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An efficient synthesis method for ternary reversible logic

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An efficient synthesis method for ternary reversible logic

IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Saikat Basu Sudhindu Bikash Mandal Amlan Chakrabarti Susmita Sur-Kolay A. K. Choudhury Advance Computing and Microelectronics Unit Indian Statistical Institute Kolkata India A.K.Choudhury School of I.T. University of Calcutta Kolkata India School of I.T. University of Calcutta Kolkata India

ISBN: (纸本)9781479953424

While the role of ternary reversible and quantum computation has been growing, synthesis methodologies for such logic, have been addressed in only a few works. A reversible ternary logic function can be expressed as minterms by using projection operators. In this paper, a novel realization of the projection operators using a minimum number of permutative ternary Muthukrishnan-Stroud (M-S) gates is presented. Next, an efficient method for logic simplification for ternary reversible logic is proposed. This method along with the new construction of projection operators yields significantly lower gate cost of approximately 31% less than that obtained by earlier methodologies, for the synthesis of ternary benchmark circuits.

关键词： Logic gates Quantum computing Quantum mechanics Minimization Electronic mail Multivalued logic Benchmark testing

Prediction of the final rank of Players in PUBG with the optimal number of features

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arXiv 2021年

作者： Sen, Diptakshi Roy, Rupam Kumar Majumdar, Ritajit Chatterjee, Kingshuk Ganguly, Debayan Department of Computer Science and Engineering University of Calcutta India Advanced Computing and Microelectronics Unit Indian Statistical Institute Kolkata India Department of Computer Science and Engineering Government College of Engineering and Ceramic Technology India Department of Computer Science and Engineering Government College of Engineering and Leather Technology India

PUBG is an online video game that has become very popular among the youths in recent years. Final rank, which indicates the performance of a player, is one of the most important feature for this game. This paper focuses on predicting the final rank of the players based on their skills and abilities. In this paper we have used different machine learning algorithms to predict the final rank of the players on a dataset obtained from kaggle which has 29 features. Using the correlation heatmap,we have varied the number of features used for the model. Out of these models GBR and LGBM have given the best result with the accuracy of 91.63% and 91.26% respectively for 14 features and the accuracy of 90.54% and 90.01% for 8 features. Although the accuracy of the models with 14 features is slightly better than 8 features, the empirical time taken by 8 features is 1.4x lesser than 14 features for LGBM and 1.5x lesser for GBR. Furthermore, reducing the number of features any more significantly hampers the performance of all the ML models. Therefore, we conclude that 8 is the optimal number of features that can be used to predict the final rank of a player in PUBG with high accuracy and low run-time. © 2021, CC0.

关键词： Forecasting

General chairs’ message

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2005年 3326 LNCS卷 V-VI页

作者： Das, Nabanita Sinha, Bhabani P. Sen, Arunabha Das, Sajal K. Indian Statistical Institute Advanced Computing and Microelectronics Unit 203 B.T. Road Kolkata700 108 India Arizona State University Dept. of Computer Science and Engineering TempeAZ United States University of Texas at Arlington Dept. of Computer Science and Engineering ArlingtonTX76019-0015 United States

FragQC: An Efficient Quantum Error Reduction Technique using Quantum Circuit Fragmentation

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arXiv 2023年

作者： Basu, Saikat Das, Arnav Saha, Amit Chakrabarti, Amlan Sur-Kolay, Susmita A.K.Choudhury School of Information Technology U. of Calcutta Kolkata India LTIMindtree Ltd. Kolkata India Dept. of Computer Science St. Xavier's University Kolkata India Pune India Advanced Computing and Microelectronics Unit Indian Statistical Institute Kolkata India

Quantum computers must meet extremely stringent qualitative and quantitative requirements on their qubits in order to solve real-life problems. Quantum circuit fragmentation techniques divide a large quantum circuit into a number of sub-circuits that can be executed on the smaller noisy quantum hardware available. However, the process of quantum circuit fragmentation involves finding an ideal cut that has exponential time complexity, and also classical post-processing required to reconstruct the output. In this paper, we represent a quantum circuit using a weighted graph and propose a novel classical graph partitioning algorithm for selecting an efficient fragmentation that reduces the entanglement between the sub-circuits along with balancing the estimated error in each sub-circuit. We also demonstrate a comparative study over different classical and quantum approaches of graph partitioning for finding such a cut. We present FragQC, a software tool that cuts a quantum circuit into sub-circuits when its error probability exceeds a certain threshold. With this proposed approach, we achieve an increase of fidelity by 14.83% compared to direct execution without cutting the circuit, and 8.45% over the state-of-the-art ILP-based method, for the benchmark circuits. © 2023, CC BY.

