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Training, testing and benchmarking medical AI models using Clinical AIBench

BenchCouncil Transactions on Benchmarks, Standards and Evaluations 2022年第1期2卷 100037-100037页

作者： Huang, Yunyou Miao, Xiuxia Zhang, Ruchang Ma, Li Liu, Wenjing Zhang, Fan Guan, Xianglong Liang, Xiaoshuang Lu, Xiangjiang Tang, Suqing Zhang, Zhifei Guangxi Key Lab of Multi-Source Information Mining & Security School of Computer Science and Engineering & School of Software Guangxi Normal University Guilin China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China Guilin Medical University Guilin China Department of Physiology and Pathophysiology Capital Medical University Beijing China Faculty of Education Guangxi Normal University Guilin China International Open Benchmark Council Beijing China

AI technology has been used in many clinical research fields, but most AI technologies are difficult to land in real-world clinical settings. In most current clinical AI research settings, the diagnosis task is to identify different types of diseases among the given ones. However, the diagnosis in real-world settings needs dynamically developing inspection strategies based on the existing resources of medical institutions and identifying different kinds of diseases out of many possibilities. To promote the development of different clinical AI technologies and the implementation of clinical applications, we propose a benchmark named Clinical AIBench for developing, verifying, and evaluating clinical AI technologies in real-world clinical settings. Specifically, Clinical AIBench can be used for: (1) Model training and testing: Researchers can use the data to train and test their models. (2)Model evaluation: Researchers can use Clinical AIBench to objectively, fairly, and comparably evaluate various models of different researchers. (3) Clinical value evaluation: Researchers can use the clinical indicators provided by Clinical AIBench to evaluate the clinical value of models, which will be applied in real-world clinical settings. For convenience, Clinical AIBench provides three different levels of clinical settings: restricted clinical setting, which is named closed clinical setting, data island clinical setting, and real-world clinical setting, which is called open clinical setting. In addition, Clinical AIBench covers three diseases: Alzheimer's disease, COVID-19, and dental. Clinical AIBench provides python APIs to researchers. The data and source code are publicly available from the project website https://***/clinical_aibench/. © 2022 The Authors

关键词： COVID-19

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Towards Dependability-Aware Design of Hardware Systems Using Extended Program State Machines

Towards Dependability-Aware Design of Hardware Systems Using...

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IEEE International Symposium on Object/Component/Service-Oriented Real-Time Distributed Computing Workshops (ISORCW)

作者： Kim Gruttner Andreas Herrholz Ulrich Kuhne Daniel Grosse Achim Rettberg Wolfgang Nebel Rolf Drechsler HW/SW Design Methodology Group OFFIS Institute of Information Technology Oldenburg Germany CoSynth GmbH and Company KG Oldenburg Germany LSV ENS de Cachan Cachan France Group for Computer Architecture University of Brethemen Bremen Germany Embedded Hardware/Software Systems Carl von Ossietzky University슠of슠Oldenburg Oldenburg Germany

Due to the continuous shrinking of the transistor sizes which is strongly driven by Moore's law, reliability becomes a dominant design challenge for embedded systems. Reliability problems arise from permanent errors due to manufacturing, process variations, aging as well as soft errors. As a result, the hardware will consist of unreliable components and hence, the development of embedded systems has to change fundamentally. Therefore, we propose a dependability-aware design approach for hardware systems through integrating dependability into a state-of-the-art system-level design language. Our approach is based on SystemC and extends the Program State Machine model to explicitly observe, diagnose, and compensate faulty behavior. Different compensation mechanisms like run-time reconfiguration or mechanisms for error propagation can be used by the designer during refinement. They are controlled by a new exception-like mechanism. Furthermore, our approach aims to integrate functional verification as well as dependability verification with respect to given fault models.

关键词： Computational modeling Hardware Circuit faults computer architecture Fault tolerance Switches Adaptation model

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Message from the SAIP, NEMI, ECRF, Journal First, and Workshops Track Chairs

Proceedings - 2021 IEEE 18th International Conference on Sof...

