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Sinogram upsampling using Primal-Dual UNet for undersampled CT and radial MRI reconstruction

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arXiv 2021年

作者： Ernst, Philipp Chatterjee, Soumick Rose, Georg Speck, Oliver Nürnberger, Andreas Faculty of Computer Science Otto von Guericke University Magdeburg Germany Data and Knowledge Engineering Group Otto von Guericke University Magdeburg Germany Biomedical Magnetic Resonance Otto von Guericke University Magdeburg Germany Otto von Guericke University Research Campus STIMULATE Magdeburg Germany Genomics Research Centre Human Technopole Milan Italy Institute for Medical Engineering Otto von Guericke University Magdeburg Germany German Centre for Neurodegenerative Disease Magdeburg Germany Centre for Behavioural Brain Sciences Magdeburg Germany

Computed tomography (CT) and magnetic resonance imaging (MRI) are two widely used clinical imaging modalities for noninvasive diagnosis. However, both of these modalities come with certain problems. CT uses harmful ionising radiation, and MRI suffers from slow acquisition speed. Both problems can be tackled by undersampling, such as sparse sampling. However, such undersampled data leads to lower resolution and introduces artefacts. Several techniques, including deep learning based methods, have been proposed to reconstruct such data. However, the undersampled reconstruction problem for these two modalities was always considered as two different problems and tackled separately by different research works. This paper proposes a unified solution for both sparse CT and undersampled radial MRI reconstruction, achieved by applying Fourier transform-based pre-processing on the radial MRI and then finally reconstructing both modalities using sinogram upsampling combined with filtered back-projection. The Primal-Dual network is a deep learning based method for reconstructing sparsely-sampled CT data. This paper introduces Primal-Dual UNet, which improves the Primal-Dual network in terms of accuracy and reconstruction speed. The proposed method resulted in an average SSIM of 0.932±0.021 while performing sparse CT reconstruction for fan-beam geometry with a sparsity level of 16, achieving a statistically significant improvement over the previous model, which resulted in 0.919±0.016. Furthermore, the proposed model resulted in 0.903±0.019 and 0.957±0.023 average SSIM while reconstructing undersampled brain and abdominal MRI data with an acceleration factor of 16, respectively-statistically significant improvements over the original model, which resulted in 0.867±0.025 and 0.949±0.025. Finally, this paper shows that the proposed network not only improves the overall image quality, but also improves the image quality for the regions-of-interest: liver, kidneys, and spleen;as well

关键词： Image reconstruction

Collective fast delivery by energy-efficient agents 43

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Collective fast delivery by energy-efficient agents

43rd International Symposium on Mathematical Foundations of computer science, MFCS 2018

作者： Bärtschi, Andreas Graf, Daniel Mihalák, Matúš Department of Computer Science ETH Zürich Switzerland Department of Data Science and Knowledge Engineering Maastricht University Netherlands

ISBN: (纸本)9783959770866

We consider k mobile agents initially located at distinct nodes of an undirected graph (on n nodes, with edge lengths). The agents have to deliver a single item from a given source node s to a given target node t. The agents can move along the edges of the graph, starting at time 0, with respect to the following: Each agent i has a weight ωi that defines the rate of energy consumption while travelling a distance in the graph, and a velocity υi with which it can move. We are interested in schedules (operating the k agents) that result in a small delivery time T (time when the item arrives at t), and small total energy consumption E. Concretely, we ask for a schedule that: either (i) Minimizes T , (ii) Minimizes lexicographically (T, E) (prioritizing fast delivery), or (iii) Minimizes · T + (1 − ) · E, for a given ∈ (0, 1). We show that (i) is solvable in polynomial time, and show that (ii) is polynomial-time solvable for uniform velocities and solvable in time O(n + k log k) for arbitrary velocities on paths, but in general is NP-hard even on planar graphs. As a corollary of our hardness result, (iii) is NP-hard, too. We show that there is a 2-approximation algorithm for (iii) using a single agent. © Andreas Bärtschi, Daniel Graf, and Matúš Mihalák.

