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The medical segmentation decathlon

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arXiv 2021年

作者： Antonelli, Michela Reinke, Annika Bakas, Spyridon Farahani, keyvan Kopp-Schneider, Annette Landman, Bennett A. Litjens, Geert Menze, Bjoern Ronneberger, Olaf Summers, Ronald M. van Ginneken, Bram Bilello, Michel Bilic, Patrick Christ, Patrick F. Do, Richard K.G. Gollub, Marc J. Heckers, Stephan H. Huisman, Henkjan Jarnagin, William R. McHugo, Maureen K. Napel, Sandy Golia Pernicka, Jennifer S. Rhode, Kawal Tobon-Gomez, Catalina Vorontsov, Eugene Hulsmon, Hankan Meakin, James A. Ourselin, Sebastien Wiesenfarth, Manuel Arbeláez, Pablo Bae, Byeonguk Chen, Sihong Daza, Laura Feng, Jianjiang He, Baochun Isensee, Fabian Ji, Yuanfeng Jia, Fucang Kim, Namkug Kim, Ildoo Merhof, Dorit Pai, Akshay Park, Beomhee Perslev, Mathias Rezaiifar, Ramin Rippel, Oliver Sarasua, Ignacio Shen, Wei Son, Jaemin Wachinger, Christian Wang, Liansheng Wang, Yan Xia, Yingda Xu, Daguang Xu, Zhanwei Zheng, Yefeng Simpson, Amber L. Maier-Hein, Lena Cardoso, M. Jorge School of Biomedical Engineering & Imaging Sciences King’s College London London United Kingdom Heidelberg Germany Heidelberg Germany Faculty of Mathematics and Computer Science University of Heidelberg Heidelberg Germany University of Pennsylvania PhiladelphiaPA United States BethesdaMD United States Heidelberg Germany Electrical Engineering and Computer Science Vanderbilt University NashvilleTN United States Radboud University Medical Center Radboud Institute for Health Sciences Nijmegen Netherlands Quantitative Biomedicine University of Zurich Zurich Switzerland DeepMind London United Kingdom School of Computing Department of Biomedical and Molecular Sciences Queen’s University KingstonON Canada Department of Surgery Memorial Sloan Kettering Cancer Center New YorkNY United States BethesdaMD United States Department of Informatics Technische Universität München Germany Department of Radiology Memorial Sloan Kettering Cancer Center New YorkNY United States Department of Psychiatry & Behavioral Sciences Vanderbilt University Medical Center NashvilleTN United States Department of Radiology Stanford University StanfordCA United States Department of Computer Science and Software Engineering École Polytechnique de Montréal 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 Medical Faculty University of Heidelberg Heidelberg Germany VUNO Inc. Seoul Korea Republic of Universidad de los Andes Colombia Department of Automation Tsinghua University Beijing China Heidelberg Germany Department of Computer Science Xiamen University Xiamen361005 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Kakao Brain Korea Republic of Institute of Imaging & Computer Vision RWTH Aachen University Aachen Germany Cere

International challenges have become the de facto standard for comparative assessment of image analysis algorithms given a specific task. Segmentation is so far the most widely investigated medical image processing task, but the various segmentation challenges have typically been organized in isolation, such that algorithm development was driven by the need to tackle a single specific clinical problem. We hypothesized that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. To investigate the hypothesis, we organized the Medical Segmentation Decathlon (MSD)—a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities. The underlying data set was designed to explore the axis of difficulties typically encountered when dealing with medical images, such as small data sets, unbalanced labels, multi-site data and small objects. The MSD challenge confirmed that algorithms with a consistent good performance on a set of tasks preserved their good average performance on a different set of previously unseen tasks. Moreover, by monitoring the MSD winner for two years, we found that this algorithm continued generalizing well to a wide range of other clinical problems, further confirming our hypothesis. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms are mature, accurate, and generalize well when retrained on unseen tasks;(2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability;(3) the training of accurate ai segmentation models is now commoditized to non ai *** Codes 68T07. © 2021, CC BY-NC-SA.

关键词： Image segmentation

Reports of the workshops held at the 2019 aaai conference on artificial intelligence

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Reports of the workshops held at the 2019 aaai conference on...

