检索结果-内蒙古大学图书馆

arXiv 2021年

作者： Banerjee, I. Bhimireddy, A.R. Burns, J.L. Celi, L.A. Chen, L. Correa, R. Dullerud, N. Ghassemi, M. Gichoya, J.W. Huang, S. Kuo, P. Lungren, M.P. Price, B.J. Purkayastha, S. Pyrros, A.A. Oakden-Rayner, L. Okechukwu, C. Seyyed-Kalantari, L. Trivedi, H. Wang, R. Zaiman, Z. Zhang, H. Department of Biomedical Informatics Emory University 100 Woodruff Circle AtlantaGA30322 United States School of Informatics and Computing Indiana University Purdue University 535 W Michigan St. IT 475E IndianapolisIN46202 United States Institute for Medical Engineering and Science Massachusetts Institute of Technology 45 Carleton Street E25- 330 CambridgeMA02142 United States Department of Medicine Beth Israel Deaconess Medical Center 330 Brookline Avenue BostonMA02215 United States Department of Computer Science National Tsing Hua University No. 101 Section 2 Kuang-Fu Road Hsinchu30013 Taiwan Department of Computer Science Emory University 201 Dowman Drive AtlantaGA30322 United States Department of Computer Science University of Toronto 214 College St TorontoONM5T 3A1 Canada Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States Department of Radiology Emory University 1364 Clifton Rd AtlantaGA30322 United States Stanford University School of Medicine 291 Campus Drive Palo AltoCA94305 United States Florida State University College of Medicine 1115 W Call St TallahasseeFL32304 United States Australian Institute for Machine Learning University of Adelaide AIML Building Lot Fourteen Cnr North Terrace and Frome Road AdelaideSA5000 Australia Department of Computer Science Georgia Institute of Technology 266 Ferst Drive AtlantaGA30332 United States Lunenfeld-Tanenbaum Research Institute Sinai Health 600 University Ave TorontoONM5G 1X5 Canada Dupage Medical Group 40 S Clay St HinsdaleIL60521 United States

Background: In medical imaging, prior studies have demonstrated disparate AI performance by race, yet there is no known correlation for race on medical imaging that would be obvious to the human expert interpreting the images. We conduct a comprehensive evaluation of AI's ability to recognize patient's racial identity from medical images. Methods: Using private and public datasets we evaluate: A) performance quantification of deep learning models to detect race from medical images, including the ability of these models to generalize to external environments and across multiple imaging modalities, B) assessment of possible confounding anatomic and phenotype population features, such as disease distribution and body habitus as predictors of race, and C) investigation into the underlying mechanism by which AI models can recognize race. Findings: Standard deep learning models can be trained to predict race from medical images with high performance across multiple imaging modalities. Our findings hold under external validation conditions, as well as when models are optimized to perform clinically motivated tasks. We demonstrate this detection is not due to trivial proxies or imaging-related surrogate covariates for race, such as underlying disease distribution. Finally, we show that performance persists over all anatomical regions and frequency spectrum of the images suggesting that mitigation efforts will be challenging and demand further study. Interpretation: We emphasize that model ability to predict self-reported race is itself not the issue of importance. However, our findings that AI can trivially predict self-reported race - even from corrupted, cropped, and noised medical images - in a setting where clinical experts cannot, creates an enormous risk for all model deployments in medical imaging: if an AI model secretly used its knowledge of self-reported race to misclassify all Black patients, radiologists would not be able to tell using the same data the model ha

关键词： Medical imaging

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26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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Mapping the Human Body at Cellular Resolution -- The NIH Common Fund Human BioMolecular Atlas Program

