis, most work focuses on regularly sampled images, or on tasks like regression or classification. However, in the clinical context, images are frequently generated irregularly due to factors such as cost constraints. This work tackles the problem of irregularly sampled longitudinal medical imaging by evaluating methods in segmentation and reconstruction tasks. We examine the reconstruction for real-life MRI from patients with Alzheimer’s Disease (AD), where the model predicts future MRI scans from an arbitrary number of input images sampled at irregular intervals. However, experiments show that most models cannot surpass the performance of using an image from the same patient at a different time point as a baseline. Therefore, we conducted experiments on a synthetic dataset to better isolate effects in temporal learning. These experiments offer insights into model behavior and serve as a benchmark for validating models’ longitudinal learning capability, thus providing a straightforward method to assess temporal understanding in a controlled environment. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neurodegenerative diseases

Development of Elliptical Cryptography Technique to Watermark Embedded and Extrusion for Healthcare Records 2nd

学校读者我要写书评

暂无评论

Development of Elliptical Cryptography Technique to Watermar...

2nd International Conference on Cyber Warfare, Security and Space Computing, SpacSec 2024

作者： Hegde, N. Prajwal Sivaraman, R. Dharmamoorthy, G. Dubey, Ankit Kumar Patro, Pramoda Mary, S. Suma Christal Department of Artificial Intelligence and Data Science NMAM Institute of Technology Nitte Deemed to be University Karkala India Department of Mathematics Dwaraka Doss Goverdhan Doss Vaishnav College Arumbakkam Tamil Nadu Chennai India Department of Pharmaceutical Analysis MB School of Pharmaceutical Sciences Mohan Babu University Tirupati Rangampeta India Department of Computer Science and Engineering Baderia Global Institute of Engineering and Management MP Jabalpur India Department of Mathematics Koneru Lakshmaiah Education Foundation Hyderabad India Department of Information Technology Panimalar Engineering College Poonamalle Chennai India

ISBN: (纸本)9783031734939

An application of E-healthcare watermarking method based on a hybridization of encryption as well as compression algorithms is proposed as the primary goal of this work. One step involves inserting a watermark into the image, and the other involves removing it. These are the two main components of the suggested system. Before beginning the embedding procedure, use a region-growing method to segment the tumour independently. Hash Secure Algorithm-256 encrypt the electronic health record (EHR) and Elliptical curve cryptography and its associated area of interest. Mathematical coding algorithms are then used to concatenate and compress the data. Lastly, the packed down bit is embedded into the original picture. When extracting a substance, the same steps are conducted again. Using the maximum signal ratio to noisy and normalized correlation, the experimental outcomes for various medicinal photos using EHR are shown, and the efficacy of the suggested method is evaluated. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Electronic health record

Scaling nnU-Net for CBCT Segmentation

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Isensee, Fabian Kirchhoff, Yannick Kraemer, Lars Rokuss, Maximilian Ulrich, Constantin Maier-Hein, Klaus H. Heidelberg Germany Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Medical Faculty Heidelberg Heidelberg University Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Germany

关键词： Image segmentation

Protecting Africa's children from extreme risk: a runway of sustainability for PEPFAR programmes

学校读者我要写书评

暂无评论

The Lancet 2025年第10490期405卷 1700-1712页

作者： Cluver, Lucie Makangila, Gibstar Hillis, Susan Flaxman, Seth Unwin, Juliette Imai-Eaton, Jeffrey W Chtimbire, Vuyelwa Sherr, Lorraine Desmond, Chris Toska, Elona Omigbodun, Olayinka Ratmann, Oliver Carey, Galen Mahy, Mary Honermann, Brian Stover, John Centre for Evidence-Based Social Intervention Department of Social Policy and Intervention University of Oxford Oxford United Kingdom Department of Psychiatry and Mental Health University of Cape Town Cape Town South Africa Circle of Hope Lusaka Zambia Department of Mathematics Imperial College London London United Kingdom School of Mathematics University of Edinburgh Edinburgh United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom School of Mathematics University of Bristol Bristol United Kingdom MRC Centre for Global Infectious Disease Analysis School of Public Health Imperial College London London United Kingdom Center for Communicable Disease Dynamics Department of Epidemiology Harvard T H Chan School of Public Health Boston MA United States Zimbabwe Association of Church-related Hospitals (ZACH) Harare Zimbabwe Institute of Global Health University College London London United Kingdom Ecumenical Pharmaceutical Network Africa Nairobi Kenya School of Economics and Finance University of the Witwatersrand Johannesburg South Africa Centre for Social Science Research University of Cape Town Cape Town South Africa Department of Sociology University of Cape Town Cape Town South Africa Centre for Child and Adolescent Mental Health College of Medicine University of Ibadan Ibadan Nigeria Department of Psychiatry College of Medicine University of Ibadan Ibadan Nigeria National Association of Evangelicals Washington DC United States Data For Impact UNAIDS Geneva Switzerland The Foundation for AIDS Research Washington DC United States Center for Modelling Planning and Policy Analysis Avenir Health Glastonbury CT United States

PEPFAR (President's Emergency Plan for AIDS Relief), a landmark US foreign health policy, is recognised for saving 26 million lives from HIV. PEPFAR investments have also had life-saving impacts for children across sub-Saharan Africa through childhood HIV prevention, care, and treatment, ensuring 7·8 million babies were born HIV-free, supporting 13 million orphaned and vulnerable children, and protecting 10·3 million girls from sexual abuse. In this Health Policy, we review data from UNAIDS, UNICEF, World Bank, Violence Against Children Surveys, SPECTRUM model data, and Population-based HIV Impact Assessments;synthesise PEPFAR reports;conduct in-depth interviews;search PubMed for programme effectiveness evidence;and review economic reports. PEPFAR support is associated with substantial collateral benefits for the USA and Africa, including a four-fold increase in export of US goods to Africa, and US$71·6 billion in total goods trade between the USA and Africa in 2024. PEPFAR-supported countries in Africa are committed to ownership of HIV responses by 2030—overall, PEPFAR-supported countries in sub-Saharan Africa have progressively increased their co-financing of their health systems through domestic government and private expenditure from $13·7 billion per year in 2004 to $42·6 billion per year in 2021. The feasibility of a 5-year transition to country-led sustainability is supported by evidence of innovative cost-saving models of delivery, including through faith-based and community-based organisations, and high return-on-investment for PEPFAR programmes. There are also collateral benefits of PEPFAR for US and Africa national security and health security, for example, reducing forced migration and increasing capacity to control emerging transborder infectious disease threats. Risks in sub-Saharan Africa remain acute: one in five girls (younger than 18 years) experience rape or sexual assault;one in ten children (younger than 18 years) are orphaned;and a child (young

关键词：

Low-Rank Reduced Biquaternion Tensor Ring Decomposition and Tensor Completion

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Luo, Hui Liu, Xin Liu, Wei Zhang, Yang School of Computer Science and Engineering Faculty of Innovation Engineering Macau University of Science and Technology China Macau Institute of Systems Engineering Faculty of Innovation Engineering Macau University of Science and Technology China School of Artificial Intelligence Sun Yat-sen University The Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Department of Mathematics University of Manitoba MB Canada

We define the reduced biquaternion tensor ring (RBTR) decomposition and provide a detailed exposition of the corresponding algorithm RBTR-SVD. Leveraging RBTR decomposition, we propose a novel low-rank tensor completion algorithm RBTR-TV integrating RBTR ranks with total variation (TV) regularization to optimize the process. Numerical experiments on color image and video completion tasks indicate the advantages of our method. © 2025, CC BY.

关键词： Tensors