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arXiv 2018年

作者： Veta, Mitko Heng, Yujing J. Stathonikos, Nikolas Bejnordi, Babak Ehteshami Beca, Francisco Wollmann, Thomas Rohr, Karl Shah, Manan A. Wang, Dayong Rousson, Mikael Hedlund, Martin Tellez, David Ciompi, Francesco Zerhouni, Erwan Lanyi, David Viana, Matheus Kovalev, Vassili Liauchuk, Vitali Phoulady, Hady Ahmady Qaiser, Talha Graham, Simon Rajpoot, Nasir Sjöblom, Erik Molin, Jesper Paeng, Kyunghyun Hwang, Sangheum Park, Sunggyun Jia, Zhipeng Chang, Eric I-Chao Xu, Yan Beck, Andrew H. Van Diest, Paul J. Pluim, Josien P.W. Medical Image Analysis Group Eindhoven University of Technology Eindhoven Netherlands Department of Pathology Beth Israel Deaconess Medical Center Harvard Medical School BostonMA United States Department of Pathology University Medical Center Utrecht Utrecht Netherlands Diagnostic Image Analysis Group Radboud University Medical Center Nijmegen Netherlands Department of Pathology Stanford University School of Medicine Biomedical Computer Vision Group University of Heidelberg BIOQUANT IPMB DKFZ Heidelberg Germany Harker School San Jose United States ContextVision AB Linköping Sweden Foundations of Cognitive Computing IBM Research Zürich Rüschlikon Switzerland Visual Analytics and Insights IBM Research Brazil Saõ Paulo Brazil Biomedical Image Analysis Department United Institute of Informatics Minsk Belarus Department of Computer Science and Engineering University of South Florida TampaFL United States Department of Computer Science University of Warwick Warwick United Kingdom Research Sectra Linköping Sweden Lunit Inc. Seoul Korea Republic of Institute for Interdisciplinary Information Sciences Tsinghua University Beijing China Microsoft Research Beijing China Biology and Medicine Engineering Beihang University Beijing China IBM Research Brazil

Tumor proliferation is an important biomarker indicative of the prognosis of breast cancer patients. Assessment of tumor proliferation in a clinical setting is a highly subjective and labor-intensive task. Previous efforts to automate tumor proliferation assessment by image analysis only focused on mitosis detection in predefined tumor regions. However, in a realworld scenario, automatic mitosis detection should be performed in whole-slide images (WSIs) and an automatic method should be able to produce a tumor proliferation score given a WSI as input. To address this, we organized the TUmor Proliferation Assessment Challenge 2016 (TUPAC16) on prediction of tumor proliferation scores from WSIs. The challenge dataset consisted of 500 training and 321 testing breast cancer histopathology WSIs. In order to ensure fair and independent evaluation, only the ground truth for the training dataset was provided to the challenge participants. The first task of the challenge was to predict mitotic scores, i.e., to reproduce the manual method of assessing tumor proliferation by a pathologist. The second task was to predict the gene expression based PAM50 proliferation scores from the WSI. The best performing automatic method for the first task achieved a quadratic-weighted Cohen's kappa score of κ = 0.567, 95% CI [0.464, 0.671] between the predicted scores and the ground truth. For the second task, the predictions of the top method had a Spearman's correlation coefficient of r = 0.617, 95% CI [0.581 0.651] with the ground truth. This was the first comparison study that investigated tumor proliferation assessment from WSIs. The achieved results are promising given the difficulty of the tasks and weakly-labeled nature of the ground truth. However, further research is needed to improve the practical utility of image analysis methods for this task. Copyright © 2018, The Authors. All rights reserved.

关键词： Diseases

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Symbolic-numeric integration of the dynamical cosserat equations

arXiv

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arXiv 2017年

作者： Lyakhov, Dmitry A. Gerdt, Vladimir P. Weber, Andreas G. Michels, Dominik L. Visual Computing Center King Abdullah University of Science and Technology Al Khawarizmi Building Thuwal23955-6900 Saudi Arabia Department of Computer Science Stanford University 353 Serra Mall StanfordCA94305 United States Laboratory of Information Technologies Joint Institute for Nuclear Research 6 Joliot-Curie St Dubna141980 Russia Peoples' Friendship University of Russia 6 Miklukho-Maklaya St Moscow117198 Russia Institute of Computer Science II University of Bonn Friedrich-Ebert-Allee 144 Bonn53113 Germany

We devise a symbolic-numeric approach to the integration of the dynamical part of the Cosserat equations, a system of nonlinear partial differential equations describing the mechanical behavior of slender structures, like fibers and rods. This is based on our previous results on the construction of a closed form general solution to the kinematic part of the Cosserat system. Our approach combines methods of numerical exponential integration and symbolic integration of the intermediate system of nonlinear ordinary differential equations describing the dynamics of one of the arbitrary vector-functions in the general solution of the kinematic part in terms of the module of the twist vector-function. We present an experimental comparison with the well-established generalized α-method illustrating the computational efficiency of our approach for problems in structural mechanics. Copyright © 2017, The Authors. All rights reserved.

