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Synthesising 3D Cardiac CINE-MR Images and Corresponding Segmentation Masks using a Latent Diffusion Model

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Synthesising 3D Cardiac CINE-MR Images and Corresponding Seg...

IEEE International Symposium on Biomedical Imaging

作者： Nina Cheng Zhengji Liu Yash Deo Haoran Dou Ning Bi Kun Wu Fengming Lin Zeike A Taylor Nishant Ravikumar Alejandro F Frangi CISTIB Centre for Computational Imaging and Simulation Technologies in Biomedicine University of Leeds School of Optometry The Hong Kong Polytechnic University NIHR Leeds Biomedical Research Centre Leeds UK Alan Turing Institute London UK Division of Informatics Imaging and Data Science Schools of Computer Science and Health Sciences University of Manchester Manchester UK Cardiovascular Sciences Departments Medical Imaging Research Center (MIRC) Electrical Engineering KU Leuven Leuven Belgium

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

We propose a novel pipeline for the generation of synthetic full spatial cine cardiac magnetic resonance (CMR) images via a latent Denoising Diffusion Implicit Models (DDIMs). These synthetic images can be used as viable alternatives to real data in deep learning model training for downstream cardiac image analysis tasks such as cardiac segmentation. To demonstrate the effectiveness of this approach, we generated synthetic CMR images along with their corresponding segmentation masks. We evaluated model performance using a variety of methods, including generated image fidelity, diversity and calculated the volumes of the generated segmentation masks and compare it with the real segmentation masks. The proposed pipeline has the potential to be widely applied to other tasks in various medical imaging modalities. Effective and efficient generation of 3D cine cardiac images with corresponding segmentation masks can supplement real patient datasets and help reduce the burden of manually annotating images.

关键词： Training Image segmentation Solid modeling Three-dimensional displays Biological system modeling Pipelines Noise reduction

Estimating geographic variation of infection fatality ratios during epidemics

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Infectious Disease Modelling 2024年第2期9卷 634-643页

作者： Joshua Ladau Eoin L.Brodie Nicola Falco Ishan Bansal Elijah B.Hoffman Marcin P.Joachimiak Ana M.Mora Angelica M.Walker Haruko M.Wainwright Yulun Wu Mirko Pavicic Daniel Jacobson Matthias Hess James B.Brown Katrina Abuabara Departments of Computational Precision Health and Dermatology University of CaliforniaSan FranciscoCA94115USA Arva Intelligence Inc.Salt Lake CityUT84101USA Computational Biosciences Group Lawrence Berkeley National LaboratoryBerkeleyCA94720USA Earth and Environmental Sciences Area Lawrence Berkeley National LaboratoryBerkeleyCA94720USA Graduate Group in Biostatistics University of CaliforniaBerkeleyCA94720USA Biosystems Data Science Environmental Genomics and Systems BiologyLawrence Berkeley National LaboratoryBerkeleyCA94720USA Center for Environmental Research and Community Health(CERCH) School of Public HealthUniversity of CaliforniaBerkeleyCA94720USA Bredesen Center for Interdisciplinary Research and Graduate Education University of TennesseeKnoxvilleTN37996USA Department of Nuclear Science and Engineering Massachusetts Institute of TechnologyBostonMA02139USA Biosciences Oak Ridge National LaboratoryOak RidgeTN37830USA University of California DavisCA95616USA Statistics Department University of CaliforniaBerkeleyCA94720USA Division of Epidemiology and Biostatistics University of California Berkeley School of Public Health2121 Berkeley WayBerkeleyCA94720USA

Objectives We aim to estimate geographic variability in total numbers of infections and infection fatality ratios(IFR;the number of deaths caused by an infection per 1,000 infected people)when the availability and quality of data on disease burden are limited during an *** We develop a noncentral hypergeometric framework that accounts for differential probabilities of positive tests and reflects the fact that symptomatic people are more likely to seek *** demonstrate the robustness,accuracy,and precision of this framework,and apply it to the United States(U.S.)COVID-19 pandemic to estimate county-level SARS-CoV-2 *** The estimators for the numbers of infections and IFRs showed high accuracy and precision;for instance,when applied to simulated validation data sets,across counties,Pearson correlation coefficients between estimator means and true values were 0.996 and 0.928,respectively,and they showed strong robustness to model *** the county-level estimators to the real,unsimulated COVID-19 data spanning April 1,2020 to September 30,2020 from across the U.S.,we found that IFRs varied from 0 to 44.69,with a standard deviation of 3.55 and a median of *** The proposed estimation framework can be used to identify geographic variation in IFRs across settings.