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Proceedings - 2021 IEEE 18th International Conference on software architecture Companion, ICSA-C 2021 2021年 x-xi页

作者： Becker, Steffen Camara, Javier Challita, Stephanie Fehling, Christoph Jansen, Anton Kopp, Oliver Koziolek, Heiko Kruchten, Philippe Lewis, Grace A. Lilienthal, Carola Spalazzese, Romina Trubiani, Catia University of Stuttgart Software Quality and Architecture Stuttgart Germany University of York York United Kingdom University of Rennes 1 Rennes France Mercedes-Benz AG Sindelfingen Germany Philips Innovation Services Philips Eindhoven Netherlands ABB Research Ladenburg Germany University of British Columbia Electrical and Computer Engineering Canada Software Engineering Institute Carnegie Mellon University PittsburghPA United States WPS-Workplace Solutions GmbH Hamburg Germany Malmö University Dept. of Computer Science and Media Technology Malmö Sweden Gran Sasso Science Institute L'Aquila Italy

ISBN: (纸本)9781665439107

The 18th IEEE International Conference on software architecture (ICSA 2021) solicited different types of submissions structured into the following tracks: The main Technical Track (included in the ICSA main proceedings), the software architecture in Practice (SAIP) track, the New and Emerging Ideas (NEMI) track, the Early Career Researchers Forum (ECRF), the Journal First track, and the Workshop track (included in the companion volume of ICSA 2021 proceedings). Each of these tracks, except for the Technical Track, is presented in the following sections. © 2021 IEEE.

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OpenClinicalAI: enabling AI to diagnose diseases in real-world clinical settings

arXiv

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arXiv 2021年

作者： Huang, Yunyou Wang, Nana Tang, Suqin Ma, Li Hao, Tianshu Jiang, Zihan Zhang, Fan Kang, Guoxin Miao, Xiuxia Guan, Xianglong Zhang, Ruchang Zhang, Zhifei Zhan, Jianfeng Guangxi Key Lab of Multi-Source Information Mining & Security School of Computer Science and Engineering School of Software Guangxi Normal University Guilin China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China Guilin Medical University Guilin China Department of Physiology and Pathophysiology Capital Medical University Beijing China University of Chinese Academy of Sciences China International Open Benchmark Council

This paper quantitatively reveals the state-of-the-art and state-of-the-practice AI systems only achieve acceptable performance on the stringent conditions that all categories of subjects are known, which we call closed clinical settings, but fail to work in real-world clinical settings. Compared to the diagnosis task in the closed setting, real-world clinical settings pose severe challenges, and we must treat them differently. We build a clinical AI benchmark named Clinical AIBench to set up real-world clinical settings to facilitate researches. We propose an open, dynamic machine learning framework and develop an AI system named OpenClinicalAI to diagnose diseases in real-world clinical settings. The first versions of Clinical AIBench and OpenClinicalAI target Alzheimer’s disease. In the real-world clinical setting, OpenClinicalAI significantly outperforms the state-of-the-art AI system. In addition, OpenClinicalAI develops personalized diagnosis strategies to avoid unnecessary testing and seamlessly collaborates with clinicians. It is promising to be embedded in the current medical systems to improve medical services. © 2021, CC BY.

关键词： Diagnosis

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A NEW GENERATION OF HIGH-PERFORMANCE PLANING CRAFT

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NAVAL ENGINEERS JOURNAL 1985年第4期97卷 234-247页

作者： JONS, OP KOELBEL, J SHELDON, R Otto Jons:is vice president of engineering at Advanced Marine Enterprises Inc. He received a Diplom Ing. from the Technical University of Hannover W. Germany and an M.S. in naval architecture and marine engineering from the Massachusetts Institute of Technology in 1976. He then joined Litton Ship Systems to work on the DD-963 and LHA programs. He was principal research scientist at Hydronautics and technical director with Designers and Planners before forming in 1976 together with S. Glatz and J. Drewry Advanced Marine where he is responsible for engineering and technical matters on behalf of the corporation. Joseph Koelbel: a senior naval architect at Advanced Marine Enterprises Inc. has for many years specialized in the design of high-speed rough-water boats including a record-setting race boat passenger boats and patrol boats such as the CPIC and the PBM. He received special recognition for his contributions to and editing of the Planing Concept Volume of the Advanced Naval Vehicles Concept Evaluation Study. He is a member of ASNE SNAME (past chairman planing boats panel of the Hydrodynamics Committee) and ABYC. He is the author of a number of published papers on small-craft design. Ray Sheldon:is the head of computer applications at Advanced Marine Enterprises Inc. where he directs activities in computer-aided ship design software development and ship performance prediction. Previously at the Hydronautics Ship Model Basin he was involved in nearly all aspects of ship hydrodynamics and the development and refinement of test methodologies and equipment. Mr. Sheldon is a 1973 graduate of Webb Institute of Naval Architecture (B.S. naval architecture and marine engineering) a member of ASNE and an associate member of SNAME. He is the author of several published papers concerning ship design and experimental methods.

Key aspects of the design development of a capable yet affordable high performance craft are addressed. These aspects include the development of mission requirements, the rationale for major design decisions, performance capabilities, and system and subsystem selection. The superior performance of the design, as demonstrated by an extensive model test program, led to the decision to develop a family of advanced fast patrol boat concepts. Selected family members are also briefly introduced. The paper, furthermore, demonstrates the successful integration of many major computer-aided design (CAD) programs currently in use for U.S. Navy ship design.

