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arXiv 2024年

作者： Grandis, S. Ghirardini, V. Bocquet, S. Garrel, C. Mohr, J.J. Liu, A. Kluge, M. Kimmig, L. Reiprich, T.H. Alarcon, A. Amon, A. Artis, E. Bahar, Y.E. Balzer, F. Bechtol, K. Becker, M.R. Bernstein, G. Bulbul, E. Campos, A. Carnero Rosell, A. Carrasco Kind, M. Cawthon, R. Chang, C. Chen, R. Chiu, I. Choi, A. Clerc, N. Comparat, J. Cordero, J. Davis, C. Derose, J. Diehl, H.T. Dodelson, S. Doux, C. Drlica-Wagner, A. Eckert, K. Elvin-Poole, J. Everett, S. Ferte, A. Gatti, M. Giannini, G. Giles, P. Gruen, D. Gruendl, R.A. Harrison, I. Hartley, W.G. Herner, K. Huff, E.M. Kleinebreil, F. Kuropatkin, N. Leget, P.F. Maccrann, N. McCullough, J. Merloni, A. Myles, J. Nandra, K. Navarro-Alsina, A. Okabe, N. Pacaud, F. Pandey, S. Prat, J. Predehl, P. Ramos, M. Raveri, M. Rollins, R.P. Roodman, A. Ross, A.J. Rykoff, E.S. Sanchez, C. Sanders, J. Schrabback, T. Secco, L.F. Seppi, R. Sevilla-Noarbe, I. Sheldon, E. Shin, T. Troxel, M. Tutusaus, I. Varga, T.N. Wu, H. Yanny, B. Yin, B. Zhang, X. Zhang, Y. Alves, O. Bhargava, S. Brooks, D. Burke, D.L. Carretero, J. Costanzi, M. da Costa, L.N. Pereira, M.E.S. De Vicente, J. Desai, S. Doel, P. Ferrero, I. Flaugher, B. Friedel, D. Frieman, J. García-Bellido, J. Gutierrez, G. Hinton, S.R. Hollowood, D.L. Honscheid, K. James, D.J. Jeffrey, N. Lahav, O. Lee, S. Marshall, J.L. Menanteau, F. Ogando, R.L.C. Pieres, A. Plazas Malagón, A.A. Romer, A.K. Sanchez, E. Schubnell, M. Smith, M. Suchyta, E. Swanson, M.E.C. Tarle, G. Weaverdyck, N. Weller, J. Universität Innsbruck Institut für Astro- und Teilchenphysik Technikerstr. 25/8 Innsbruck6020 Austria Max Planck Institute for Extraterrestrial Physics Giessenbachstrasse 1 Garching85748 Germany Faculty of Physics Ludwig-Maximilians-Universität München Scheinerstr. 1 Munich81679 Germany Universität Bonn Auf dem Hügel 71 Bonn53121 Germany Kavli Institute for Cosmology University of Cambridge Mading- ley Road CambridgeCB3 0HA United Kingdom Institute of Astronomy University of Cambridge Madingley Road CambridgeCB3 0HA United Kingdom Physics Department University of Wisconsin-Madison MadisonWI53706 United States Argonne National Laboratory 9700 South Cass Avenue LemontIL60439 United States Department of Physics and Astronomy University of Pennsylvania PhiladelphiaPA19104 United States Department of Physics Carnegie Mellon University PittsburghPA15312 United States Instituto de Astrofisica de Canarias Tenerife La LagunaE-38205 Spain Laboratório Interinstitucional de e-Astronomia - LIneA Rua Gal. José Cristino 77 RJ Rio de Janeiro20921-400 Brazil Universidad de La Laguna Dpto. Astrofísica Tenerife La LagunaE-38206 Spain Center for Astrophysical Surveys National Center for Supercom- puting Applications 1205 West Clark St. UrbanaIL61801 United States Department of Astronomy University of Illinois at Urbana-Champaign 1002 W. Green Street UrbanaIL61801 United States Physics Department William Jewell College LibertyMO64068 United States Department of Astronomy and Astrophysics University of Chicago ChicagoIL60637 United States Kavli Institute for Cosmological Physics University of Chicago ChicagoIL60637 United States Department of Physics Duke University DurhamNC27708 United States Department of Physics National Cheng Kung University Tainan70101 Taiwan NASA Goddard Space Flight Center 8800 Greenbelt Rd Green- belt MD20771 United States IRAP Université de Toulouse CNRS UPS CNES Toulouse France Jodrell Bank Center f