作者： Barash, Guy Castillo-Effen, Mauricio Chhaya, Niyati Clark, Peter Espinoza, Huáscar Farchi, Eitan Geib, Christopher Gundersen, Odd Erik Heígeartaig, Seán Ó Hernández-Orallo, José Hori, Chiori Huang, Xiaowei Jaidka, Kokil Kapanipathi, Pavan Keren, Sarah Kim, Seokhwan Lanctot, Marc Lange, Danny Martinez, David Mattar, Marwan Mausam Mcauley, Julian Michalowski, Martin Mirsky, Reuth Mottaghi, Roozbeh Osborn, Joseph C. Pérolat, Julien Schmid, Martin Shaban-Nejad, Arash Shehory, Onn Srivastava, Biplav Streilein, William Talamadupula, Kartik Togelius, Julian Yoshino, Koichiro Zhang, Quanshi Zitouni, Imed Western Digital Lockheed Martin United States Adobe Research Allen Institute for Artificial Intelligence United States Commissariat àĺÉnergie Atomique France IBM Research Haifa Israel SIFT LLC Norwegian University of Science and Technology AI Officer at TrønderEnergi Norway University of Cambridge's Centre United Kingdom Universitat Politècnica de València Spain United States Department of Computer Science University of Liverpool United Kingdom Nanyang Tech University Singapore IBM Research AI Harvard University United States Research Scientist at Adobe Research United States Research scientist at DeepMind Alberta Edmonton Canada Unity Technologies University of California San Diego United States MIT Lincoln Laboratory Unity Technologies Computer Science at Indian Institute of Technology Delhi India University of Minnesota School of Nursing United States University of Texas United States Allen Institutem for Artificial Intelligence United States Pomona College United States DeepMind London United Kingdom DeepMind Alberta Edmonton Canada University of Tennessee Health Science Center-Oak Ridge National Laboratory United States Bar Ilan University Israel IBM's ArmonkNY United States IBM Research AI Tandon School of Engineering New York University United States Japan MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China Conversation understanding group at Microsoft

Affective Content Analysis and CL-Aff Shared Task: In Pursuit of Happiness The Affective Content Analysis workshop series held at the AAai Conference on Artificial Intelligence is an interdisciplinary platform intended to engage the ai and machinelearning communities about open problems in affective content analysis and understanding, with a special focus on affect in language and text. The theme of this second workshop was modeling affect-in-action, with a shared task (CL-Aff-in pursuit of happiness) to encourage the development of new models and approaches for modeling happy moments. Copyright © 2019, Association for the Advancement of Artificial Intelligence.

关键词： Artificial intelligence

Invocation-driven neural approximate computing with a multiclass-classifier and multiple approximators

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arXiv 2018年

作者： Song, Haiyue Xu, Chengwen Xu, Qiang Song, Zhuoran Jing, Naifeng Liang, Xiaoyao Jiang, Li MoE Key Lab of Artificial Intelligence AI Institute School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University China Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong

Neural approximate computing gains enormous energy-efficiency at the cost of tolerable quality-loss. A neural approximator can map the input data to output while a classifier determines whether the input data are safe to approximate with quality guarantee. However, existing works cannot maximize the invocation of the approximator, resulting in limited speedup and energy saving. By exploring the mapping space of those target functions, in this paper, we observe a nonuniform distribution of the approximation error incurred by the same approximator. We thus propose a novel approximate computing architecture with a Multiclass-Classifier and Multiple Approximators (MCMA). These approximators have identica network topologies, and thus can share the same hardware resource in an neural processing unit(NPU) clip. In the runtime, MCMA can swap in the invoked approximator by merely shipping the synapse weights from the on-chip memory to the buffers near MAC within a cycle. We also propose efficient co-training methods for such MCMA architecture. Experimental results show a more substantial invocation of MCMA as well as the gain of energy-efficiency. Copyright © 2018, The Authors. All rights reserved.

关键词： Input output programs

Deep RNN framework for visual sequential applications

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arXiv 2018年

作者： Pang, Bo Zha, Kaiwen Cao, Hanwen Shi, Chen Lu, Cewu Shanghai Jiao Tong University China Department of Computer Science and Engineering Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China