关键词： Infection fatality ratio Infection fatality rate Noncentral hypergeometric distribution COVID-19 SARS-CoV-2

Absorption Bias: An Ideal Descriptor for Radiation Tolerance of Polycrystalline Bcc Metals

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SSRN

SSRN 2024年

作者： Wei, Liuming Zhao, Zhe Li, Yonggang Zheng, Qirong Zhang, Chuanguo Li, Jingyu Zhao, Gaofeng Da, Bo Zeng, Zhi Key Laboratory of Materials Physics Institute of Solid State Physics HFIPS Chinese Academy of Sciences Hefei230031 China University of Science and Technology of China Hefei230026 China Department of Physics Henan Police College Zhengzhou450046 China Institute for Computational Materials Science School of Physics and Electronics Henan University Kaifeng475004 China Research and Services Division of Materials Data and Integrated System National Institute for Materials Science Namiki 1-1 Ibaraki Tsukuba305-0044 Japan

To evaluate the radiation tolerance of polycrystalline materials, the damage effects of Fe and W as typical body-centered cubic metals under uniform irradiation are studied by multi-scale models. An ideal descriptor, the absorption bias (the ratio of the absorption abilities of grain boundaries (GBs) to interstitials (I) and vacancies (V)), is proposed to reflect the radiation tolerance of metals with different grain sizes. Low absorption bias promotes defects annihilation through enhancing I-V recombination and optimally tuning its competition with GB absorption. Polycrystalline metals possess high anti-irradiation performance only with low absorption bias regulated by grain size and temperature. Furthermore, by comprehensively considering the mechanical property, thermal stability, and radiation tolerance described by absorption bias, nano-crystals are recommended for Fe-based structural materials but coarsegrained crystals for W-based plasma-facing materials. This work reevaluates the radiation tolerance of polycrystalline metals, resulting in new strategies for designing structural materials of nuclear devices through manipulating grain sizes. © 2024, The Authors. All rights reserved.

关键词： Polycrystalline materials

The Artificial Intelligence Ontology: LLM-assisted construction of AI concept hierarchies

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arXiv 2024年

作者： Joachimiak, Marcin P. Miller, Mark A. Caufield, J. Harry Ly, Ryan Harris, Nomi L. Tritt, Andrew Mungall, Christopher J. Bouchard, Kristofer E. Biosystems Data Science Department Environmental Genomics and Systems Biology Division Lawrence Berkeley National Laboratory 1 Cyclotron Road BerkeleyCA94720 United States Scientific Data Division Lawrence Berkeley National Laboratory 1 Cyclotron Road BerkeleyCA94720 United States Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory 1 Cyclotron Road BerkeleyCA94720 United States Biological Systems & Engineering Division Lawrence Berkeley National Laboratory 1 Cyclotron Road BerkeleyCA94720 United States Helen Wills Neuroscience Institute UC Berkeley BerkeleyCA94720 United States Redwood Center for Theoretical Neuroscience UC Berkeley BerkeleyCA94720 United States

The Artificial Intelligence Ontology (AIO) is a systematization of artificial intelligence (AI) concepts, methodologies, and their interrelations. Developed via manual curation, with the additional assistance of large language models (LLMs), AIO aims to address the rapidly evolving landscape of AI by providing a comprehensive framework that encompasses both technical and ethical aspects of AI technologies. The primary audience for AIO includes AI researchers, developers, and educators seeking standardized terminology and concepts within the AI domain. The ontology is structured around six top-level branches: Networks, Layers, Functions, LLMs, Preprocessing, and Bias, each designed to support the modular composition of AI methods and facilitate a deeper understanding of deep learning architectures and ethical considerations in AI. AIO's development utilized the Ontology Development Kit (ODK) for its creation and maintenance, with its content being dynamically updated through AI-driven curation support. This approach not only ensures the ontology's relevance amidst the fast-paced advancements in AI but also significantly enhances its utility for researchers, developers, and educators by simplifying the integration of new AI concepts and methodologies. The ontology's utility is demonstrated through the annotation of AI methods data in a catalog of AI research publications and the integration into the BioPortal ontology resource, highlighting it potential for cross-disciplinary research. The AIO ontology is open source and is available on GitHub (https://***/berkeleybop/artificial-intelligence-ontology) and BioPortal (https://***/ontologies/AIO). © 2024, CC BY.

关键词： Deep neural networks

Weakly Supervised IDH-Status Glioma Classification from H&E-Stained Whole Slide Images

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Weakly Supervised IDH-Status Glioma Classification from H&E-...