Context. Number counts of galaxy clusters across redshift are a powerful cosmological probe, if a precise and accurate reconstruction of the underlying mass distribution is performed - a challenge called mass calibration. With the advent of wide and deep photometric surveys, weak gravitational lensing by clusters has become the method of choice to perform this measurement. Aims. We measure and validate the weak gravitational lensing (WL) signature in the shape of galaxies observed in the first 3 years of the Dark Energy Survey (DES Y3) caused by galaxy clusters and groups selected in the first all-sky survey performed by SRG (Spectrum Roentgen Gamma)/eROSITA (eRASS1). These data are then used to determine the scaling between X-ray photon count rate of the clusters and their halo mass and redshift. Methods. We empirically determine the degree of cluster member contamination in our background source sample. The individual cluster shear profiles are then analysed with a Bayesian population model that self-consistently accounts for the lens sample selection and contamination, and includes marginalization over a host of instrumental and astrophysical systematics. To quantify the accuracy of the mass extraction of that model, we perform mass measurements on mock cluster catalogs with realistic synthetic shear profiles. This allows us to establish that hydro-dynamical modelling uncertainties at low lens redshifts (z © 2024, CC BY.

关键词： Calibration

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Retracted: Design of Decision Support System Determination of Inventory Inventory Using Single Exponential Smoothing Forecasting Method

Retracted: Design of Decision Support System Determination o...

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2018 Workshop on Multidisciplinary and Its Applications: Applied mathematics, computer science, Information Systems, and Information Technology, WMA-Mathcomtech 2018

作者： Iqbal, Muhammad Abdullah, Dahlan Hartono, H. Erliana, Cut Ita Sriadhi, S. Nurmawati, N. Prasnowo, M Adhi Hasibuan, Abdurrozzaq Iskandar, Akbar Saddhono, Kundharu Abdussakir, A. Musa, Hastuti Setiawan, Muhammad Ikhsan Sagala, Youmeni Siahaan, Rumiris Sinaga, Eka Mayastika Syahputri, Anggraini Milanie, Feby Aspan, Henry Khaddafi, Muammar Department of Computer System Universitas Pembangunan Panca Budi Medan Indonesia Department of Informatics Universitas Malikussaleh Aceh Indonesia Department of Computer Science STMIK IBBI Medan Indonesia Department of Industrial Engineering Universitas Malikussaleh Aceh Indonesia Department of Electrical Engineering Universitas Negeri Medan Indonesia Faculty of Industry Technique University of 45 Surabaya Indonesia Department of Industrial Engineering Universitas Maarif Hasyim Latif Sidoarjo Indonesia Faculty of Engineering Universitas Islam Sumatera UtaraMedan Indonesia Indonesia Informatics Engineering STMIK AKBA Makassar Indonesia Graduate Program Universitas Sebelas Maret Surakarta Indonesia Department of Mathematics Education State Islamic University of Malang Malang Indonesia Department of Mathematics Universitas Muhammadiyah Parepare Indonesia Department of Civil Engineering Narotama University Surabaya Indonesia STIE Bina Karya Tebing Tinggi Indonesia Universitas Pembangunan Panca Budi Medan Indonesia Department of Accounting Universitas Malikussaleh Aceh Indonesia