关键词： Integration

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8-3:Invited Paper: Light Steering Projection Systems and Attributes for HDR Displays

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SID Symposium Digest of Technical Papers 2017年第1期48卷

作者： Gerwin Damberg Ronan Boitard Anders Ballestad Wolfgang Heidrich Department of Computer Science University of British Columbia Vancouver Canada MTT Innovation Inc. Vancouver Canada Visual Computing Center King Abdullah University of Science and Technology

New light steering projectors in cinema form images by moving light away from dark regions into bright areas of an image. In these systems, the peak luminance of small features can far exceed full screen white luminance. In traditional projectors where light is filtered or blocked in order to give shades of gray (or colors), the peak luminance is fixed. The luminance of chromatic features benefit in the same way as white features, and chromatic image details can be reproduced at high brightness leading to a much wider overall color gamut coverage than previously possible. Projectors of this capability are desired by the creative community to aid in and enhance storytelling. Furthermore, reduced light source power requirements of light steering projectors provide additional economic and environmental benefits. While the dependency of peak luminance level on (bright) image feature size is new in the digital cinema space, display technologies with identical characteristics such as OLED, LED LCD and Plasma TVs are well established in the home. Similarly, direct view LED walls are popular in events, advertising and architectural markets. To enable consistent color reproduction across devices in today’s content production pipelines, models that describe modern projectors and display attributes need to evolve together with HDR standards and available metadata. This paper is a first step towards rethinking legacy display descriptors such as contrast, peak luminance and color primaries in light of new display technology. We first summarize recent progress in the field of light steering projectors in cinema and then, based on new projector and existing display characteristics propose the inclusion of two simple display attributes: Maximum Average Luminance and Peak (Color) Primary Luminance. We show that the proposed attributes allow a better prediction of content reproducibility on HDR displays. To validate this assertion, we test professional

关键词： Light Steering Projection System High Dynamic Range Displays Capabilities Peak Luminance

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Improving Shadow Suppression for Illumination Robust Face Recognition

arXiv

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arXiv 2017年

作者： Zhang, Wuming Zhao, Xi Morvan, Jean-Marie Chen, Liming Laboratory of LIRIS Department of Mathematic and Computer Science Ecole Centrale de Lyon Ecully69310 France School of Management Xi'an Jiaotong University Xi'an710049 China Universite Lyon 1 Institut Camille Jordan CNRS UMR 5208 43 blvd du 11 Novembre 1918 Villeurbanne-CedexF-69622 France King Abdullah University of Science and Technology Visual Computing Center Bldg 1 Thuwal23955-6900 Saudi Arabia

2D face analysis techniques, such as face landmarking, face recognition and face verification, are reasonably dependent on illumination conditions which are usually uncontrolled and unpredictable in the real world. An illumination robust preprocessing method thus remains a significant challenge in reliable face analysis. In this paper we propose a novel approach for improving lighting normalization through building the underlying reflectance model which characterizes interactions between skin surface, lighting source and camera sensor, and elaborates the formation of face color appearance. Specifically, the proposed illumination processing pipeline enables the generation of Chromaticity Intrinsic Image (CII) in a log chromaticity space which is robust to illumination variations. Moreover, as an advantage over most prevailing methods, a photo-realistic color face image is subsequently reconstructed which eliminates a wide variety of shadows whilst retaining the color information and identity details. Experimental results under different scenarios and using various face databases show the effectiveness of the proposed approach to deal with lighting variations, including both soft and hard shadows, in face recognition. Copyright © 2017, The Authors. All rights reserved.

关键词： Lighting

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A Survey of Blue-Noise Sampling and Its Applications

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Journal of computer science & Technology 2015年第3期30卷 439-452页

作者：严冬明郭建伟王斌张晓鹏 Peter Wonk Visual Computing Center King Abdullah University of Science and Technology Thuwal 23955-6900 Saudi Arabia National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing 100190 China School of Software Tsinghua University Beijing 100084 China Department of Computer Science and Engineering Arizona State University Tempe AZ 85287 U.S.A.

In this paper, we survey recent approaches to blue-noise sampling and discuss their beneficial applications. We discuss the sampling algorithms that use points as sampling primitives and classify the sampling algorithms based on various aspects, e.g., the sampling domain and the type of algorithm. We demonstrate several well-known applications that can be improved by recent blue-noise sampling techniques, as well as some new applications such as dynamic sampling and blue-noise remeshing.