arXiv

引用

arXiv 2019年

作者： Snyder, Michael P. Lin, Shin Posgai, Amanda Atkinson, Mark Regev, Aviv Rood, Jennifer Rozenblatt-Rosen, Orit Gaffney, Leslie Hupalowska, Anna Satija, Rahul Gehlenborg, Nils Shendure, Jay Laskin, Julia Harbury, Pehr Nystrom, Nicholas A. Bar-Joseph, Ziv Zhang, Kun Börner, Katy Lin, Yiing Conroy, Richard Procaccini, Dena Roy, Ananda L. Pillai, Ajay Brown, Marishka Galis, Zorina S. Cai, Long Shendure, Jay Trapnell, Cole Lin, Shin Jackson, Dana Snyder, Michael P. Nolan, Garry Greenleaf, William James Lin, Yiing Plevritis, Sylvia Ahadi, Sara Lee, Hayan Nevins, Stephanie Esplin, Ed Horning, Aaron Bahmani, Amir Zhang, Kun Sun, Xin Jain, Sanjay Hagood, James Kharchenko, Peter Atkinson, Mark Bodenmiller, Bernd Brusko, Todd Clare-Salzler, Michael Nick, Harry Otto, Kevin Posgai, Amanda Wasserfall, Clive Maffioletti, Sergio Caprioli, Richard M. Spraggins, Jeffrey M. Gutierrez, Danielle Patterson, Nathan Heath Neumann, Elizabeth K. Harris, Raymond deCaestecker, Mark de Plas, Raf Van Lau, Ken Cai, Long Yuan, Guo-Cheng Zhu, Qian Dries, Ruben Yin, Peng Saka, Sinem K. Kishi, Jocelyn Y. Wang, Yu Goldaracena, Isabel Laskin, Julia Ye, DongHye Burnum-Johnson, Kristin E. Piehowski, Paul D. Ansong, Charles Zhu, Ying Harbury, Pehr Desai, Tushar Mulye, Jay Chou, Peter Nagendran, Monica Bar-Joseph, Ziv Teichmann, Sarah A. Paten, Benedict Murphy, Robert F. Ma, Jian Kiselev, Vladimir Yu Kingsford, Carl Ricarte, Allyson Ruffalo, Matthew Gehlenborg, Nils Kharchenko, Peter Vella, Margaret McCallum, Chuck Börner, Katy Cross, Leonard E. Friedman, Samuel H. Heiland, Randy Herr, Bruce Macklin, Paul Quardokus, Ellen M. Record, Lisel Sluka, James P. Weber, Griffin M. Nystrom, Nicholas A. Silverstein, Jonathan C. Blood, Philip D. Ropelewski, Alexander J. Shirey, William E. Mabee, Paula Lenhardt, W. Christopher Robasky, Kimberly Michailidis, Stavros Satija, Rahul Marioni, John Regev, Aviv Butler, Andrew Stuart, Tim Fisher, Eyal Ghazanfar, Shila Rood, Jennifer Gaffney, Leslie Ersalan, Gokcen Biancalani, Tommaso Conroy, Richard Procaccini, Dena Roy, Anan School of Medicine Stanford University 300 Pasteur Drive United States Department of Genetics Stanford School of Medicine StanfordCA94305 United States Department of Medicine University of Washington SeattleWA98195 United States Department of Pathology University of Florida Diabetes Institute GainesvilleFL32610 United States Klarman Cell Observatory Broad Institute of MIT and Harvard CambridgeMA02142 United States Howard Hughes Medical Institute Koch Institute of Integrative Cancer Research Department of Biology Massachusetts Institute of Technology CambridgeMA02140 United States New York Genome Center New York CityNY10013 United States New York University New YorkNY10012 United States Department of Biomedical Informatics Harvard Medical School BostonMA02115 United States Department of Genome Sciences University of Washington SeattleWA98105 United States Department of Chemistry Purdue University West LafayetteIN47907 United States Department of Biochemistry Stanford University School of Medicine StanfordCA94305 United States Pittsburgh Supercomputing Center Carnegie Mellon University PittsburghPA15213 United States Department of Computational Biology School of Computer Science Carnegie Mellon University PittsburghPA15213 United States Department of Bioengineering University of California San Diego La JollaCA92093 United States Department of Intelligent Systems Engineering School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States Department of Surgery Washington University School of Medicine St LouisMO63110 United States Division of Program Coordination Planning and Strategic Initiatives National Institutes of Health BethesdaMD20892 United States National Human Genome Research Institute National Institutes of Health BethesdaMD20892 United States National Heart Lung and Blood Institute National Institutes of Health BethesdaMD20892 United States Caltech-UW TMC Departmen

Transformative technologies are enabling the construction of three dimensional (3D) maps of tissues with unprecedented spatial and molecular resolution. Over the next seven years, the NIH Common Fund Human Biomolecular Atlas Program (HuBMAP) intends to develop a widely accessible framework for comprehensively mapping the human body at single-cell resolution by supporting technology development, data acquisition, and detailed spatial mapping. HuBMAP will integrate its efforts with other funding agencies, programs, consortia, and the biomedical research community at large towards the shared vision of a comprehensive, accessible 3D molecular and cellular atlas of the human body, in health and various disease settings. Copyright © 2019, The Authors. All rights reserved.