The development of information and communication technology at this time has touched all sides of life. Almost no side of human life is not touched by this information technology, which is more synonymous with the world of computerization. No exception also with the computerization that is popular in the corporate environment and government agencies. If in ancient times everything was still done manually. However, at this time all leaders and management have realized the importance of this information technology products that can facilitate them in carrying out the day-to-day corporate functions. Decision Making System of goods purchasing is one of the determinants of the accumulation of goods. If the decision is taken right then stockpiling of goods can be avoided. In this thesis the authors designed a web-based system that helps Management in making the decision of the purchase amount of goods. the system was developed by using Microsoft Visual Studio 2010 and using SQL Server database processing applications. The end result of system design is expected to help minimize the accumulation of goods. © Published under licence by IOP Publishing Ltd.

关键词： Decision support systems

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Vessel tracking via sub-riemannian geodesics on R2× P1

arXiv

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arXiv 2017年

作者： Bekkers, E.J. Duits, R. Mashtakov, A. Sachkov, Yu. Eindhoven University of Technology Department of Mathematics and Computer Science Netherlands Program Systems Institute of RAS Control Processes Research Center Russia

We study a data-driven sub-Riemannian (SR) curve opti-mization model for connecting local orientations in orientation lifts of images. Our model lives on the projective line bundle R2 × P1, with P1 = S1/∼ with identification of antipodal points. It extends previous cortical models for contour perception on R2×P1 to the data-driven case. We provide a complete (mainly numerical) analysis of the dynamics of the 1st Maxwell-set with growing radii of SR-spheres, revealing the cut-locus. Furthermore, a comparison of the cusp-surface in R2 × P1 to its counterpart in R2 ×S1 of a previous model, reveals a general and strong reduction of cusps in spatial projections of geodesics. Numerical solutions of the model are obtained by a single wavefront propagation method re-lying on a simple extension of existing anisotropic fast-marching or it-erative morphological scale space methods. Experiments show that the projective line bundle structure greatly reduces the presence of cusps. Another advantage of including R2 ×P1 instead of R2 ×S1 in the wave-front propagation is reduction of computational time. Copyright © 2017, The Authors. All rights reserved.

关键词： Wavefronts

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Low Dose CT Filtering in the Image Domain Using MAP Algorithms

Low Dose CT Filtering in the Image Domain Using MAP Algorith...

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作者： Geraldo, Rafael J. Cura, Luis M.V. Cruvinel, Paulo E. Mascarenhas, Nelson D.A. Institute of Mathematics and Computer Science-USP Saõ Carlos13566-590 Brazil Computing Department Federal University of Saõ Carlos Saõ Carlos Brazil Faculdade Campo Limpo Paulista Campo Limpo Paulista Brazil Embrapa Instrumentation Center Saõ Carlos Brazil Post-Graduation Program in Computer Sciences Federal University of Saõ Carlos Saõ Carlos Brazil

The purpose of this paper is to present two new noise reduction filters techniques in the CT image space, in order to provide a better quality to the images acquired with low radiation exposure. For the noise reduction, a new denoising technique is presented based on a pointwise Maximum a Posteriori (MAP). The noise is considered Gaussian with zero mean, as observed experimentally, and the variance is estimated considering a signal-independent noise. For the a priori density of the signal, we used different non-negative probability densities (reflecting the fact that the pixels of an image are non-negative). In another approach, the histogram of the images were segmented into unimodal parts and each segment was filtered using the filter based on the MAP criterion with the a priori density that best fits it. After filtering, the evaluation of the method is performed using the following criteria: Peak Signal-to-Noise Ratio, Universal Image Quality Index and Structural Similarity Index. The 2D filtering results are compared with the results obtained by pointwise Wiener filter. Simulation and real CT images results show that the proposed techniques increase the image quality and improve the use of a low-dose CT protocol. © 2017 IEEE.