Extracting temporal and representation features efficiently plays a pivotal role in understanding visual sequence information. To deal with this, we propose a new recurrent neural framework that can be stacked deep effectively. There are mainly two novel designs in our deep RNN framework: one is a new RNN module called Context Bridge Module (CBM) which splits the information flowing along the sequence (temporal direction) and along dep.h (spatial representation direction), making it easier to train when building deep by balancing these two directions;the other is the Overlap Coherence Training Scheme that reduces the training complexity for long visual sequential tasks on account of the limitation of computing resources. We provide empirical evidence to show that our deep RNN framework is easy to optimize and can gain accuracy from the increased dep.h on several visual sequence problems. On these tasks, we evaluate our deep RNN framework with 15 layers, 7× than conventional RNN networks, but it is still easy to train. Our deep framework achieves more than 11% relative improvements over shallow RNN models on Kinetics, UCF-101, and HMDB-51 for video classification. For auxiliary annotation, after replacing the shallow RNN part of Polygon-RNN with our 15-layer deep CBM, the performance improves by 14.7%. For video future prediction, our deep RNN improves the state-of-the-art shallow model’s performance by 2.4% on PSNR and SSIM. The code and trained models are published accompanied by this paper: https://***/BoPang1996/Deep-RNN-Framework. Copyright © 2018, The Authors. All rights reserved.

关键词： Machine learning

Scrdet: Towards more robust detection for small, cluttered and rotated objects

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arXiv 2018年

作者： Yang, Xue Yang, Jirui Yan, Junchi Zhang, Yue Zhang, Tengfei Guo, Zhi Sun, Xian Fu, Kun China University of Chinese Academy of Sciences Beijing China Department of Computer Science and Engineering Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University

Object detection has been a building block in computer vision. Though considerable progress has been made, there still exist challenges for objects with small size, arbitrary direction, and dense distribution. Apart from natural images, such issues are especially pronounced for aerial images of great importance. This paper presents a novel multi-category rotation detector for small, cluttered and rotated objects, namely SCRDet. Specifically, a sampling fusion network is devised which fuses multi-layer feature with effective anchor sampling, to improve the sensitivity to small objects. Meanwhile, the supervised pixel attention network and the channel attention network are jointly explored for small and cluttered object detection by suppressing the noise and highlighting the objects feature. For more accurate rotation estimation, the IoU constant factor is added to the smooth L1 loss to address the boundary problem for the rotating bounding box. Extensive experiments on two remote sensing public datasets DOTA, NWPU VHR-10 as well as natural image datasets COCO, VOC2007 and scene text data ICDAR2015 show the state-of-the-art performance of our detector. The code and models will be available at https://***/DetectionTeamUCAS. Copyright © 2018, The Authors. All rights reserved.

关键词： Rotation

Author Correction: π-HuB: the proteomic navigator of the human body

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Nature 2025年第8046期637卷 E22页

作者： Fuchu He Ruedi Aebersold Mark S Baker Xiuwu Bian Xiaochen Bo Daniel W Chan Cheng Chang Luonan Chen Xiangmei Chen Yu-Ju Chen Heping Cheng Ben C Collins Fernando Corrales Jürgen Cox Weinan E Jennifer E Van Eyk Jia Fan Pouya Faridi Daniel Figeys George Fu Gao Wen Gao Zu-Hua Gao Keisuke Goda Wilson Wen Bin Goh Dongfeng Gu Changjiang Guo Tiannan Guo Yuezhong He Albert J R Heck Henning Hermjakob Tony Hunter Narayanan Gopalakrishna Iyer Ying Jiang Connie R Jimenez Lokesh Joshi Neil L Kelleher Ming Li Yang Li Qingsong Lin Cui Hua Liu Fan Liu Guang-Hui Liu Yansheng Liu Zhihua Liu Teck Yew Low Ben Lu Matthias Mann Anming Meng Robert L Moritz Edouard Nice Guang Ning Gilbert S Omenn Christopher M Overall Giuseppe Palmisano Yaojin Peng Charles Pineau Terence Chuen Wai Poon Anthony W Purcell Jie Qiao Roger R Reddel Phillip J Robinson Paola Roncada Chris Sander Jiahao Sha Erwei Song Sanjeeva Srivastava aihua Sun Siu Kwan Sze Chao Tang Liujun Tang Ruijun Tian Juan Antonio Vizcaíno Chanjuan Wang Chen Wang Xiaowen Wang Xinxing Wang Yan Wang Tobias Weiss Mathias Wilhelm Robert Winkler Bernd Wollscheid Limsoon Wong Linhai Xie Wei Xie Tao Xu Tianhao Xu Liying Yan Jing Yang Xiao Yang John Yates Tao Yun Qiwei Zhai Bing Zhang Hui Zhang Lihua Zhang Lingqiang Zhang Pingwen Zhang Yukui Zhang Yu Zi Zheng Qing Zhong Yunping Zhu State Key Laboratory of Medical Proteomics Beijing Proteome Research Center National Center for Protein Sciences (Beijing) Beijing Institute of Lifeomics Beijing China. hefc@***. International Academy of Phronesis Medicine (Guangdong) Guangdong China. hefc@***. Department of Biology Institute of Molecular Systems Biology ETH Zurich Zurich Switzerland. aebersold@imsb.biol.ethz.ch. Macquarie Medical School Macquarie University Sydney New South Wales Australia. Institute of Pathology and Southwest Cancer Center Southwest Hospital Third Military Medical University (Army Medical University) and Key Laboratory of Tumor Immunopathology Ministry of Education of China Chongqing China. Institute of Health Service and Transfusion Medicine Beijing China. Department of Pathology and The Sidney Kimmel Comprehensive Cancer Center Johns Hopkins University Baltimore MD USA. State Key Laboratory of Medical Proteomics Beijing Proteome Research Center National Center for Protein Sciences (Beijing) Beijing Institute of Lifeomics Beijing China. Key Laboratory of Systems Biology Center for Excellence in Molecular Cell Science Shanghai Institute of Biochemistry and Cell Biology Chinese Academy of Sciences Shanghai China. Department of Nephrology First Medical Center of Chinese PLA General Hospital Nephrology Institute of the Chinese People's Liberation Army State Key Laboratory of Kidney Diseases National Clinical Research Center for Kidney Diseases Beijing Key Laboratory of Kidney Disease Research Beijing China. Institute of Chemistry Academia Sinica Taipei China. National Biomedical Imaging Center State Key Laboratory of Membrane Biology Institute of Molecular Medicine Peking-Tsinghua Center for Life Sciences College of Future Technology Peking University Beijing China. School of Biological Sciences Queen's University of Belfast Belfast UK. Functional Proteomics Laboratory Centro Nacional de Biotecnología-CSIC Madrid Spain. Computational Systems Biochemistry Research Group Ma