关键词： blue-noise sampling Poisson-disk sampling Lloyd relaxation rendering remeshing

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Color Object Recognition via Cross-Domain Learning on RGB-D Images

Color Object Recognition via Cross-Domain Learning on RGB-D ...

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IEEE International Conference on Robotics and Automation

作者： Yawen Huang Fan Zhu Ling Shao Alejandro F. Frangi the Center for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB) Department of Electronic and Electrical Engineering The University of Sheffield Sheffield United Kingdom the Multimedia and Visual Computing Lab Department of Electrical and Computer Engineering New York University Abu Dhabi Abu Dhabi the Computer Vision and Artificial Intelligence Group Department of Computer Science and Digital Technologies Northumbria University Newcastle upon Tyne United Kingdom

ISBN: (纸本)9781467380270

This paper addresses the object recognition problem using multiple-domain inputs. We present a novel approach that utilizes labeled RGB-D data in the training stage, where depth features are extracted for enhancing the discriminative capability of the original learning system that only relies on RGB images. The highly dissimilar source and target domain data are mapped into a unified feature space through transfer at both feature and classifier levels. In order to alleviate cross-domain discrepancy, we employ a state-of-the-art domain-adaptive dictionary learning algorithm that updates image representations in both domains and the classifier parameters simultaneously. The proposed method is trained on a RGB-D Object dataset and evaluated on the Caltech-256 dataset. Experimental results suggest that our approach can lead to significant performance gain over the state-of-the-art methods.

关键词： RGB-D enhancing discriminative capability

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROscience 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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A STT-RAM-based low-power hybrid register file for GPGPUs

A STT-RAM-based low-power hybrid register file for GPGPUs

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Design Automation Conference

作者： Gushu Li Xiaoming Chen Guangyu Sun Henry Hoffmann Yongpan Liu Yu Wang Huazhong Yang Deparrnent of E.E. Tsinghua University Beiiing China Electrical and Computer Engineering Department Carnegie Mellon University Pittsburgh PA USA Center for Energy-efficient Computing and Applications Peking University Beijing China Deparment of Computer Science University of Chicago Chicago IL USA Tsinghua University Beijing Beijing CN

ISBN: (纸本)9781479980536

Recently, general-purpose graphics processing units (GPGPUs) have been widely used to accelerate computing in various applications. To store the contexts of thousands of concurrent threads on a GPU, a large static random-access memory (SRAM)-based register file is employed. Due to high leakage power of SRAM, the register file consumes 20% to 40% of the total GPU power consumption. Thus, hybrid memory system, which combines SRAM and the emerging non-volatile memory (NVM), has been employed for register file design on GPUs. Although it has shown strong potential to alleviate the power issue of GPUs, existing hybrid memory solutions might not exploit the intrinsic feature of GPU register file. By leveraging the warp schedule on GPU, this paper proposes a hybrid register architecture which consists of a NVM-based register file and mixed SRAM-based write buffers with a warp-aware write back strategy. Simulation results show that our design can eliminate 64% of write accesses to NVM and reduce power of register file by 66% on average, with only 4.2% performance degradation. After we apply the power gating technique, the register power is further reduced to 25% of SRAM counterpart on average.

关键词： Registers Random access memory Graphics processing units Radio frequency Nonvolatile memory Power demand Benchmark testing

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Guest editors' foreword

引用

International Journal of Computational Geometry and Applications 2014年第4期24卷 259-260页

作者： Ahn, Hee-Kap Vigneron, Antoine Department of Computer Science and Engineering Pohang University of Science and Technology (POSTECH) 77 Cheongam-Ro Nam-Gu Pohang Gyeongbuk South Korea Visual Computing Center King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia

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Wasserstein propagation for semi-supervised learning 31

Wasserstein propagation for semi-supervised learning

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31st International Conference on Machine Learning, ICML 2014

作者： Solomon, Justin Rustamov, Raif M. Guibas, Leonidas Butscher, Adrian Department of Computer Science Stanford University 353 Serra Mall StanfordCA94305 United States Max Planck Center for Visual Computing and Communication Campus El 4 Saarbrücken66123 Germany

ISBN: (纸本)9781634393973

2014 Probability distributions and histograms are natural representations for product ratings, traffic measurements, and other data considered in many machine learning applications. Thus, this paper introduces a technique for graph-based semi- supervised learning of histograms, derived from the theory of optimal transportation. Our method has several properties making it suitable for this application;in particular, its behavior can be characterized by the moments and shapes of the histograms at the labeled nodes. In addition, it can be used for histograms on non-standard domains like circles, revealing a strategy for manifold-valued semi-supervised learning. We also extend this technique to related problems such as smoothing distributions on graph nodes.

关键词： Probability distributions

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