关键词： Image denoising

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The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)

arXiv

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arXiv 2023年

作者： Kazerooni, Anahita Fathi Khalili, Nastaran Liu, Xinyang Haldar, Debanjan Jiang, Zhifan Anwar, Syed Muhammed Albrecht, Jake Adewole, Maruf Anazodo, Udunna Anderson, Hannah Bagheri, Sina Baid, Ujjwal Bergquist, Timothy Borja, Austin J. Calabrese, Evan Chung, Verena Conte, Gian-Marco Dako, Farouk Eddy, James Ezhov, Ivan Familiar, Ariana Farahani, Keyvan Haldar, Shuvanjan Iglesias, Juan Eugenio Janas, Anastasia Johansen, Elaine Jones, Blaise V. Kofler, Florian LaBella, Dominic Lai, Hollie Anne Van Leemput, Koen Li, Hongwei Bran Maleki, Nazanin McAllister, Aaron S. Meier, Zeke Menze, Bjoern Moawad, Ahmed W. Nandolia, Khanak K. Pavaine, Julija Piraud, Marie Poussaint, Tina Prabhu, Sanjay P. Reitman, Zachary Rodriguez, Andres Rudie, Jeffrey D. Sanchez-Montano, Mariana Shaikh, Ibraheem Salman Shah, Lubdha M. Sheth, Nakul Shinohara, Russel Taki Tu, Wenxin Viswanathan, Karthik Wang, Chunhao Ware, Jeffrey B. Wiestler, Benedikt Wiggins, Walter Zapaishchykova, Anna Aboian, Mariam Bornhorst, Miriam de Blank, Peter Deutsch, Michelle Fouladi, Maryam Hoffman, Lindsey Kann, Benjamin Lazow, Margot Mikael, Leonie Nabavizadeh, Ali Packer, Roger Resnick, Adam Rood, Brian Vossough, Arastoo Bakas, Spyridon Linguraru, Marius George Children’s Hospital of Philadelphia PhiladelphiaPA United States Department of Neurosurgery University of Pennsylvania PhiladelphiaPA United States University of Pennsylvania PhiladelphiaPA United States Sheikh Zayed Institute for Pediatric Surgical Innovation Children’s National Hospital WashingtonDC United States Department of Neurosurgery Thomas Jefferson University Hospital PA United States Sage Bionetworks United States Lab Crestview Radiology Lagos Nigeria McGill University MontrealQC Canada Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Neurosurgery The University of Southern California CA United States Department of Radiology Duke University Medical Center United States Mayo Clinic MN United States Center for Global Health Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Informatics Technical University Munich Germany TranslaTUM - Central Institute for Translational Cancer Research Technical University of Munich Germany Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD United States Biomedical Engineering Rutgers University New BrunswickNJ United States Athinoula A Martinos Center for Biomedical Imaging Massachusetts General Hospital BostonMA United States Yale University New HavenCT United States PrecisionFDA U.S. Food and Drug Administration Silver SpringMD United States Cincinnati Children’s Hospital Medical Center United States Helmholtz AI Helmholtz Munich Germany Department of Radiation Oncology Duke University Medical Center United States Department of Radiology Children’s Health Orange County CA United States Department of Applied Mathematics and Computer Science Technical University of Denmark Denmark Department of Radiol

Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations. The MICCAI Brain Tumor Segmentation (BraTS) Challenge is a landmark community benchmark event with a successful history of 12 years of resource creation for the segmentation and analysis of adult glioma. Here we present the CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge, which represents the first BraTS challenge focused on pediatric brain tumors with data acquired across multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. The BraTS-PEDs 2023 challenge focuses on benchmarking the development of volumentric segmentation algorithms for pediatric brain glioma through standardized quantitative performance evaluation metrics utilized across the BraTS 2023 cluster of challenges. Models gaining knowledge from the BraTS-PEDs multi-parametric structural MRI (mpMRI) training data will be evaluated on separate validation and unseen test mpMRI dataof high-grade pediatric glioma. The CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge brings together clinicians and AI/imaging scientists to lead to faster development of automated segmentation techniques that could benefit clinical trials, and ultimately the care of children with brain tumors. © 2023, CC BY.

