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arXiv 2017年

作者： Yang, Qingsong Yan, Pingkun Zhang, Yanbo Yu, Hengyong Shi, Yongyi Mou, Xuanqin Kalra, Mannudeep K. Zhang, Yi Sun, Ling Wang, Ge Department of Biomedical Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Department of Electrical and Computer Engineering University of Massachusetts Lowell LowellMA01854 United States Institute of Image Processing and Pattern Recognition Xian Jiaotong University Xian Shaanxi710049 China Department of Radiology Massachusetts General Hospital Harvard Medical School BostonMA United States College of Computer Science Sichuan Universiyt Chengdu610065 China Department of Radiology West China Hospital Sichuan University Chengdu610041 China

The continuous development and extensive use of CT in medical practice has raised a public concern over the associated radiation dose to the patient. Reducing the radiation dose may lead to increased noise and artifacts, which can adversely affect the radiologists judgement and confidence. Hence, advanced image reconstruction from low-dose CT data is needed to improve the diagnostic performance, which is a challenging problem due to its ill-posed nature. Over the past years, various low-dose CT methods have produced impressive results. However, most of the algorithms developed for this application, including the recently popularized deep learning techniques, aim for minimizing the mean-squared-error (MSE) between a denoised CT image and the ground truth under generic penalties. Although the peak signal-to-noise ratio (PSNR) is improved, MSE-or weighted-MSE-based methods can compromise the visibility of important structural details after aggressive denoising. This paper introduces a new CT image denoising method based on the generative adversarial network (GAN) with Wasserstein distance and perceptual similarity. The Wasserstein distance is a key concept of the optimal transport theory, and promises to improve the performance of GAN. The perceptual loss suppresses noise by comparing the perceptual features of a denoised output against those of the ground truth in an established feature space, while the GAN focuses more on migrating the data noise distribution from strong to weak statistically. Therefore, our proposed method transfers our knowledge of visual perception to the image denoising task and is capable of not only reducing the image noise level but also trying to keep the critical information at the same time. Promising results have been obtained in our experiments with clinical CT images. Copyright © 2017, The Authors. All rights reserved.

关键词： image denoising

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Preface

ICMLCA 2021 - 2nd International Conference on Machine Learni...

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ICMLCA 2021 - 2nd International Conference on Machine Learning and computer Application 2021年 iii页

作者： Ning, Xiansheng Feng, Yongxin Zhang, Wenbo Yue, Mingkai Du, Ke-Lin Huang, Shutao Zhou, Fang Ba, Shuhong Fu, Chong Shui, Penglang Xu, Shuwen Wu, Sheng Wang, Shushan Du, Zhonghua Gao, Zhijun Fan, Chunlong Li, Yufeng Wang, Yupeng Liu, Qingli Yu, Jingang Yin, Zhenyu Zhang, Deyu Gao, Hongwei Wen, Feng Guo, Cean Hao, Yongping Chan, Yung-Kuan Pavlovich, Vinogradov Gennady Kirpichnikova, Irina Qian, Zhihong Ivanov, Dmitriy V. Zhou, Xueguan Nezamabadi-Pour, Hossein Yang, Qing Zhang, Yongtang Liao, Haibin Huang, Ying Srinivasan, Kathiravan Xu, Zhenghua Pachori, Ram Bilas Zhang, Jialu AlShawabkeh, Mahmoud Guo, Chengjun Chaugule, Archana Ajit Lima, Alberto Sampaio Liu, Huayong Vidal, Jorge Maestre Alrshah, Mohamed A. Ghafoor, Kayhan Zrar Kubicek, Jan Chaugule, Archana Ajit Rodríguez-Pérez, Miguel Kumar, Gulshan Nasri, Mehdi Borrego, Carlos Persico, Valerio Wagh, Sanjeev Yan, Wenjun Yang, Li Yang, Dawei Xing, Yongkang Yang, Huadong Shang, Chengguo Chang, Xingong Shi, Hongbo Li, Aijun Feng, Liping Lv, Yali Liang, Minfu Wang, Kaixuan Bai, Zengliang Liang, Anhui Nan, Zhihong Ji, Suqin Chang, Liwei Zhou, Hongbin Yao, Chunli Cao, Guodong Zhang, Juling School of Information Science and Engineering Shenyang Ligong University China School of Equipment Engineering Shenyang Ligong University China Concordia University Canada School of Automotive and Transportation Shenyang Ligong University China School of Information Science and Engineering Shenyang Ligong University China School of Equipment Engineering Shenyang Ligong University China Northeastern University China Xidian University China Beijing University of Posts and Telecommunications China Beijing Institute of Technology China Nanjing University of Science & Technology China Shenyang Jianzhu University China Shenyang Aerospace University China Dalian University China Shenyang Institute of Computing Technology Chinese Academy of Sciences China Shenyang Ligong University China National Chun Hsing University Taiwan Department of Informatics and Applied Mathematics Russia South Ural State University Russia Jilin University China Samara State University of Transport Russia Naval Universit of Engineering China Shahid Bahonar University of Kermanto Iran Central China Normal University China Guangdong Neusoft Institute China Hubei University of Science and Technology China Liuzhou Railway Vocational Technical College China Vellore Institute of Technology India Hebei University of Technology China Indian Institute of Technology Indore India Xiangnan University China Guangxi Normal University for Nationalities China UESTC China Pimpri-Chinchwad College of Engineering & Research India Federal University of Ceará Brazil Complutense University of Madrid Spain Universiti Putra Malaysia Malaysia Shanghai Jiao Tong University China VŠB - Technical University of Ostrava Cuw Pimpri-Chinchwad College of Engineering & Research Ravet Pune India University of Vigo Spain Division of Research and Development India Islamic Azad University Iran Departament Enginyeria of Information Comunicacions Autonomous University of Barcelona Spain Opportunistic Networks United States Univers