关键词： Energy utilization

Traffic4cast at NeurIPS 2021 – Temporal and Spatial Few-Shot Transfer Learning in Gridded Geo-Spatial Processes

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arXiv 2022年

作者： Eichenberger, Christian Neun, Moritz Martin, Henry Herruzo, Pedro Spanring, Markus Lu, Yichao Choi, Sungbin Konyakhin, Vsevolod Lukashina, Nina Shpilman, Aleksei Wiedemann, Nina Raubal, Martin Wang, Bo Vu, Hai L. Mohajerpoor, Reza Cai, Chen Kim, Inhi Hermes, Luca Melnik, Andrew Velioglu, Riza Vieth, Markus Schilling, Malte Bojesomo, Alabi Al Marzouqi, Hasan Liatsis, Panos Santokhi, Jay Hillier, Dylan Yang, Yiming Sarwar, Joned Jordan, Anna Hewage, Emil Jonietz, David Tang, Fei Gruca, Aleksandra Kopp, Michael Kreil, David Hochreiter, Sepp Vienna Austria Institute of Cartography and Geoinformation ETH Zurich Switzerland Layer 6 AI Toronto Canada ITMO University Saint Petersburg Russia JetBrains Research Saint Petersburg Russia HSE University Saint Petersburg Russia Institute of Transport Studies Monash University ClaytonVIC Australia CSIRO’s Data61 Eveleigh Australia Institute Civil and Environmental Engineering Department Kongju National University Korea Republic of Machine Learning & Neuroinformatics Group Bielefeld University Germany Electrical Engineering and Computer Science Department Khalifa University Abu Dhabi United Arab Emirates Alchera Data Technologies Ltd Cambridge United Kingdom HERE Technologies Zurich Switzerland Silesian University of Technology Gliwice Poland Machine Learning Institute Johannes Kepler University Linz Austria

The IARAI Traffic4cast competitions at NeurIPS 2019 and 2020 showed that neural networks can successfully predict future traffic conditions 1 hour into the future on simply aggregated GPS probe data in time and space bins. We thus reinterpreted the challenge of forecasting traffic conditions as a movie completion task. U-Nets proved to be the winning architecture, demonstrating an ability to extract relevant features in this complex real-world geo-spatial process. Building on the previous competitions, Traffic4cast 2021 now focuses on the question of model robustness and generalizability across time and space. Moving from one city to an entirely different city, or moving from pre-COVID times to times after COVID hit the world thus introduces a clear domain shift. We thus, for the first time, release data featuring such domain shifts. The competition now covers ten cities over 2 years, providing data compiled from over 1012 GPS probe data. Winning solutions captured traffic dynamics sufficiently well to even cope with these complex domain shifts. Surprisingly, this seemed to require only the previous 1h traffic dynamic history and static road graph as input. © 2022, CC BY-NC-SA.

关键词： Probes

Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence

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arXiv 2025年

作者： Sun, Yingying Jun, A. Liu, Zhiwei Sun, Rui Qian, Liujia Payne, Samuel H. Bittremieux, Wout Ralser, Markus Li, Chen Chen, Yi Dong, Zhen Perez-Riverol, Yasset Khan, Asif Sander, Chris Aebersold, Ruedi Vizcaíno, Juan Antonio Krieger, Jonathan R. Yao, Jianhua Wen, Han Zhang, Linfeng Zhu, Yunping Xuan, Yue Sun, Benjamin Boyang Qiao, Liang Hermjakob, Henning Tang, Haixu Gao, Huanhuan Deng, Yamin Zhong, Qing Chang, Cheng Bandeira, Nuno Li, Ming Weinan, E. Sun, Siqi Yang, Yuedong Omenn, Gilbert S. Zhang, Yue Xu, Ping Fu, Yan Liu, Xiaowen Overall, Christopher M. Wang, Yu Deutsch, Eric W. Chen, Luonan Cox, Jürgen Demichev, Vadim He, Fuchu Huang, Jiaxing Jin, Huilin Liu, Chao Li, Nan Luan, Zhongzhi Song, Jiangning Yu, Kaicheng Wan, Wanggen Wang, Tai Zhang, Kang Zhang, Le Bell, Peter A. Mann, Matthias Zhang, Bing Guo, Tiannan Affiliated Hangzhou First People’s Hospital State Key Laboratory of Medical Proteomics School of Medicine Westlake University Zhejiang Province Hangzhou China Westlake Center for Intelligent Proteomics Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Province Hangzhou China Biology Department Brigham Young University ProvoUT84602 United States Department of Computer Science University of Antwerp Antwerp2020 Belgium Department of Biochemistry CharitéUniversitätsmedizin Berlin Berlin Germany Biomedicine Discovery Institute Department of Biochemistry and Molecular Biology Monash University MelbourneVICVIC 3800 Australia Wellcome Genome Campus Hinxton CambridgeCB10 1SD United Kingdom Harvard Medical School Ludwig Center at Harvard United States Harvard Medical School Broad Institute Ludwig Center at Harvard Dana-Farber Cancer Institute United States Department of Biology Institute of Molecular Systems Biology ETH Zürich Zürich Switzerland Bruker Ltd. MiltonONL9T 6P4 Canada AI for Life Sciences Lab Tencent Shenzhen518057 China State Key Laboratory of Medical Proteomics AI for Science Institute Beijing100080 China Beijing Institute of Lifeomics Beijing102206 China Thermo Fisher Scientific GmbH Hanna-Kunath Str. 11 Bremen28199 Germany Informatics and Predictive Sciences Research Bristol Myers Squibb United States Department of Chemistry Fudan University Songhu Road 2005 Shanghai200438 China Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University IN47408 United States ProCan® Children’s Medical Research Institute Faculty of Medicine and Health The University of Sydney WestmeadNSW Australia La Jolla CA United States Central China Institute of Artificial Intelligence University of Waterloo Canada AI for Science Institute Center for Machine Learning Research School of Mathematical Sciences Peking University China Research Institute of Intelligent Complex Systems Fudan U

Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI techniques, are unlocking new challenges and opportunities in biological discovery. Here, we highlight key areas where AI is driving innovation, from data analysis to new biological insights. These include developing an AI-friendly ecosystem for proteomics data generation, sharing, and analysis;improving peptide and protein identification and quantification;characterizing protein-protein interactions and protein complexes;advancing spatial and perturbation proteomics;integrating multi-omics data;and ultimately enabling AI-empowered virtual cells. © 2025, CC BY.

关键词： Biotic

A deep belief network-based method to identify proteomic risk markers for Alzheimer’s disease

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arXiv 2020年

作者： An, Ning Jin, Liuqi Ding, Huitong Yang, Jiaoyun Yuan, Jing Key Laboratory of Knowledge Engineering Big Data of Ministry of Education Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Department of Neurology Peking Union Medical College Hospital Chinese Academy of Medical Sciences Beijing China

While a large body of research has formally identified apolipoprotein E (APOE) as a major genetic risk marker for Alzheimer’s disease, accumulating evidence supports the notion that other risk markers may exist. The traditional Alzheimer’s-specific signature analysis methods, however, have not been able to make full use of rich protein expression data, especially the interaction between attributes. This paper develops a novel feature selection method to identify pathogenic factors of Alzheimer’s disease using the proteomic and clinical data. This approach has taken the weights of network nodes as the importance order of signaling protein expression values. After generating and evaluating the candidate subset, the method helps to select an optimal subset of proteins that achieved an accuracy greater than 90%, which is superior to traditional machine learning methods for clinical Alzheimer’s disease diagnosis. Besides identifying a proteomic risk marker and further reinforce the link between metabolic risk factors and Alzheimer’s disease, this paper also suggests that apidonectin-linked pathways are a possible therapeutic drug target. Copyright © 2020, The Authors. All rights reserved.

关键词： Feature extraction

Workload Allocation Based on User Mobility in Mobile Edge Computing

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Journal on Big data 2020年第3期2卷 105-115页

作者： Tengfei Yang Xiaojun Shi Yangyang Li Binbin Huang Haiyong Xie Yanting Shen National Engineering Laboratory for Public Safety Risk Perception and Control by Big Data(NEL-PSRPC) BeijingChina National Computer Network Emergency Response Technical Team Coordination Center of China BeijingChina Department of Science and Technology China Electronics Technology Group CorporationBeijingChina School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhouChina University of Science and Technology of China HefeiChina

Mobile Edge Computing(MEC)has become the most possible network architecture to realize the vision of interconnection of all *** offloading compute-intensive or latency-sensitive applications to nearby small cell base stations(sBSs),the execution latency and device power consumption can be reduced on resource-constrained mobile ***,computation delay of Mobile Edge Network(MEN)tasks are neglected while the unloading decision-making is studied in *** this paper,we propose a workload allocation scheme which combines the task allocation optimization of mobile edge network with the actual user behavior activities to predict the task allocation of single *** obtain the next possible location through the user's past location information,and receive the next access server according to the grid ***,the next time task sequence is calculated on the base of the historical time task sequence,and the server is chosen to preload the *** the experiments,the results demonstrate a high accuracy of our proposed model.