关键词： Tumors

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Application of Message Security Application Using Vigenere Cipher Algorithm Utilizing One Time Pad (OTP) Algorithm as a Key Generator

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Journal of Physics: Conference Series 2019年第1期1363卷

作者： A M H Pardede Lorena Perbina Br Sitepu Muhammad Zarlis Tulus Akbar Iskandar S Sriadhi Rosida Tiurma Manurung Nurintan Asyiah Siregar Ayu Esteka Sari Sri Yunawati M Mursyidah M Mursalin Nuning Kurniasih Citra Kurniawan Edy Winarno STMIK Kaputama Binjai Sumatera Utara Indonesia Department of Computer Science Universitas Sumatera Utara Medan Indonesia Department of Mathematics Universitas Sumatera Utara Medan Indonesia Department of Electrical Engineering Universitas Negeri Medan Indonesia Graduate Program in Scientific Psychology Universitas Kristen Maranatha Indonesia Department of Management STIE Labuhanbatu Sumatera Utara Indonesia Department of Management STIE Sakti Alam Kerinci Jambi Indonesia Program Study of Accounting Pasir Pengaraian University Riau Indonesia Departement of Information Technology and Computer Politeknik Negeri Lhokseumawe Aceh Indonesia Department of Mathematics Education Universitas Malikussaleh Aceh Indonesia Faculty of Communication Sciences Library and Information Science Program Universitas Padjadjaran Bandung Indonesia Department of Electrical Engineering Sekolah Tinggi Teknik Malang Indonesia Faculty of Information Technology Universitas Stikubank Semarang Indonesia

Cryptography is the science and art of maintaining the security of messages when messages are sent from one place to another. One of the ways securing the form of text message information is by the encryption process using the Vigenere Cipher algorithm and utilizing the One Time Pad (OTP) algorithm as a key generator, where the message will be random when it is opened. The message encryption process used the vigenere cipher algorithm while OTP is used to secure the key with the same formula. After this research has been done, an application was designed to secure text messages by converting the text message into a random message so that the message was unreadable due to a secret message that could not be known by others. The results achieved can secure an encrypted message that cannot be reopened and if those messages were reopened, they must be decrypted.

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Author Correction: Genomic footprints of activated telomere maintenance mechanisms in cancer (Dec, 10.1038/s41467-019-13824-9, 2022)

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NATURE COMMUNICATIONS 2022年第1期13卷 1页

作者： Sieverling, Lina Hong, Chen Koser, Sandra D. Ginsbach, Philip Kleinheinz, Kortine Hutter, Barbara Braun, Delia M. Cortes-Ciriano, Isidro Xi, Ruibin Kabbe, Rolf Park, Peter J. Eils, Roland Schlesner, Matthias Brors, Benedikt Rippe, Karsten Jones, David T. W. Feuerbach, Lars Division of Applied Bioinformatics German Cancer Research Center (DKFZ) 69120 Heidelberg Germany Faculty of Biosciences Heidelberg University 69120 Heidelberg Germany Division of Applied Bioinformatics German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Biosciences Heidelberg University Heidelberg Germany German Cancer Consortium (DKTK) Heidelberg Germany National Center for Tumor Diseases (NCT) Heidelberg Heidelberg Germany German Cancer Consortium (DKTK) German Cancer Research Center (DKFZ) Heidelberg Germany Wellcome Sanger Institute Wellcome Genome Campus Hinxton Cambridge CB10 1SA UK Korea Advanced Institute of Science and Technology Daejeon 34141 South Korea Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Korea Advanced Institute of Science and Technology Daejeon South Korea Division of Chromatin Networks German Cancer Research Center (DKFZ) and BioQuant 69120 Heidelberg Germany Division of Theoretical Bioinformatics German Cancer Research Center (DKFZ) 69120 Heidelberg Germany Department for Bioinformatics and Functional Genomics Institute for Pharmacy and Molecular Biotechnology (IPMB) and BioQuant 69120 Heidelberg Germany Division of Theoretical Bioinformatics German Cancer Research Center (DKFZ) Heidelberg Germany Institute of Pharmacy and Molecular Biotechnology and BioQuant Heidelberg University Heidelberg Germany Heidelberg University Heidelberg Germany New BIH Digital Health Center Berlin Institute of Health (BIH) and Charité - Universitätsmedizin Berlin Berlin Germany Heidelberg Center for Personalized Oncology (DKFZ-HIPO) German Cancer Research Center (DKFZ) Heidelberg Germany Department of Biomedical Informatics Harvard Medical School Boston MA USA Ludwig Center at Harvard Medical School Boston MA USA Department of Biomedical Informatics Harvard Medical School Boston Massachusetts 02115 USA Ludwig Center at Harvard Medical School Boston Massachusetts 02115 USA Broad Institute of MIT and Harvard