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Mixed one-bit compressive sensing with application to overexposure correction for CT reconstruction

arXiv

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arXiv 2017年

作者： Huang, Xiaolin Xia, Yan Shi, Lei Huang, Yixing Yan, Ming Hornegger, Joachim Maier, Andreas Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Department of Radiology Stanford University CA United States School of Mathematical Sciences Fudan University Shanghai China Pattern Recognition Lab of Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Department of Computational Mathematics Science and Engineering Michigan State University MI United States

When a measurement falls outside the quantization or measurable range, it becomes saturated and cannot be used in classical reconstruction methods. For example, in C-arm angiography systems, which provide projection radiography, fluoroscopy, digital subtraction angiography, and are widely used for medical diagnoses and interventions, the limited dynamic range of C-arm flat detectors leads to overexposure in some projections during an acquisition, such as imaging relatively thin body parts (e.g., the knee). Aiming at overexposure correction for computed tomography (CT) reconstruction, we in this paper propose a mixed one-bit compressive sensing (M1bitCS) to acquire information from both regular and saturated measurements. This method is inspired by the recent progress on one-bit compressive sensing, which deals with only sign observations. Its successful applications imply that information carried by saturated measurements is useful to improve recovery quality. For the proposed M1bit-CS model, alternating direction methods of multipliers is developed and an iterative saturation detection scheme is established. Then we evaluate M1bit-CS on one-dimensional signal recovery tasks. In some experiments, the performance of the proposed algorithms on mixed measurements is almost the same as recovery on unsaturated ones with the same amount of measurements. Finally, we apply the proposed method to overexposure correction for CT reconstruction on a phantom and a simulated clinical image. The results are promising, as the typical streaking artifacts and capping artifacts introduced by saturated projection data are effectively reduced, yielding significant error reduction compared with existing algorithms based on extrapolation. Copyright © 2017, The Authors. All rights reserved.

关键词： Compressed sensing

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Guest Editorial Biological Applications of Information Theory in Honor of Claude Shannon's Centennial-Part 1

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IEEE Transactions on Molecular, Biological, and Multi-Scale Communications 2016年第1期2卷 1-4页

作者： Dimitrov, Alexander G. Fekri, Faramarz Lazar, Aurel A. Moser, Stefan M. Thomas, Peter J. Department of Mathematics and Statistics Washington State University Vancouver VancouverWA98686-9600 United States School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332-0250 United States Department of Electrical Engineering Columbia University New YorkNY10027 United States Signal and Information Processing Laboratory Department of Information Technology and Electrical Engineering ETH Zurich Zürich8092 Switzerland Department of Electrical and Computer Engineering National Chiao Tung University Hsinchu300 Taiwan Department of Mathematics Applied Mathematics and Statistics Case Western Reserve University ClevelandOH44106 United States

Claude Shannon, born on April 30, 1916, pioneered the mathematical theory of communication in his 1948 paper in the Bell System Technical Journal. Information theory has since provided the foundation for the digital revolution in communications technology. In addition, it has provided a powerful framework for investigating the fundamental limitations of naturally occurring communications, particularly in biological systems. This two-part special issue of the IEEE Transactions on Molecular, Biological, and Multi-Scale Communications honors Shannon's centennial by highlighting recent progress in biological and bio-inspired information theory. Part I includes papers on applications of multivariate mutual information to data clustering, statistical thermodynamics of nonequilibrium chemical processes, information processing in neuronal systems, biochemical signal transduction systems, DNA sequencing, and the swarming of insects. © 2017 IEEE.