关键词： Mapping

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Advances in quantum cryptography

arXiv

引用

arXiv 2019年

作者： Pirandola, S. Andersen, U.L. Banchi, L. Berta, M. Bunandar, D. Colbeck, R. Englund, D. Gehring, T. Lupo, C. Ottaviani, C. Pereira, J. Razavi, M. Shaari, J.S. Tomamichel, M. Usenko, V.C. Vallone, G. Villoresi, P. Wallden, P. Computer Science and York Centre for Quantum Technologies University of York YorkYO10 5GH United Kingdom CambridgeMA02139 United States Department of Physics Technical University of Denmark Fysikvej 2800 Kgs. Lyngby2800 Denmark I-50019 Italy Department of Computing Imperial College Kensington LondonSW7 2AZ United Kingdom Department of Mathematics University of York YorkYO10 5DD United Kingdom Department of Physics and Astronomy University of Sheffield SheffieldS3 7RH United Kingdom School of Electronic and Electrical Engineering University of Leeds LeedsLS2 9JT United Kingdom Jalan Sultan Ahmad Shah Kuantan Pahang25200 Malaysia University Putra Malaysia 43400 UPM Serdang Selangor Malaysia Centre for Quantum Software and Information School of Software University of Technology Sydney SydneyNSW2007 Australia Department of Optics Palacky University 17. listopadu 50 Olomouc772 07 Czech Republic Dipartimento di Ingegneria dell’Informazione Universitá degli Studi di Padova Via Gradenigo 6B Padova35131 Italy School of Informatics University of Edinburgh 10 Crichton Street EdinburghEH8 9AB United Kingdom

Quantum cryptography is arguably the fastest growing area in quantum information science. Novel theoretical protocols are designed on a regular basis, security proofs are constantly improving, and experiments are gradually moving from proof-of-principle lab demonstrations to in-field implementations and technological prototypes. In this review, we provide both a general introduction and a state of the art description of the recent advances in the field, both theoretically and experimentally. We start by reviewing protocols of quantum key distribution based on discrete variable systems. Next we consider aspects of device independence, satellite challenges, and high rate protocols based on continuous variable systems. We will then discuss the ultimate limits of point-to-point private communications and how quantum repeaters and networks may overcome these restrictions. Finally, we will discuss some aspects of quantum cryptography beyond standard quantum key distribution, including quantum data locking and quantum digital signatures. Copyright © 2019, The Authors. All rights reserved.

关键词： Quantum cryptography

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The breakthrough listen search for intelligent life near the Galactic Center I

arXiv

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arXiv 2021年

作者： Gajjar, Vishal Perez, Karen I. Siemion, Andrew P.V. Foster, Griffin Brzycki, Bryan Chatterjee, Shami Chen, Yuhong Cordes, James M. Croft, Steve Czech, Daniel DeBoer, David DeMarines, Julia Drew, Jamie Gowanlock, Michael Isaacson, Howard Lacki, Brian C. Lebofsky, Matt MacMahon, David H.E. Morrison, Ian S. Ng, Cherry de Pater, Imke Price, Danny C. Sheikh, Sofia Z. Suresh, Akshay Webb, Claire Worden, S. Pete Department of Astronomy University of California Berkeley BerkeleyCA94720 United States Department of Astronomy Columbia University 550 West 120th Street New YorkNY10027 United States Department of Astrophysics IMAPP Radboud University Nijmegen Netherlands SETI Institute Mountain ViewCA United States University of Malta Institute of Space Sciences and Astronomy Cornell Center for Astrophysics and Planetary Science Department of Astronomy Cornell University IthacaNY14853 United States Breakthrough Initiatives NASA Research Park Bld. 18 Moffett FieldCA94035 United States School of Informatics Computing and Cyber Systems Northern Arizona University FlagstaffAZ86011 United States University of Southern Queensland ToowoombaQLD4350 Australia International Centre for Radio Astronomy Research Curtin Institute of Radio Astronomy Curtin University PerthWA6845 Australia Department of Astronomy and Astrophysics Pennsylvania State University University ParkPA16802 United States Massachusetts Institute of Technology CambridgeMA United States