IEEE International Symposium on Biomedical Imaging

作者： Shubham Innani Bhakti Baheti MacLean P. Nasrallah Spyridon Bakas Department of Pathology and Laboratory Medicine School of Medicine Division of Computational Pathology Indiana University Indianapolis IN USA Department of Pathology & Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Neurological Surgery School of Medicine Indiana University Indianapolis IN USA Department of Biostatistics & Health Data Science School of Medicine Indiana University Indianapolis IN USA Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University Indianapolis IN USA

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Gliomas are the most common malignant primary brain tumors in the Central Nervous System. Isocitrate dehydrogenase (IDH) mutation is a key driver of gliomagenesis in infiltrating gliomas and a diagnostic criterion with prognostic relevance. Determining IDH status from hematoxylin and eosin (H&E) stained Whole Slide Images (WSI) would be a valuable tool in precision oncology as it would expedite diagnosis and downstream patient treatment. We seek a computational approach to predict IDH status from digitized histopathology glioma sections (WSI). The proposed approach first applies a comprehensive curation step to remove artifactual content on each H&E-stained WSI, followed by a classification mechanism based on weakly supervised attention-based multiple instance learning. Quantitative evaluation on 1,534 digitized tissue sections from 799 cases from the publicly available TCGA-GBM and TCGA-LGG dataset revealed AUC of 0.92 and a balanced accuracy of 0.85 on a 10-fold cross-validation (10CV) schema. Histological prediction of IDH status represents an ideal prototype to interrogate WSI towards identifying clinically-relevant tumor biomarkers.

关键词： Accuracy Histopathology Prototypes Medical treatment Central nervous system Biomarkers Oncology

Truthful Generalized Linear Models

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arXiv 2022年

作者： Qiu, Yuan Liu, Jinyan Wang, Di College of Computing Georgia Institute of Technology United States School of Computer Science and Technology Beijing Institute of Technology China Division of CEMSE King Abdullah University of Science and Technology Saudi Arabia SDAIA-KAUST Center of Excellence in Data Science and Artificial Intelligence Saudi Arabia Computational Bioscience Research Center Saudi Arabia

In this paper we study estimating Generalized Linear Models (GLMs) in the case where the agents (individuals) are strategic or self-interested and they concern about their privacy when reporting data. Compared with the classical setting, here we aim to design mechanisms that can both incentivize most agents to truthfully report their data and preserve the privacy of individuals’ reports, while their outputs should also close to the underlying parameter. In the first part of the paper, we consider the case where the covariates are sub-Gaussian and the responses are heavy-tailed where they only have the finite fourth moments. First, motivated by the stationary condition of the maximizer of the likelihood function, we derive a novel private and closed form estimator. Based on the estimator, we propose a mechanism which has the following properties via some appropriate design of the computation and payment scheme for several canonical models such as linear regression, logistic regression and Poisson regression: (1) the mechanism is o(1)-jointly differentially private (with probability at least 1 − o(1));(2) it is an o(n1 )-approximate Bayes Nash equilibrium for a (1 − o(1))-fraction of agents to truthfully report their data, where n is the number of agents;(3) the output could achieve an error of o(1) to the underlying parameter;(4) it is individually rational for a (1 − o(1)) fraction of agents in the mechanism;(5) the payment budget required from the analyst to run the mechanism is o(1). In the second part, we consider the linear regression model under more general setting where both covariates and responses are heavy-tailed and only have finite fourth moments. By using an 4-norm shrinkage operator, we propose a private estimator and payment scheme which have similar properties as in the sub-Gaussian case. © 2022, CC BY.

关键词： Linear regression

Community microgrid:an approach towards positive energy community in an urban area of Dhaka,Bangladesh

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Clean Energy 2023年第4期7卷 926-939页

作者： Mohammad Zeyad S.M.Masum Ahmed Sayeed Hasan Dewan Mahnaaz Mahmud Energy and Technology Research Division Advanced BioinformaticsComputational Biology and Data Science LaboratoryBangladesh(ABCD LaboratoryBangladesh)House:24Road:01Block:BHalishaharChattogram 4226Bangladesh School of Engineering and Physical Sciences(EPS) Heriot-Watt UniversityCampus The AvenueEdinburghScotland EH144ASUK Department of Energy Engineering University of the Basque Country(UPV/EHU)Ingeniero Torres Quevedo Plaza1BilbaoBiscay 48013Spain Department of Electrical Electronic and Control EngineeringSchool of Industrial EngineeringUniversity of ExtremaduraCampus UniversitarioAv.de Elvass/nBadajozBadajoz 06006Spain Centre for Research on Geography ResourcesEnvironmentEnergy and Networks(Green)Bocconi UniversityVia Roentgen 1MilanoLombardy 20136Italy Control and Robotics(CORO) Ecole Centrale De Nantes1 Rue de la No?44300 NantesFrance

The pollution and toxic greenhouse gases produced by fossil fuel combustion are troubling as global energy demand continues to *** mitigate the consequences of global warming,a transition to sustainable energy sources is *** manuscript presents a feasible community microgrid design in Hazaribagh,Dhaka based on meteorological data that leads to photovoltaic installation on the rooftop of a local community *** study shows a microgrid design of a system with the lowest cost of energy and a large renewable fraction,which is analysed using the HOMER Pro *** real-time data,analysis of the system cost,cost of energy,renewable fraction,unmet load,energy purchased and energy sold is discussed.A suitable case for electrification is also identified and presented for the selected *** proposed case yields a cost of energy of$0.0357/kWh,which is 52%less than the current tariff rate,with a 70%renewable *** study will provide people in this community with more green energy at a lower cost;in addition,this designed microgrid sells additional energy to the grid to avoid possible power *** potential for a positive energy community is also investigated in terms of energy consumption and renewable output of the planned microgrid.