关键词： Genetic algorithms

Surface Code Design for Asymmetric Error Channels

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arXiv 2021年

作者： Azad, Utkarsh Lipińska, Aleksandra Mahato, Shilpa Sachdeva, Rijul Bhoumik, Debasmita Majumdar, Ritajit Center for Computational Natural Sciences and Bioinformatics International Institute of Information Technology Hyderabad India Center for Quantum Science and Technology International Institute of Information Technology Hyderabad India Xanadu TorontoONM5G 2C8 Canada Faculty of Mathematics and Computer Science Jagiellonian University Kraków Poland Department of Physics Indian Institute of Technology Dhanbad India Jülich Supercomputing Center Forschungszentrum Jülich Germany RWTH Aachen University Germany Advanced Computing & Microelectronics Unit Indian Statistical Institute Kolkata India

Surface codes are quantum error correcting codes typically defined on 2D array of qubits. In this paper, a [dx,dz] surface code design is being introduced, where dx(dz) represents the distance of the code for bit (phase) error correction, motivated by the fact that the severity of bit flip and phase flip errors in the physical quantum system is asymmetric. We present pseudo-threshold and threshold values for the proposed surface code design for asymmetric error channels in the presence of various degrees of asymmetry of Pauli Xˆ, Ŷ, and Ẑ errors in a depolarization channel. We demonstrate that compared to symmetric surface codes, our asymmetric surface codes can provide almost double the pseudo-threshold rates while requiring less than half the number of physical qubits in the presence of increasing asymmetry in the error channel. Our results show that for low degree of asymmetry, it is advantageous to increase dx along with dz. However, as the asymmetry of the channel increases, higher pseudo-threshold is obtained with increasing dz when dx is kept constant at a low value. Additionally, we also show that the advantage in the pseudo-threshold rates begins to saturate for any possible degree of asymmetry in the error channel as the surface code asymmetry is continued to increase. © 2021, CC BY-NC-ND.

关键词： Error correction

25th Annual Computational Neuroscience Meeting CNS-2016, Seogwipo City, South Korea, July 2-7, 2016 Abstracts

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BMC NEUROSCIENCE 2016年第1期17卷 1-112页

作者： [Anonymous] Computational Neurobiology Laboratory The Salk Institute for Biological Studies San Diego USA UNIC CNRS Gif sur Yvette France The European Institute for Theoretical Neuroscience (EITN) Paris France ATR Computational Neuroscience Laboratories Kyoto Japan Krembil Research Institute University Health Network Toronto Canada Department of Physiology University of Toronto Toronto Canada Department of Medicine (Neurology) University of Toronto Toronto Canada Department of Physics University of New Hampshire Durham USA Department of Neurophysiology Nencki Institute of Experimental Biology Warsaw Poland Department of Theory Wigner Research Centre for Physics of the Hungarian Academy of Sciences Budapest Hungary Department of Mathematical Sciences KAIST Daejoen Republic of Korea Department of Mathematics University of Houston Houston USA Department of Biochemistry & Cell Biology and Institute of Biosciences and Bioengineering Rice University Houston USA Department of Biology and Biochemistry University of Houston Houston USA Grupo de Neurocomputación Biológica Dpto. de Ingeniería Informática Escuela Politécnica Superior Universidad Autónoma de Madrid Madrid Spain Department of Biological Sciences University of Southern California Los Angeles USA Center for Neuroscience Korea Institute of Science and Technology Seoul South Korea Department of Neurology Albert Einstein College of Medicine Bronx USA Center for Neuroscience KIST Seoul South Korea Department of Neuroscience University of Science and Technology Daejon South Korea Systems Neuroscience Group QIMR Berghofer Medical Research Institute Herston Australia Department of Psychology Yonsei University Seoul South Korea Department of Psychiatry Kyung Hee University Hospital at Gangdong Seoul South Korea Department of Psychiatry Veterans Administration Boston Healthcare System and Harvard Medical School Brockton USA Department of Electrical and Electronic Engineering The University of Melbourne Parkvil