关键词： Edge computing workload allocation social-LSTM context-sensitive user mobility

BraTS-Path Challenge: Assessing Heterogeneous Histopathologic Brain Tumor Sub-regions

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arXiv 2024年

作者： Bakas, Spyridon Thakur, Siddhesh P. Faghani, Shahriar Moassefi, Mana Baid, Ujjwal Chung, Verena Pati, Sarthak Innani, Shubham Baheti, Bhakti Albrecht, Jake Karargyris, Alexandros Kassem, Hasan Nasrallah, MacLean P. Ahrendsen, Jared T. Barresi, Valeria Gubbiotti, Maria A. Lpez, Giselle Y. Lucas, Calixto-Hope G. Miller, Michael L. Cooper, Lee A.D. Huse, Jason T. Bell, William R. Department of Pathology and Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Department of Neurological Surgery Indiana University School of Medicine IndianapolisIN United States Department of Radiology and Imaging Sciences Indiana University School of Medicine IndianapolisIN United States Department of Biostatistics and Health Data Science Indiana University School of Medicine IndianapolisIN United States Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University IndianapolisIN United States Medical Research Group MLCommons San FranciscoCA United States Department of Radiology Mayo Clinic MN United States Sage Bionetworks United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology Feinberg School of Medicine Northwestern University ChicagoIL United States Department of Diagnostics and Public Health University of Verona Verona Italy Division of Pathology and Laboratory Medicine Department of Anatomical Pathology University of Texas MD Anderson Cancer Center TX United States The Preston Robert Tisch Brain Tumor Center at Duke DurhamNC United States Department of Pathology Duke University School of Medicine DurhamNC United States Department of Neurosurgery Duke University Medical School DurhamNC United States Division of Neuropathology Department of Pathology Johns Hopkins University School of Medicine BaltimoreMD United States Department of Pathology and Cell Biology Columbia University Irving Medical Center New YorkNY United States Department of Translational Molecular Pathology University of Texas MD Anderson Cancer Center TX United States

Glioblastoma is the most common primary adult brain tumor, with a grim prognosis - median survival of 12-18 months following treatment, and 4 months otherwise. Glioblastoma is widely infiltrative in the cerebral hemispheres and well-defined by heterogeneous molecular and micro-environmental histopathologic profiles, which pose a major obstacle in treatment. Correctly diagnosing these tumors and assessing their heterogeneity is crucial for choosing the precise treatment and potentially enhancing patient survival rates. In the gold-standard histopathology-based approach to tumor diagnosis, detecting various morpho-pathological features of distinct histology throughout digitized tissue sections is crucial. Such "features" include the presence of cellular tumor, geographic necrosis, pseudopalisading necrosis, areas abundant in microvascular proliferation, infiltration into the cortex, wide extension in subcortical white matter, leptomeningeal infiltration, regions dense with macrophages, and the presence of perivascular or scattered lymphocytes. With these features in mind and building upon the main aim of the BraTS Cluster of Challenges https://***/brats2024, the goal of the BraTS-Path challenge is to provide a systematically prepared comprehensive dataset and a benchmarking environment to develop and fairly compare deep-learning models capable of identifying tumor sub-regions of distinct histologic profile. These models aim to further our understanding of the disease and assist in the diagnosis and grading of conditions in a consistent manner. © 2024, CC BY.

关键词： Tumors

Chemically inspired Erdős-Rényi oriented hypergraphs

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arXiv 2023年

作者： Garcia-Chung, Angel Bermúdez-Montaña, Marisol Stadler, Peter F. Jost, Jürgen Restrepo, Guillermo Tecnológico de Monterrey Escuela de Ingeniería y Ciencias Estado de México52926 Mexico Max Planck Institute for Mathematics in the Sciences Leipzig Germany Interdisciplinary Center for Bioinformatics Universität Leipzig Leipzig Germany Bioinformatics Group Department of Computer Science Universität Leipzig Leipzig Germany Institute for Theoretical Chemistry University of Vienna Vienna Austria The Santa Fe Institute Santa FeNM United States Facultad de Ciencias Universidad Nacional de Colombia Bogotá Colombia Center for Scalable Data Analytics and Artificial Intelligence Universität Leipzig Leipzig Germany School of Applied Sciences and Engineering EAFIT University Medellín Colombia