Image denoising based on overcomplete topographic sparse coding

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Image denoising based on overcomplete topographic sparse cod...

20th International Conference on Neural Information Processing, ICONIP 2013

作者： Zhao, Haohua Luo, Jun Huang, Zhiheng Nagumo, Takefumi Murayama, Jun Zhang, Liqing MOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems Dep. of Computer Science and Engineering Shanghai Jiao Tong Univ. Shanghai China SONY Corporation Tokyo Japan

ISBN: (纸本)9783642420504

This paper presents a novel image denoising framework using overcomplete topographic model. To adapt to the statistics of natural images, we impose sparseness constraints on the denoising model. Based on the overcomplete topographic model, our denoising system improves over previous work on the following aspects: multi-category based sparse coding, adaptive learning, local normalization, and shrinkage function. A large number of simulations have been performed to show the performance of the modified model, demonstrating that the proposed model achieves better denoising performance. © Springer-Verlag 2013.

关键词： Shrinkage

Automatic semantic web service composition via agent intention execution in AgentSpeak 07

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Automatic semantic web service composition via agent intenti...

IEEE/WIC/ACM International Conference on Web Intelligence, WI 2007

作者： Li, Huan Yu, Fan Qin, Zheng Qin, Jun Yang, Bo Dep. of Computer Science and Technology Xian JiaoTong University Dongguan University of Technology Key Lab. for ISS of MOE Software School Tsinghua University Dep. of Computer Science and Technology Xian JiaoTong University Tsinghua University Manchester Business School Dep. of Computer Science and Technology Xian JiaoTong University

ISBN: (纸本)0769530265

ai planning is the main stream method for automatic semantic web service composition (SWSC) research. However, planning based SWSC method can only return service composition upon user requirement description and lacks flexibility to deal with environment change. Deliberate agent architecture, such as BDI agent, is hopeful to make SWSC more intelligent. In this paper, we propose an automatic SWSC enabling method for AgentSpeak agent. Firstly, conversion algorithm from OWL-S web service description to agent's plan set (OWLS2APS) is presented. Target service is converted to agent's goal and related services are converted into agent's plan set. Then, SWSC is automatically performed through agent's intention formation. Agent invokes web service according to service sequence converted back from its intention. Agent can behave rationally with rules or ask for human intervention when SWSC or service invocation is not feasible. At last, a case study on enterprise credit rating service composition is presented to illustrate the method. © 2007 IEEE.

关键词： Web services