关键词： cost of energy microgrid positive energy community renewable fraction

Integrating Deep Learning with Fundus and Optical Coherence Tomography for Cardiovascular Disease Prediction

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arXiv 2024年

作者： Maldonado-Garcia, Cynthia Zakeri, Arezoo Frangi, Alejandro F. Ravikumar, Nishant Centre for Computational Imaging and Simulation Technologies in Biomedicine School of Computing University of Leeds Leeds United Kingdom Division of Informatics Imaging and Data Sciences School of Health Sciences Faculty of Biology Medicine and Health University of Manchester Manchester United Kingdom School of Computer Science Faculty of Science and Engineering University of Manchester Kilburn Building Manchester United Kingdom Christabel Pankhurst Institute University of Manchester Manchester United Kingdom NIHR Manchester Biomedical Research Centre Manchester Academic Health Science Centre Manchester United Kingdom

Early identification of patients at risk of cardiovascular diseases (CVD) is crucial for effective preventive care, reducing healthcare burden, and improving patients’ quality of life. This study demonstrates the potential of retinal optical coherence tomography (OCT) imaging in conjunction with fundus photographs for identifying future adverse cardiac events. We utilized data from 977 patients who experienced CVD within a 5-year interval post-image acquisition, alongside 1,877 control participants without CVD, totaling 2,854 subjects. We propose a novel binary classification network based on a Multi-channel Variational Autoencoder (MCVAE), which learns a latent embedding of patients’ fundus and OCT images to classify individuals into two groups: those likely to develop CVD in the future and those who are not. Our model, trained on both imaging modalities, achieved promising results (AUROC 0.78 ± 0.02, accuracy 0.68 ± 0.002, precision 0.74 ± 0.02, sensitivity 0.73 ± 0.02, and specificity 0.68 ± 0.01), demonstrating its efficacy in identifying patients at risk of future CVD events based on their retinal images. This study highlights the potential of retinal OCT imaging and fundus photographs as cost-effective, non-invasive alternatives for predicting cardiovascular disease risk. The widespread availability of these imaging techniques in optometry practices and hospitals further enhances their potential for large-scale CVD risk screening. Our findings contribute to the development of standardized, accessible methods for early CVD risk identification, potentially improving preventive care strategies and patient outcomes. © 2024, CC BY-NC-ND.

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Mobility and the spatial spread of sars-cov-2 in Belgium

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arXiv 2022年

作者： Rollier, Michiel Miranda, Gisele H.B. Vergeynst, Jenna Meys, Joris Alleman, Tijs W. Baetens, Jan M. KERMIT Department of Data Analysis and Mathematical Modelling Ghent University Coupure Links 653 Ghent9000 Belgium BIOMATH Department of Data Analysis and Mathematical Modelling Ghent University Coupure Links 653 Ghent9000 Belgium Division of Computational Science and Technology KTH Royal Institute of Technology Tomtebodavägen 23A Solna17165 Sweden

We analyse and mutually compare time series of covid-19-related data and mobility data across Belgium's 43 arrondissements (NUTS 3). In this way, we reach three conclusions. First, we could detect a decrease in mobility during high-incidence stages of the pandemic. This is expressed as a significant change in the average amount of time spent outside one's home arrondissement, investigated over five distinct periods, and in more detail using an inter-arrondissement "connectivity index" (CI). Second, we analyse spatio-temporal covid-19-related hospitalisation time series, after smoothing them using a generalise additive mixed model (GAMM). We confirm that some arrondissements are ahead of others and morphologically dissimilar to others, in terms of epidemiological progression. The tools used to quantify this are time-lagged cross-correlation (TLCC) and dynamic time warping (DTW), respectively. Third, we demonstrate that an arrondissement's CI with one of the three identified first-outbreak arrondissements is correlated to a significant local excess mortality some five to six weeks after the first outbreak. More generally, we couple results leading to the first and second conclusion, in order to demonstrate an overall correlation between CI values on the one hand, and TLCC and DTW values on the other. We conclude that there is a strong correlation between physical movement of people and viral spread in the early stage of the sars-cov-2 epidemic in Belgium, though its strength weakens as the virus spreads. © 2022, CC BY-NC-SA.

关键词： Time series analysis