High-order structures have been recognised as suitable models for systems going beyond the binary relationships for which graph models are appropriate. Despite their importance and surge in research on these structures, their random cases have been only recently become subjects of interest. One of these high-order structures is the oriented hypergraph, which relates couples of subsets of an arbitrary number of vertices. Here we develop the Erdős-Rényi model for oriented hypergraphs, which corresponds to the random realisation of oriented hyperedges of the complete oriented hypergraph. A particular feature of random oriented hypergraphs is that the ratio between their expected number of oriented hyperedges and their expected degree or size is 3/2 for large number of vertices. We highlight the suitability of oriented hypergraphs for modelling large collections of chemical reactions and the importance of random oriented hypergraphs to analyse the unfolding of chemistry. © 2023, CC BY-SA.

关键词： Graph theory

Deep learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge

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arXiv 2021年

作者： Lalande, Alain Chen, Zhihao Pommier, Thibaut Decourselle, Thomas Qayyum, Abdul Salomon, Michel Ginhac, Dominique Skandarani, Youssef Boucher, Arnaud Brahim, Khawla de Bruijne, Marleen Camarasa, Robin Correia, Teresa M. Feng, Xue Girum, Kibrom B. Hennemuth, Anja Huellebrand, Markus Hussain, Raabid Ivantsits, Matthias Ma, Jun Meyer, Craig Sharma, Rishabh Shi, Jixi Tsekos, Nikolaos V. Varela, Marta Wang, Xiyue Yang, Sen Zhang, Hannu Zhang, Yichi Zhou, Yuncheng Zhuang, Xiahai Couturier, Raphael Meriaudeau, Fabrice ImViA laboratory University of Burgundy Dijon France MRI department University Hospital of Dijon Dijon France Femto-ST laboratory University of Franche-Comté Belfort France Cardiology department University Hospital of Dijon Dijon France CASIS Company Quetigny France National Engineering School of Sousse University of Sousse Sousse Tunisia LASEE laboratory National Engineering School of Monastir University of Monastir Monastir Tunisia Biomedical Imaging Group Rotterdam Erasmus MC Rotterdam Netherlands Department of Radiology and Nuclear Medicine Erasmus MC Rotterdam Netherlands Department of Computer Science University of Copenhagen Copenhagen Denmark Centre of Marine Sciences University of Algarve Faro Portugal School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom Department of Biomedical Engineering University of Virginia Charlottesville United States Charité – Universitätsmedizin Berlin Berlin Germany Fraunhofer MEVIS Bremen Germany German Centre for Cardiovascular Research Berlin Germany Department of Mathematics Nanjing University of Science and Technology Nanjing China Data Analysis and Intelligent Systems Lab Department of Computer Science University of Houston Houston United States Medical Robotics and Imaging Lab Department of Computer Science University of Houston Houston United States National Heart and Lung Institute Imperial College London London United Kingdom College of Computer Science Sichuan University Chengdu China College of Biomedical Engineering Sichuan University Chengdu China School of Biological Science and Medical Engineering Beihang University Beijing China School of Data Science Fudan University Shanghai China

A key factor for assessing the state of the heart after myocardial infarction (MI) is to measure whether the myocardium segment is viable after reperfusion or revascularization therapy. Delayed enhancement-MRI or DE-MRI, which is performed several minutes after injection of the contrast agent, provides high contrast between viable and nonviable myocardium and is therefore a method of choice to evaluate the extent of MI. To automatically assess myocardial status, the results of the EMIDEC challenge that focused on this task are presented in this paper. The challenge’s main objectives were twofold. First, to evaluate if deep learning methods can distinguish between normal and pathological cases. Second, to automatically calculate the extent of myocardial infarction. The publicly available database consists of 150 exams divided into 50 cases with normal MRI after injection of a contrast agent and 100 cases with myocardial infarction (and then with a hyperenhanced area on DE-MRI), whatever their inclusion in the cardiac emergency department. Along with MRI, clinical characteristics are also provided. The obtained results issued from several works show that the automatic classification of an exam is a reachable task (the best method providing an accuracy of 0.92), and the automatic segmentation of the myocardium is possible. However, the segmentation of the diseased area needs to be improved, mainly due to the small size of these areas and the lack of contrast with the surrounding structures. © 2021, CC BY-NC-SA.

关键词： Cardiology