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Analysis of the BraTS 2023 Intracranial Meningioma Segmentation Challenge

arXiv

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arXiv 2024年

作者： LaBella, Dominic Baid, Ujjwal Khanna, Omaditya McBurney-Lin, Shan McLean, Ryan Nedelec, Pierre Rashid, Arif Tahon, Nourel Hoda Altes, Talissa Bhalerao, Radhika Dhemesh, Yaseen Godfrey, Devon Hilal, Fathi Floyd, Scott Janas, Anastasia Kazerooni, Anahita Fathi Kirkpatrick, John Kent, Collin Kofler, Florian Leu, Kevin Maleki, Nazanin Menze, Bjoern Pajot, Maxence Reitman, Zachary J. Rudie, Jeffrey D. Saluja, Rachit Velichko, Yury Wang, Chunhao Warman, Pranav Adewole, Maruf Albrecht, Jake Anazodo, Udunna Anwar, Syed Muhammad Bergquist, Timothy Chen, Sully Francis Chung, Verena Conte, Gian-Marco Dako, Farouk Eddy, James Ezhov, Ivan Khalili, Nastaran Iglesias, Juan Eugenio Jiang, Zhifan Johanson, Elaine Van Leemput, Koen Li, Hongwei Bran Linguraru, Marius George Liu, Xinyang Mahtabfar, Aria Meier, Zeke Moawad, Ahmed W. Mongan, John Piraud, Marie Shinohara, Russell Takeshi Wiggins, Walter F. Abayazeed, Aly H. Akinola, Rachel Jakab, András Bilello, Michel de Verdier, Maria Correia Crivellaro, Priscila Davatzikos, Christos Farahani, Keyvan Freymann, John Hess, Christopher Huang, Raymond Lohmann, Philipp Moassefi, Mana Pease, Matthew W. Vollmuth, Phillipp Sollmann, Nico Diffley, David Nandolia, Khanak K. Warren, Daniel I. Hussain, Ali Fehringer, Pascal Bronstein, Yulia Deptula, Lisa Stein, Evan G. Taherzadeh, Mahsa de Oliveira, Eduardo Portela Haughey, Aoife Kontzialis, Marinos Saba, Luca Turner, Benjamin Brüßeler, Melanie M.T. Ansari, Shehbaz Gkampenis, Athanasios Weiss, David Maximilian Mansour, Aya Shawali, Islam H. Yordanov, Nikolay Stein, Joel M. Hourani, Roula Moshebah, Mohammed Yahya Abouelatta, Ahmed Magdy Rizvi, Tanvir Willms, Klara Martin, Dann C. Okar, Abdullah D'Anna, Gennaro Taha, Ahmed Sharifi, Yasaman Faghani, Shahriar Kite, Dominic Pinho, Marco Haider, Muhammad Ammar Aristizabal, Alejandro Karargyris, Alexandros Kassem, Hasan Pati, Sarthak Sheller, Micah Alonso-Basanta, Michelle Villanueva-Meyer, Javier Rauschecker, Andreas M. Nada, Ayman Aboian, Mariam Flanders, Adam E. Wiestler, Benedikt Bakas, Spyridon Ca Duke University Medical Center Department of Radiation Oncology DurhamNC United States Division of Computational Pathology Department of Pathology Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Center for Federated Learning in Medicine Indiana University IndianapolisIN United States Department of Neurosurgery Thomas Jefferson University PhiladelphiaPA United States University of California San FranciscoCA United States Yale University New HavenCT United States Department of Radiation Oncology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States University of Missouri ColumbiaMO United States Department of Biostatistics and Health Data Science Indiana University School of Medicine IndianapolisIN United States Children's Hospital of Philadelphia University of Pennsylvania PhiladelphiaPA United States University of Pennsylvania PhiladelphiaPA United States Helmholtz AI Helmholtz Munich Germany Department of Informatics Technical University Munich Germany TranslaTUM - Central Institute for Translational Cancer Research Technical University of Munich Germany Department of Diagnostic and Interventional Neuroradiology School of Medicine Klinikum rechts der Isar Technical University of Munich Germany San FranciscoCA United States Biomedical Image Analysis and Machine Learning Department of Quantitative Biomedicine University of Zurich Switzerland Department of Neuroradiology Technical University of Munich Munich Germany University of San Diego CA United States Cornell University IthacaNY United States Department of Radiology Northwestern University EvanstonIL United States Duke University Medical Center School of Medicine DurhamNC United States Lab Crestview Radiology Lagos Nigeria Sage Bionetworks United States McGill University MontrealQC Canada Children's National Hospital WashingtonDC United States George Washington University WashingtonDC United State