关键词： Information theory

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Corrigendum to: More Than Smell-COVID-19 Is Associated With Severe Impairment of Smell, Taste, and Chemesthesis

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Chemical senses 2021年第46期46卷 bjab050页

作者： Valentina Parma Kathrin Ohla Maria G Veldhuizen Masha Y Niv Christine E Kelly Alyssa J Bakke Keiland W Cooper Cédric Bouysset Nicola Pirastu Michele Dibattista Rishemjit Kaur Marco Tullio Liuzza Marta Y Pepino Veronika Schöpf Veronica Pereda-Loth Shannon B Olsson Richard C Gerkin Paloma Rohlfs Domínguez Javier Albayay Michael C Farruggia Surabhi Bhutani Alexander W Fjaeldstad Ritesh Kumar Anna Menini Moustafa Bensafi Mari Sandell Iordanis Konstantinidis Antonella Di Pizio Federica Genovese Lina Öztürk Thierry Thomas-Danguin Johannes Frasnelli Sanne Boesveldt Özlem Saatci Luis R Saraiva Cailu Lin Jérôme Golebiowski Liang-Dar Hwang Mehmet Hakan Ozdener Maria Dolors Guàrdia Christophe Laudamiel Marina Ritchie Jan Havlícek Denis Pierron Eugeni Roura Marta Navarro Alissa A Nolden Juyun Lim Katherine L Whitcroft Lauren R Colquitt Camille Ferdenzi Evelyn V Brindha Aytug Altundag Alberto Macchi Alexia Nunez-Parra Zara M Patel Sébastien Fiorucci Carl M Philpott Barry C Smith Johan N Lundström Carla Mucignat Jane K Parker Mirjam van den Brink Michael Schmuker Florian Ph S Fischmeister Thomas Heinbockel Vonnie D C Shields Farhoud Faraji Enrique Santamaría William E A Fredborg Gabriella Morini Jonas K Olofsson Maryam Jalessi Noam Karni Anna D'Errico Rafieh Alizadeh Robert Pellegrino Pablo Meyer Caroline Huart Ben Chen Graciela M Soler Mohammed K Alwashahi Antje Welge-Lüssen Jessica Freiherr Jasper H B de Groot Hadar Klein Masako Okamoto Preet Bano Singh Julien W Hsieh Danielle R Reed Thomas Hummel Steven D Munger John E Hayes Department of Psychology Temple University Philadelphia PA USA. Institute of Neuroscience and Medicine (INM-3) Research Center Jülich Wilhelm-Johnen-Straße Jülich Germany. Department of Anatomy Faculty of Medicine Mersin University Çiftlikköy Campus Yenişehir Mersin Turkey. Institute of Biochemistry Food Science and Nutrition The Hebrew University of Jerusalem Rehovot Israel. AbScent Andover Hampshire UK. Department of Food Science The Pennsylvania State University Erickson Food Science Building University Park PA USA. Center for the Neurobiology of Learning and Memory University of California and Qureshey Research Laboratory Irvine CA USA. Institut de Chimie de Nice UMR CNRS 7272 Université Côte d'Azur Avenue Valrose Nice France. Centre for Global Health Research Usher Institute The University of Edinburgh Old Medical School Teviot Place Edinburgh UK. Department of Basic Medical Sciences Neuroscience and Sensory Organs Università degli Studi di Bari A. Moro P.zza G. Cesare Bari Italy. CSIR-Central Scientific Instruments Organisation Chandigarh India. Department of Medical and Surgical Sciences "Magna Graecia" University of Catanzaro Viale Europa (Loc. Germaneto) Catanzaro Italy. Department of Food Science and Human Nutrition and Division of Nutritional Sciences University of Illinois at Urbana Champaign Urbana IL USA. Department of Biomedical Imaging and Image-guided Therapy Medical University of Vienna Währinger Gürtel Vienna Austria. Laboratoire d'Anthropologie Moléculaire et Imagerie de Synthese UMR 5288 CNRS Universitéde Toulouse Toulouse France. National Centre for Biological Sciences Tata Institute of Fundamental Research GKVK Campus Bengaluru India. School of Life Sciences Arizona State University Tempe AZ USA. Department of Psychology and Anthropology University of Extremadura Avenida de la Universidad s/n Cáceres Spain. Department of General Psychology University of Padova Via Venezia Padova Italy. Department of Psychiatry

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MOTION SKETCH BASED CROWD VIDEO RETRIEVAL VIA MOTION STRUCTURE CODING

MOTION SKETCH BASED CROWD VIDEO RETRIEVAL VIA MOTION STRUCTU...