A line-of-sight towards the Galactic Center (GC) offers the largest number of potentially habitable systems of any direction in the sky. The Breakthrough Listen program is undertaking the most sensitive and deepest targeted SETI surveys towards the GC. Here, we outline our observing strategies with Robert C. Byrd Green Bank Telescope (GBT) and Parkes telescope to conduct 600 hours of deep observations across 0.7–93 GHz. We report preliminary results from our survey for ETI beacons across 1–8 GHz with 7.0 and 11.2 hours of observations with Parkes and GBT, respectively. With our narrowband drifting signal search, we were able to place meaningful constraints on ETI transmitters across 1–4 GHz and 3.9–8 GHz with EIRP limits of ≥4×1018 W among 60 million stars and ≥5×1017 W among half a million stars, respectively. For the first time, we were able to constrain the existence of artificially dispersed transient signals across 3.9–8 GHz with EIRP ≥1×1014 W/Hz with a repetition period ≤4.3 hours. We also searched our 11.2 hours of deep observations of the GC and its surrounding region for Fast Radio Burst-like magnetars with the DM up to 5000 pc cm−3 with maximum pulse widths up to 90 ms at 6 GHz. We detected several hundred transient bursts from SGR J1745−2900, but did not detect any new transient burst with the peak luminosity limit across our observed band of ≥1031 erg s−1 and burst-rate of ≥0.23 burst-hr−1. These limits are comparable to bright transient emission seen from other Galactic radio-loud magnetars, constraining their presence at the GC. Copyright © 2021, The Authors. All rights reserved.

关键词： Surveys

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

arXiv

引用

arXiv 2021年

作者： Nakaji, Kouhei Uno, Shumpei Suzuki, Yohichi Raymond, Rudy Onodera, Tamiya Tanaka, Tomoki Tezuka, Hiroyuki Mitsuda, Naoki Yamamoto, Naoki Graduate School of Science and Technology Keio University 3-14-1 Hiyoshi Kohoku-ku Kanagawa Yokohama223-8522 Japan Quantum Computing Center Keio University 3-14-1 Hiyoshi Kohoku-ku Kanagawa Yokohama223-8522 Japan Mizuho Research & Technologies Ltd. 2-3 Kanda-Nishikicho Chiyoda-ku Tokyo101-8443 Japan IBM Quantum IBM Research-Tokyo 19-21 Nihonbashi Hakozaki-cho Chuo-ku Tokyo103-8510 Japan Mitsubishi UFJ Financial Group Inc. MUFG Bank Ltd. 2-7-1 Marunouchi Chiyoda-ku Tokyo100-8388 Japan Sony Group Corporation 1-7-1 Konan Minato-ku Tokyo108-0075 Japan Sumitomo Mitsui Trust Bank Ltd. 1-4-1 Marunouchi Chiyoda-ku Tokyo100-8233 Japan Department of Applied Physics and Physico-Informatics Keio University Hiyoshi 3-14-1 Kohoku-ku Yokohama223-8522 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 only load 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 dataset 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 timeseries 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. Copyright © 2021, The Authors. All rights reserved.

关键词： Parameter estimation

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SMILE-UHURA Challenge - Small Vessel Segmentation at Mesoscopic Scale from Ultra-High Resolution 7T Magnetic Resonance Angiograms

arXiv

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arXiv 2024年

作者： Chatterjee, Soumick Mattern, Hendrik Dörner, Marc Sciarra, Alessandro Dubost, Florian Schnurre, Hannes Khatun, Rupali Yu, Chun-Chih Hsieh, Tsung-Lin Tsai, Yi-Shan Fang, Yi-Zeng Yang, Yung-Ching Huang, Juinn-Dar Xu, Marshall Liu, Siyu Ribeiro, Fernanda L. Bollmann, Saskia Chintalapati, Karthikesh Varma Radhakrishna, Chethan Mysuru Ram Kumar, Sri Chandana Hudukula Sutrave, Raviteja Qayyum, Abdul Mazher, Moona Razzak, Imran Rodero, Cristobal Niederer, Steven Lin, Fengming Xia, Yan Wang, Jiacheng Qiu, Riyu Wang, Liansheng Panah, Arya Yazdan Jurdi, Rosana El Fu, Guanghui Arslan, Janan Vaillant, Ghislain Valabregue, Romain Dormont, Didier Stankoff, Bruno Colliot, Olivier Vargas, Luisa Chacón, Isai Daniel Pitsiorlas, Ioannis Arbeláez, Pablo Zuluaga, Maria A. Schreiber, Stefanie Speck, Oliver Nürnberger, Andreas Faculty of Computer Science Otto von Guericke University Magdeburg Magdeburg Germany Data and Knowledge Engineering Group Otto von Guericke University Magdeburg Magdeburg Germany Genomics Research Centre Human Technopole Milan Italy Biomedical Magnetic Resonance Otto von Guericke University Magdeburg Magdeburg Germany Department of Neurology Medical Faculty University Hospital of Magdeburg Magdeburg Germany German Centre for Neurodegenerative Diseases Magdeburg Germany Centre for Behavioural Brain Sciences Magdeburg Germany University Hospital Zurich University of Zurich Zurich Switzerland Google Inc. United States Translational Radiobiology Department of Radiation Oncology Universitätsklinikum Erlangen Friedrich-Alexnder-Universität Erlangen-Nürnberg Erlangen Germany National Yang Ming Chiao Tung University Hsinchu Taiwan School of Electrical Engineering and Computer Science University of Queensland Brisbane Australia Australian eHealth Research Centre Chile National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Computer Science and Engineering University of New South Wales Sydney Australia Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates The Alan Turing Institute London United Kingdom School of Computing University of Leeds Leeds United Kingdom Department of Computer Science School of Informatics Xiamen University Xiamen China Manteia Technologies Co. Ltd Xiamen China Leicester International Institute Dalian University of Technology Dalian China Sorbonne Université Institut du Cerveau - Paris Brain Institute ICM CNRS Inria Inserm AP-HP Hôpital de la Pitié Salpêtrière ParisF-75013 France Centre of Formation and Research in Artificial Intelligence Universidad de Los Andes Colombia Data Science Department EURECOM Sophia Antipolis Franc

The human brain receives nutrients and oxygen through an intricate network of blood vessels. Pathology affecting small vessels, at the mesoscopic scale, represents a critical vulnerability within the cerebral blood supply and can lead to severe conditions, such as Cerebral Small Vessel Diseases. The advent of 7 Tesla MRI systems has enabled the acquisition of higher spatial resolution images, making it possible to visualise such vessels in the brain. However, the lack of publicly available annotated datasets has impeded the development of robust, machine learning-driven segmentation algorithms. To address the complexities of mesoscopic vessel segmentation and to highlight the need for advanced techniques to manage the high noise levels and poor vessel-to-background contrast inherent in"ultra-high-resolution" data, the SMILE-UHURA challenge was organised. This challenge, held in conjunction with the ISBI 2023, in Cartagena de Indias, Colombia, aimed to provide a platform for researchers working on related topics. The SMILE-UHURA challenge addresses the gap in publicly available annotated datasets by providing an annotated dataset of Time-of-Flight angiography acquired with 7T MRI. This dataset was created through a combination of automated pre-segmentation and extensive manual refinement. In this manuscript, sixteen submitted methods and two baseline methods are compared both quantitatively and qualitatively on two different datasets: held-out test MRAs from the same dataset as the training data (with labels kept secret) and a separate 7T ToF MRA dataset where both input volumes and labels are kept secret. The results demonstrate that most of the submitted deep learning methods, trained on the provided training dataset, achieved reliable segmentation performance. Dice scores reached up to 0.838 ± 0.066 and 0.716 ± 0.125 on the respective datasets, with an average performance of up to 0.804 ± 0.15. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

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Joint segmentation and deformable registration with the presence of the noise using active contour and linear curvature

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AIP Conference Proceedings 2024年第1期3150卷

作者： Nurul Asyiqin Mohd Fauzi Mazlinda Ibrahim Hoo Yann Seong Abdul Kadir Jumaat Lavdie Rada Faculty of Defence Science and Technology National Defence University of Malaysia 57000 Kuala Lumpur Malaysia Centre for Defence Foundation Studies National Defence University of Malaysia 57000 Kuala Lumpur Malaysia School of Mathematical Sciences College of Computing Informatics and Media Universiti Teknologi MARA (UiTM) 40450 Shah Alam Selangor Malaysia Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Universiti Teknologi MARA (UiTM) 40450 Shah Alam Selangor Malaysia Biomedical Engineering Department Bahcesehir University Besiktas Istanbul Turkey