We describe the design and results from the BraTS 2023 Intracranial Meningioma Segmentation Challenge. The BraTS Meningioma Challenge differed from prior BraTS Glioma challenges in that it focused on meningiomas, which are typically benign extra-axial tumors with diverse radiologic and anatomical presentation and a propensity for multiplicity. Nine participating teams each developed deep-learning automated segmentation models using image data from the largest multi-institutional systematically expert annotated multilabel multi-sequence meningioma MRI dataset to date, which included 1000 training set cases, 141 validation set cases, and 283 hidden test set cases. Each case included T2, T2/FLAIR, T1, and T1Gd brain MRI sequences with associated tumor compartment labels delineating enhancing tumor, non-enhancing tumor, and surrounding non-enhancing T2/FLAIR hyperintensity. Participant automated segmentation models were evaluated and ranked based on a scoring system evaluating lesion-wise metrics including dice similarity coefficient (DSC) and 95% Hausdorff Distance. The top ranked team had a lesion-wise median dice similarity coefficient (DSC) of 0.976, 0.976, and 0.964 for enhancing tumor, tumor core, and whole tumor, respectively and a corresponding average DSC of 0.899, 0.904, and 0.871, respectively. These results serve as state-of-the-art benchmarks for future pre-operative meningioma automated segmentation algorithms. Additionally, we found that 1286 of 1424 cases (90.3%) had at least 1 compartment voxel abutting the edge of the skull-stripped image edge, which requires further investigation into optimal pre-processing face anonymization steps. © 2024, CC BY.

关键词： Tumors

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The Brain Tumor Segmentation in Pediatrics (BraTS-PEDs) Challenge: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)