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IEEE International Conference on image processing

作者： Shuang Wu Hang Su Shibao Zheng Hua Yang Qin Zhou Institute of Image Processing and Network Engineering Shanghai Jiao Tong University Department of Computer Science and Technology Tsinghua University

ISBN: (纸本)9781467399623

Crowd video retrieval is an important problem in surveillance video management in the era of big data, e.g., video indexing and browsing. In this paper, we address this issue from the motion-level perspective by using hand-drawn sketches as queries. Motion sketch based crowd video retrieval naturally suffers from challenges in motion-level video indexing and sketch representation. We tackle them by leveraging the motion structure coding algorithm to extract robust structure-preserved motion descriptors. For video indexing, we use motion decomposition to separate the sub-motion vector fields with typical patterns from a set of optical flows. Then, the motion-level descriptors of the vector fields are computed and stored in the index database. To represent sketch queries, we propose a sketch vectorization algorithm followed by motion structure coding. In the retrieval stage, given a new query, the retrieval function learned by the Ranking SVM algorithm predicts the ranking score of each motion pattern in the index database. Extensive experiments are conducted on the publicly available crowd datasets, which demonstrate the robustness and effectiveness of the proposed sketch based crowd video retrieval system.

关键词： Crowd video retrieval Motion sketch Motion structure coding Ranking SVM Sketches Videotapes patterning of movement Vector field video retrieval retrieval big data Indexing

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Corrigendum to "syndetically proximal Pairs" [J. Math. Anal. Appl. 379 (2011) 656-663]

arXiv

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arXiv 2017年

作者： Li, Jian Moothathu, T.K. Subrahmonian Oprocha, Piotr Department of Mathematics Shantou University Shantou Guangdong515063 China Guangdong Provincial Key Laboratory of Digital Signal and Image Processing Techniques Shantou University Shantou Guangdong515063 China School of Mathematics and Statistics University of Hyderabad Hyderabad500 046 India AGH University of Science and Technology Faculty of Applied Mathematics AL. Mickiewicza 30 KrakÃ³ W30-059 Poland National Supercomputing Centre IT4 Innovations Division of the University of Ostrava Institute for Research and Applications of Fuzzy Modeling 30. Dubna 22 Ostrava70103 Czech Republic

We give a counterexample to Theorem 9 in "Syndetically proximal pairs"[J. Math. Anal. Appl. 379 (2011) 656-663]. We also provide sufficient conditions for the conclusion of Theorem 9 to hold. Copyright ©... 详细信息

关键词： Optimization

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Perceptual image quality assessment combining free-energy principle and sparse representation

Perceptual image quality assessment combining free-energy pr...

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International Symposium on Circuits and Systems

作者： Yutao Liu Guangtao Zhai Xianming Liu Debin Zhao Department of Computer Science Harbin Institute of Technology Harbin China Institute of Image Communication and Information Processing Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781479953424

Since the purpose of objective image quality assessment is to be consistent with subjective image quality assessment as highly as possible, the understanding of the mechanisms of human visual system will certainly benefit the study of objective image quality assessment. Recent developments in brain theory and neuroscience, particularly the free-energy principle, account for the perception and understanding of visual scenes. As the free-energy principle conjectures, the brain tries to generate the corresponding prediction for its encountered scene by an internal generative model. On the other hand, sparse representation is evidenced to resemble the neural response properties of simple cells in the primary visual cortex. Conjunctively, in this paper, we suppose the prediction manner of the internal generative model in free-energy principle follows sparse representation and propose an image quality metric accordingly. Experiments on LIVE, TID2008 and CSIQ image databases demonstrate the effectiveness of the proposed image quality metric. Noteworthily, our metric needs little information (only a single scalar) of the reference image and is training-free.

关键词： image quality Brain modeling Visualization Measurement Predictive models Mathematical model Computational modeling

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A Novel Reversible Ternary Embedding Algorithm Based on Modified Full Context Prediction Errors

A Novel Reversible Ternary Embedding Algorithm Based on Modi...

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2016 IEEE International Conference on Signal and image processing

作者： Li Li Chin-Chen Chang K.Bharanitharan Yanjun Liu Institute of Computer Graphics and Image Processing Hangzhou Dianzi University Department of Information Engineering and Computer Science Feng Chia University The Australian National University ACT

We propose a high capacity reversible ternary embedding-watermarking algorithm based on a modification of full-context-prediction-errors(MFCPE) wherein the binary bit stream is converted to the ternary stream then error histogram shifting is utilized to embed the ternary *** the existing predictor methods, we provide a full context prediction with a modification of each pixel at most by 1, which significantly reduces *** results confirm that the proposed algorithm achieves high PSNR while providing a higher embedding ***, results indicate that MFCPE outperforms the existing methods in terms of payload and the watermarked image quality.

关键词： reversible watermarking ternary embedding histogram shifting full context prediction

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

arXiv

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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

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