关键词： NAVAL VESSELS

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Correction to: High-speed tracking based on multi-CF filters and attention mechanism

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Signal, Image and Video Processing 2020年第3期14卷 637-637页

作者： Fan, Songyuan Wang, Rui Wu, Zhihao Rho, Seungmin Liu, Shaohui Xiong, Jiaxin Fu, Sen Jiang, Feng School of Computer Science Harbin Institute of Technology Harbin China School of Architecture Harbin Institute of Technology Harbin China College of Software and Convergence Technology Sejong University Seoul Republic of Korea Beijing Institute of Technology Beijing China Shanghai Radio Equipment Research Institute Shanghai China

Unfortunately, the Acknowledgement section has been published as blinded version.

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Improved meet-in-the-middle attacks on 7-round AES-192 and 8-round AES-256

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Tongxin Xuebao/Journal on Communications 2010年第9 A期31卷 197-201页

作者： Dong, Xiao-Li Hu, Yu-Pu Chen, Jie Li, Shun-Bo Yang, Yang Key Laboratory of Computer Networks and Information Security of Ministry of Education Xidian University Xi'an 710071 China State Key Laboratory of Information Security Institute of Software Chinese Academy of Sciences Beijing 100049 China School of Science Xi'an University of Architecture and Technology Xi'an 710055 China

A new variant of 5-round distinguisher of meet-in-the-middle attack on AES was constructed by exploiting the properties of the round transform of AES. Based on the variant of distinguisher, meet-in-the-middle attacks on 7-round AES-192 and 8-round AES-256 were presented by using the time-memory tradeoff approach. It is shown that the new results are better than the cryptanalytic results presented at FSE2008 on reduced AES in terms of the time complexity and the memory complexity.

关键词： Cryptography

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Package of the vector math library based on the sunway processor

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Ruan Jian Xue Bao/Journal of software 2014年 25卷 70-79页

作者： Xie, Qing-Chun Zhang, Yun-Quan Li, Yan Pang, Ren-Bo Wu, Zai-Long Lu, Yong-Quan Gao, Peng-Dong High Performance Computing Center Communication University of China Beijing100024 China Laboratory of Parallel Computing Institute of Software The Chinese Academy of Sciences Beijing100190 China State Key Laboratory of Computer Architecture Institute of Computing Technology The Chinese Academy of Sciences Beijing100190 China Department of Computer and Network National Marine Environmental Forecasting Center Beijing100081 China School of Information Sceience and Technology The Ocean University of China Qingdao266100 China

This paper first introduces the SIMD (single instruction multiple data) extension technology and presents three ways to use SIMD instructions. It is considered that calling the third party library, which is optimized for the target platform by using those instructions, is the best way to benefit the developers. Next, it introduces the China-developed SW-1600 CPU, and a software package called SW-VML, which consists of many mathematical functions, by using the SIMD extension technology. In order to solve the additional overhead caused by unaligned address access and transformation between vector and scalar array, the paper provides some performance optimized methods, such as aligned address access, simplifying vector condition branch and loop unrolling. An upgrade to SW-VML is also offered to support multi-thread with OpenMP. Finally, functions in the package are tested using arrays of different sizes on SW-1600, and the test results show that high performance is achieved with the technology of the SIMD vectorization. Compared with the traditional methods of the scalar calculation, the average speedup is up to 2.06. The performance speedup of package using 4 threads is up to 2.26 compared to using a single thread. SW-VML is a common vector function package for domestic Sunway processor series, and it can be used as a basic toolkit which is beneficial to high performance computing on Sunway platform. ©2014 ISCAS.

关键词： Vectors

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Analysis and Design of Mobile Model for DTN

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电子学报(英文版) 2009年第4期

作者： LIN Zhiting LIN Zhiting QU Yugui QU Yugui JI Yusheng JI Yusheng ZHAO Baohua ZHAO Baohua Department of Computer Science University of Science and Technology of China Hefei 230027 China Department of Computer Science University of Science and Technology of China Hefei 230027 China Province Key Laboratory of Software in Computing and Communication Hcfei 230027 China Province Key Laboratory of Software in Computing and Communication Hcfei 230027 China Information Systems Architecture Science Research Division National Institute of Info~natics Tokyo Japan) Information Systems Architecture Science Research Division National Institute of Info~natics Tokyo Japan)

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NTIRE 2024 Challenge on Low Light Image Enhancement: Methods and Results

arXiv

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arXiv 2024年

作者： Liu, Xiaoning Wu, Zongwei Li, Ao Vasluianu, Florin Zhang, Yulun Gu, Shuhang Zhang, Le Zhu, Ce Timofte, Radu Jin, Zhi Wu, Hongjun Wang, Chenxi Ling, Haitao Cai, Yuanhao Bian, Hao Zheng, Yuxin Lin, Jing Yuille, Alan Shao, Ben Guo, Jin Liu, Tianli Wu, Mohao Feng, Yixu Hou, Shuo Lin, Haotian Zhu, Yu Wu, Peng Dong, Wei Sun, Jinqiu Zhang, Yanning Yan, Qingsen Zou, Wenbin Yang, Weipeng Li, Yunxiang Wei, Qiaomu Ye, Tian Chen, Sixiang Zhang, Zhao Zhao, Suiyi Wang, Bo Luo, Yan Zuo, Zhichao Wang, Mingshen Wang, Junhu Wei, Yanyan Sun, Xiaopeng Gao, Yu Huang, Jiancheng Chen, Hongming Chen, Xiang Tang, Hui Chen, Yuanbin Zhou, Yuanbo Dai, Xinwei Qiu, Xintao Deng, Wei Gao, Qinquan Tong, Tong Li, Mingjia Hu, Jin He, Xinyu Guo, Xiaojie Sabarinathan Uma, K. Sasithradevi, A. Sathya Bama, B. Mohamed Mansoor Roomi, S. Srivatsav, V. Wang, Jinjuan Sun, Long Chen, Qiuying Shao, Jiahong Zhang, Yizhi Conde, Marcos V. Feijoo, Daniel Benito, Juan C. García, Alvaro Lee, Jaeho Kim, Seongwan Sharif, S.M.A. Khujaev, Nodirkhuja Tsoy, Roman Murtaza, Ali Khairuddin, Uswah Faudzi, Ahmad'Athif Mohd Malagi, Sampada Joshi, Amogh Akalwadi, Nikhil Desai, Chaitra Tabib, Ramesh Ashok Mudenagudi, Uma Lian, Wenyi Lian, Wenjing Kalyanshetti, Jagadeesh Aralikatti, Vijayalaxmi Ashok Yashaswini, Palani Upasi, Nitish Hegde, Dikshit Patil, Ujwala Sujata, C. Yan, Xingzhuo Hao, Wei Fu, Minghan Choksy, Pooja Sarvaiya, Anjali Upla, Kishor Raja, Kiran Yan, Hailong Zhang, Yunkai Li, Baiang Zhang, Jingyi Zheng, Huan University of Electronic Science and Technology of China China Computer Vision Lab University of Würzburg Germany Shanghai Jiao Tong University China Computer Vision Lab ETH Zurich Switzerland School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen Campus Guangdong Shenzhen518107 China Guangdong Provincial Key Laboratory of Fire Science and Technology Guangzhou510006 China Johns Hopkins University United States Tsinghua University China Zhejiang Dahua Technology Co. Ltd. China School of Computer Science Northwestern Polytechnical University China School of Software Northwestern Polytechnical University China School of Computer Science Xi'an University of Architecture and Technology China South China University of Technology China Fuzhou University China Chengdu University of Information Technology China Hong Kong University of Science and Technology Guangzhou China Hefei University of Technology China Individual Researcher Shenyang Aerospace University China Nanjing University of Science and Technology China Fuzhou University Fuzhou China Imperial Vision Technology Fuzhou China Tianjin University China Couger Inc Japan Sasi Institute of Technology & Engineering India Vellore Institute of Technology India Thiagarajar college of engineering India Coventry University United Kingdom Cidaut AI CVLab University of Wuerzburg Germany Opt-AI University Teknologi Malaysia Kuala Lumpur Malaysia Universiti Teknologi Malaysia Kuala Lumpur Malaysia KLE Technological University Karnataka Hubballi India School of Electronics and Communication Engineering KLE Technological University Karnataka Hubballi India School of Computer Science and Engineering KLE Technological University Karnataka Hubballi India Uppsala University Sweden Northeastern University United States Bosch Investment Ltd. China Fortinet Inc. University of Saskatchewan Canada Sardar Vallabhbhai National Institute of Technology India Norwegian University of Science and Technolog

This paper reviews the NTIRE 2024 low light image enhancement challenge, highlighting the proposed solutions and results. The aim of this challenge is to discover an effective network design or solution capable of generating brighter, clearer, and visually appealing results when dealing with a variety of conditions, including ultra-high resolution (4K and beyond), non-uniform illumination, backlighting, extreme darkness, and night scenes. A notable total of 428 participants registered for the challenge, with 22 teams ultimately making valid submissions. This paper meticulously evaluates the state-of-the-art advancements in enhancing low-light images, reflecting the significant progress and creativity in this field. © 2024, CC BY.

关键词： Image enhancement

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