Medical image processing requires image segmentation and image registration as important diagnostic tools. Image segmentation aims to split structures of interest into distinct components. Meanwhile, image registration finds an ideal transformation between given images. Despite their interrelationship, both tasks are often performed separately. As a result, it faces several problems when undertaken independently. Therefore, the joint models between segmentation and registration have advantages over the separate tasks, particularly when dealing with noisy images. This paper reviewed and compared three existing variational joint segmentation and registration (JSR) models based on active contour without edges and linear curvature. The models are tested on 2D mono-modal and multi-modal images with the presence of white Gaussian noise. The Dice coefficient metric and the registration performance measure are used to evaluate the performance of the models. Numerical results showed that the JSR model using the modified sum of the squared difference outperformed the other two JSR models for noisy mono-modal images. However, the JSR model using the modified normalized gradient fields performed better than the JSR model based on the modified mutual information for noisy multi-modal images.

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Decoding the exposome: data science methodologies and implications in exposome-wide association studies (ExWASs)

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Exposome 2024年第1期4卷 osae001页

作者： Ming Kei Chung John S House Farida S Akhtari Konstantinos C Makris Michael A Langston Khandaker Talat Islam Philip Holmes Marc Chadeau-Hyam Alex I Smirnov Xiuxia Du Anne E Thessen Yuxia Cui Kai Zhang Arjun K Manrai Alison Motsinger-Reif Chirag J Patel Department of Biomedical Informatics Harvard Medical School Boston MA USA. School of Public Health and Primary Care The Chinese University of Hong Kong Hong Kong China. Institute of Environment Energy and Sustainability The Chinese University of Hong Kong Hong Kong China. Biostatistics and Computational Biology Branch Division of Intramural Research National Institute of Environmental Health Sciences Durham NC USA. Cyprus International Institute for Environmental and Public Health School of Health Sciences Cyprus University of Technology Limassol Cyprus. Department of Electrical Engineering and Computer Science University of TN Knoxville TN USA. Department of Population and Public Health Sciences Keck School of Medicine of the University of Southern CA Los Angeles CA USA. Department of Physics Villanova University Villanova Philadelphia USA. Department of Epidemiology and Biostatistics School of Public Health Imperial College London London UK. Department of Chemistry NC State University Raleigh NC USA. Department of Bioinformatics and Genomics College of Computing and Informatics University of NC at Charlotte Charlotte NC USA. Department of Biomedical Informatics University of CO Anschutz Medical Campus Aurora CO USA. Exposure Response and Technology Branch Division of Extramural Research and Training National Institute of Environmental Health Sciences Durham NC USA. Department of Environmental Health Sciences School of Public Health University at Albany State University of NY Rensselaer NY USA.

This paper explores the exposome concept and its role in elucidating the interplay between environmental exposures and human health. We introduce two key concepts critical for exposomics research. Firstly, we discuss the joint impact of genetics and environment on phenotypes, emphasizing the variance attributable to shared and nonshared environmental factors, underscoring the complexity of quantifying the exposome's influence on health outcomes. Secondly, we introduce the importance of advanced data-driven methods in large cohort studies for exposomic measurements. Here, we introduce the exposome-wide association study (ExWAS), an approach designed for systematic discovery of relationships between phenotypes and various exposures, identifying significant associations while controlling for multiple comparisons. We advocate for the standardized use of the term "exposome-wide association study, ExWAS," to facilitate clear communication and literature retrieval in this field. The paper aims to guide future health researchers in understanding and evaluating exposomic studies. Our discussion extends to emerging topics, such as FAIR Data Principles, biobanked healthcare datasets, and the functional exposome, outlining the future directions in exposomic research. This abstract provides a succinct overview of our comprehensive approach to understanding the complex dynamics of the exposome and its significant implications for human health.

关键词： Exposome-Wide Association Study (ExWAS) data science epidemiology exposome false discovery rate phenotype

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PennyLane: Automatic differentiation of hybrid quantum-classical computations

arXiv

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arXiv 2018年

作者： Bergholm, Ville Izaac, Josh Schuld, Maria Gogolin, Christian Ahmed, Shahnawaz Ajith, Vishnu Alam, M. Sohaib Alonso-Linaje, Guillermo AkashNarayanan, B. Asadi, Ali Arrazola, Juan Miguel Azad, Utkarsh Banning, Sam Blank, Carsten Bromley, Thomas R. Cordier, Benjamin A. Ceroni, Jack Delgado, Alain Matteo, Olivia Di Dusko, Amintor Garg, Tanya Guala, Diego Hayes, Anthony Hill, Ryan Ijaz, Aroosa Isacsson, Theodor Ittah, David Jahangiri, Soran Jain, Prateek Jiang, Edward Khandelwal, Ankit Kottmann, Korbinian Lang, Robert A. Lee, Christina Loke, Thomas Lowe, Angus McKiernan, Keri Meyer, Johannes Jakob Montañez-Barrera, J.A. Moyard, Romain Niu, Zeyue O’Riordan, Lee James Oud, Steven Panigrahi, Ashish Park, Chae-Yeun Polatajko, Daniel Quesada, Nicolás Roberts, Chase Sá, Nahum Schoch, Isidor Shi, Borun Shu, Shuli Sim, Sukin Singh, Arshpreet Strandberg, Ingrid Soni, Jay Száva, Antal Thabet, Slimane Vargas-Hernández, Rodrigo A. Vincent, Trevor Vitucci, Nicola Weber, Maurice Wierichs, David Wiersema, Roeland Willmann, Moritz Wong, Vincent Zhang, Shaoming Killoran, Nathan Xanadu 777 Bay Street Toronto Canada Wallenberg Centre for Quantum Technology Department of Microtechnology and Nanoscience Chalmers University of Technology Gothenburg412 96 Sweden Indian Institute of Information Technology Kottayam India NASA Ames Research Center Moffett FieldCA94035 United States Mountain ViewCA94043 United States data cybernetics Martin-Kolmsperger-Str 26 Landsberg86899 Germany Department of Medical Informatics and Clinical Epidemiology Oregon Health and Science University PortlandOR97202 United States Dept. of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada Department of Physics Indian Institute of Technology Roorkee Uttarakhand Roorkee India qBraid 5235 South Harper Court ChicagoIL60615 United States Factal Analytics Level 2 Chimes Building Plot 61 Sector - 44 Haryana Gurgaon122003 India Centre for High Energy Physics Indian Institute of Science Karnataka Bengaluru560012 India ICFO - Institut de Ciencies Fotoniques The Barcelona Institute of Science and Technology Av. Carl Friedrich Gauss 3 Castelldefels Barcelona08860 Spain Chemical Physics Theory Group Department of Chemistry University of Toronto Canada DUG Technology 76 Kings Park Rd West PerthWA6005 Australia Rigetti Computing 2919 Seventh Street BerkeleyCA94710 United States Dahlem Center for Complex Quantum Systems Freie Universität Berlin Berlin14195 Germany Institute for Advanced Simulation Jülich Supercomputing Centre Forschungszentrum Jülich Jülich52425 Germany University of Amsterdam Netherlands School of Physical Sciences National Institute of Science Education and Research HBNI Odisha Jatni752 050 India Cervest United Kingdom Centro Brasileiro de Pesquisas Físicas Rua Dr. Xavier Sigaud 150 Rio de Janeiro22290-180 Brazil Zurich8092 Switzerland Neo4j UK Ltd United Kingdom Zapata Computing Inc. United States ITC Infotech Bangalore India Department of Microtechnology and Nanoscienc

PennyLane is a Python 3 software framework for differentiable programming of quantum computers. The library provides a unified architecture for near-term quantum computing devices, supporting both qubit and continuous-variable paradigms. PennyLane’s core feature is the ability to compute gradients of variational quantum circuits in a way that is compatible with classical techniques such as backpropagation. PennyLane thus extends the automatic differentiation algorithms common in optimization and machine learning to include quantum and hybrid computations. A plugin system makes the framework compatible with any gate-based quantum simulator or hardware. We provide plugins for hardware providers including the Xanadu Cloud, Amazon Braket, and IBM Quantum, allowing PennyLane optimizations to be run on publicly accessible quantum devices. On the classical front, PennyLane interfaces with accelerated machine learning libraries such as TensorFlow, PyTorch, JAX, and Autograd. PennyLane can be used for the optimization of variational quantum eigensolvers, quantum approximate optimization, quantum machine learning models, and many other applications. Copyright © 2018, The Authors. All rights reserved.

关键词： Machine learning

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