arXiv

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arXiv 2024年

作者： Kazerooni, Anahita Fathi Khalili, Nastaran Liu, Xinyang Gandhi, Deep Jiang, Zhifan Anwar, Syed Muhammed Albrecht, Jake Adewole, Maruf Anazodo, Udunna Anderson, Hannah Baid, Ujjwal Bergquist, Timothy Borja, Austin J. Calabrese, Evan Chung, Verena Conte, Gian-Marco Dako, Farouk Eddy, James Ezhov, Ivan Familiar, Ariana Farahani, Keyvan Franson, Andrea Gottipati, Anurag Haldar, Shuvanjan Iglesias, Juan Eugenio Janas, Anastasia Johansen, Elaine Jones, Blaise V. Khalili, Neda Kofler, Florian LaBella, Dominic Lai, Hollie Anne Van Leemput, Koen Li, Hongwei Bran Maleki, Nazanin McAllister, Aaron S. Meier, Zeke Menze, Bjoern Moawad, Ahmed W. Nandolia, Khanak K. Pavaine, Julija Piraud, Marie Poussaint, Tina Prabhu, Sanjay P. Reitman, Zachary Rudie, Jeffrey D. Sanchez-Montano, Mariana Shaikh, Ibraheem Salman Sheth, Nakul Tu, Wenxin Wang, Chunhao Ware, Jeffrey B. Wiestler, Benedikt Zapaishchykova, Anna Bornhorst, Miriam Deutsch, Michelle Fouladi, Maryam Lazow, Margot Mikael, Leonie Hummel, Trent Kann, Benjamin de Blank, Peter Hoffman, Lindsey Aboian, Mariam Nabavizadeh, Ali Packer, Roger Bakas, Spyridon Resnick, Adam Rood, Brian Vossough, Arastoo Linguraru, Marius George Children’s Hospital of Philadelphia PhiladelphiaPA United States Department of Neurosurgery University of Pennsylvania PhiladelphiaPA United States University of Pennsylvania PhiladelphiaPA United States Sheikh Zayed Institute for Pediatric Surgical Innovation Children’s National Hospital WashingtonDC United States Sage Bionetworks United States Lab Crestview Radiology Lagos Nigeria McGill University MontrealQC Canada Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Division of Computational Pathology Department of Pathology and Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Department of Neurosurgery The University of Southern California CA United States Department of Radiology Duke University Medical Center DurhamNC United States Mayo Clinic MN United States Center for Global Health Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Informatics Technical University Munich Germany TranslaTUM - Central Institute for Translational Cancer Research Technical University of Munich Germany Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD United States C. S. Mott Children’s Hospital University of Michigan MI United States Biomedical Engineering Rutgers University New BrunswickNJ United States Athinoula A Martinos Center for Biomedical Imaging Massachusetts General Hospital BostonMA United States Yale University New HavenCT United States PrecisionFDA U.S. Food and Drug Administration Silver SpringMD United States Cincinnati Children’s Hospital Medical Center OH United States Helmholtz AI Helmholtz Munich Germany Department of Radiation Oncology Duke University Medical Center DurhamNC United States Department of Radiology Children’s Health Orange County CA United States Department of Applied Mathematics and Computer Science Technical University of De

关键词： Deep learning

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Data mining for dengue hemorrhagic fever (DHF) prediction with naive Bayes method

引用

Journal of Physics: Conference Series 2018年第1期948卷

作者： Ria Arafiyah Fariani Hermin Computer Science Program Faculty of Mathematics and Natural Sciences Jakarta State University Jl. Rawamangun Muka East Jakarta Indonesia.

Handling of infectious diseases is determined by the accuracy and speed of diagnosis. Government through the Regulation of the Minister of Health of the Republic of Indonesia No. 82 of 2014 on the Control of Communicable Diseases establishes Dengue Hemorrhagic Fever (DHF) has made DHF prevention a national priority. Various attempts were made to overcome this misdiagnosis. The treatment and diagnosis of DHF using ANFIS has result an application program that can decide whether a patient has dengue fever or not [1]. An expert system of dengue prevention by using ANFIS has predict the weather and the number of sufferers [2]. The large number of data on DHF often cannot affect a person in making decisions. The use of data mining method, able to build data base support in decision makers diagnose DHF disease [3]. This study predicts DHF with the method of Naive Bayes. Parameter of The input variable is the patient's medical data (temperature, spotting, bleeding, and tornuine test) and the output variable suffers from DBD or not while the system output is diagnosis of the patient suffering from DHF or not. Result of model test by using tools of Orange 3.4.5 obtained level of precision model is 77,3%.

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