A1 Functional advantages of cell-type heterogeneity in neural circuits Tatyana O. Sharpee A2 Mesoscopic modeling of propagating waves in visual cortex Alain Destexhe A3 Dynamics and biomarkers of mental disorders Mitsuo Kawato F1 Precise recruitment of spiking output at theta frequencies requires dendritic h-channels in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneurons Vladislav Sekulić, Frances K. Skinner F2 Kernel methods in reconstruction of current sources from extracellular potentials for single cells and the whole brains Daniel K. Wójcik, Chaitanya Chintaluri, Dorottya Cserpán, Zoltán Somogyvári F3 The synchronized periods depend on intracellular transcriptional repression mechanisms in circadian clocks. Jae Kyoung Kim, Zachary P. Kilpatrick, Matthew R. Bennett, Kresimir Josić O1 Assessing irregularity and coordination of spiking-bursting rhythms in central pattern generators Irene Elices, David Arroyo, Rafael Levi, Francisco B. Rodriguez, Pablo Varona O2 Regulation of top-down processing by cortically-projecting parvalbumin positive neurons in basal forebrain Eunjin Hwang, Bowon Kim, Hio-Been Han, Tae Kim, James T. McKenna, Ritchie E. Brown, Robert W. McCarley, Jee Hyun Choi O3 Modeling auditory stream segregation, build-up and bistability James Rankin, Pamela Osborn Popp, John Rinzel O4 Strong competition between tonotopic neural ensembles explains pitch-related dynamics of auditory cortex evoked fields Alejandro Tabas, André Rupp, Emili Balaguer-Ballester O5 A simple model of retinal response to multi-electrode stimulation Matias I. Maturana, David B. Grayden, Shaun L. Cloherty, Tatiana Kameneva, Michael R. Ibbotson, Hamish Meffin O6 Noise correlations in V4 area correlate with behavioral performance in visual discrimination task Veronika Koren, Timm Lochmann, Valentin Dragoi, Klaus Obermayer O7 Input-location dependent gain modulation in cerebellar nucleus neurons Maria Psarrou, Maria Schilstra, Neil Davey, Benjamin Torben-Ni

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Abstracts from the Food Allergy and Anaphylaxis Meeting 2016 Abstracts

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CLINICAL AND TRANSLATIONAL ALLERGY 2017年第SUPPL 1期7卷 10-10页

作者： [Anonymous] Department of Pediatrics Children’s Hospital Roubaix France Division of Pulmonology and Allergology Department of Pediatrics Faculty of Medicine and Children’s Hospital Lille France Allergy Vigilance Network Vandoeuvre les Nancy France Université Lille 2 CHU Lille EA 2694 - Santé Publique: épidémiologie et qualité des soins Lille France Biostatistics Unit Maison Régionale de la Recherche Clinique CHRU Lille Lille France Department of Allergology Emile Durkheim Hospital Epinal France Centre d’Epidémiologie sur les Causes Médicales de Décès INSERM CHU de Bicêtre Le Kremlin-Bicêtre France The Allergy Vigilance Network University Hospital Nancy Nancy France Pediatric Pulmonology and Allergy Department Hôpital Jeanne de Flandre CHU Lille Lille France Charité - Universitätsmedizin Berlin Berlin Germany Institute for Social Medicine Epidemiology and Health Economics Charité - Universitätsmedizin Berlin Berlin Germany Institute of Social Medicine Epidemiology and Health Economics Charité Universitätsmedizin Berlin Berlin Germany Department of Dermatology and Allergology Comprehensive Allergy Center Charité Charité – Universitätsmedizin Berlin Berlin Germany Department of Dermatology and Allergy Allergie-Centrum-Charité Charité-Universiätsmedizin Berlin Berlin Germany Department of Dermatology and Allergology Comprehensive Allergy Center Charité Charité Universitätsmedizin Berlin Berlin Germany University Hospital Basel Basel Switzerland Allergy Unit Department of Dermatology University Hospital Basel Switzerland Universitätsklinikum Salzburg Salzburg Austria Department of Dermatology and Allergy Centre Odense Research Center for Anaphylaxis (ORCA) Odense University Hospital Odense Denmark Odense University Hospital Odense Denmark Serviço de ImunoalergologiaCentro Hospitalar de São João E.P.E. Porto Portugal GRIAC Research Institute University of Groningen University Medical Center Groningen Groningen The Netherlands Serviço de Imunoalergologia Centr

QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results

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arXiv 2021年

作者： Mehta, Raghav Filos, Angelos Baid, Ujjwal Sako, Chiharu McKinley, Richard Rebsamen, Michael Dätwyler, Katrin Meier, Raphael Radojewski, Piotr Murugesan, Gowtham Krishnan Nalawade, Sahil Ganesh, Chandan Wagner, Ben Yu, Fang F. Fei, Baowei Madhuranthakam, Ananth J. Maldjian, Joseph A. Daza, Laura Gómez, Catalina Arbeláez, Pablo Dai, Chengliang Wang, Shuo Reynaud, Hadrien Mo, Yuanhan Angelini, Elsa Guo, Yike Bai, Wenjia Banerjee, Subhashis Pei, Linmin Murat, A.K. Rosas-González, Sarahi Zemmoura, Ilyess Tauber, Clovis Vu, Minh H. Nyholm, Tufve Löfstedt, Tommy Ballestar, Laura Mora Vilaplana, Veronica McHugh, Hugh Talou, Gonzalo Maso Wang, Alan Patel, Jay Chang, Ken Hoebel, Katharina Gidwani, Mishka Arun, Nishanth Gupta, Sharut Aggarwal, Mehak Singh, Praveer Gerstner, Elizabeth R. Kalpathy-Cramer, Jayashree Boutry, Nicolas Huard, Alexis Vidyaratne, Lasitha Rahman, Md Monibor Iftekharuddin, Khan M. Chazalon, Joseph Puybareau, Elodie Tochon, Guillaume Ma, Jun Cabezas, Mariano Llado, Xavier Oliver, Arnau Valencia, Liliana Valverde, Sergi Amian, Mehdi Soltaninejad, Mohammadreza Myronenko, Andriy Hatamizadeh, Ali Feng, Xue Dou, Quan Tustison, Nicholas Meyer, Craig Shah, Nisarg A. Talbar, Sanjay Weber, Marc-André Mahajan, Abhishek Jakab, Andras Wiest, Roland Fathallah-Shaykh, Hassan M. Nazeri, Arash Milchenko, Mikhail Marcus, Daniel Kotrotsou, Aikaterini Colen, Rivka Freymann, John Kirby, Justin Davatzikos, Christos Menze, Bjoern Bakas, Spyridon Gal, Yarin Arbel, Tal McGill University MontrealQC Canada Group University of Oxford Oxford United Kingdom University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine The University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States University Institute of Diagnostic and Interventional Neuroradiology University of Bern Inselspital Bern University Hospital Bern Switzerland Department of Radiology University of Texas Southwestern Medical Center DallasTX United States Department of Bioengineering University of Texas DallasTX United States Advanced Imaging Research Center University of Texas Southwestern Medical Center DallasTX United States Universidad de los Andes Bogotá Colombia Data Science Institute Imperial College London London United Kingdom NIHR Imperial BRC ITMAT Data Science Group Imperial College London London United Kingdom Department of Brain Sciences Imperial College London London United Kingdom Machine Intelligence Unit Indian Statistical Institute Kolkata India Department of CSE University of Calcutta Kolkata India Department of Information Technology Uppsala University Uppsala Sweden Department of Diagnostic Radiology The University of Pittsburgh Medical Center PittsburghPA United States UMR U1253 iBrain Université de Tours Inserm Tours France Department of Radiation Sciences Umeå University Umeå Sweden Department of Computing Science Umeå University Umeå Sweden Signal Theory and Communications Department Universitat Politècnica de Catalunya Barcelona Tech Barcelona Spain Faculty of Medical and Health Sciences University of Auckland Auckland New Zealand Radiology Department Auckland City Hospital Auckland New Zealand Auckland Bioengineering Institute University of Auckland New Zealand Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology

Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder translating DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties could enable clinical review of the most uncertain regions, thereby building trust and paving the way toward clinical translation. Several uncertainty estimation methods have recently been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentage of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, highlighting the need for uncertainty quantification in medical image analyses. Finally, in favor of transparency and reproducibility, our evaluation code is made publicly available at https://***/RagMeh11/QU-BraTS. © 2021, CC BY.

关键词： Benchmarking

Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

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arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States 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 Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors