检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Christodoulou, Evangelia Reinke, Annika Houhou, Rola Kalinowski, Piotr Erkan, Selen Sudre, Carole H. Burgos, Ninon Boutaj, Sofiène Loizillon, Sophie Solal, Maëlys Rieke, Nicola Cheplygina, Veronika Antonelli, Michela Mayer, Leon D. Tizabi, Minu D. Jorge Cardoso, M. Simpson, Amber Jäger, Paul F. Kopp-Schneider, Annette Varoquaux, Gaël Colliot, Olivier Maier-Hein, Lena Heidelberg Div. Intelligent Medical Systems Germany AI Health Innovation Cluster Germany NCT Heidelberg a partnership between DKFZ Heidelberg University Hospital Germany DKFZ Heidelberg Helmholtz Imaging Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Germany DKFZ Heidelberg Interactive Machine Learning Group Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London United Kingdom Sorbonne Université Institut du Cerveau - Paris Brain Institute - ICM CNRS Inria Inserm AP-HP Hôpital de la Pitié-Salpêtrière France NVIDIA Germany Department of Computer Science IT University of Copenhagen Denmark Centre for Medical Image Computing University College London United Kingdom School of Computing Queen’s University Canada Department of Biomedical and Molecular Sciences Queen’s University Canada Division of Biostatistics DKFZ Germany Parietal project team INRIA Saclay-Île de France France Faculty of Mathematics and Computer Science Heidelberg University Germany Medical Faculty Heidelberg University Germany

Medical imaging is spearheading the AI transformation of healthcare. Performance reporting is key to determine which methods should be translated into clinical practice. Frequently, broad conclusions are simply derived from mean performance values. In this paper, we argue that this common practice is often a misleading simplification as it ignores performance variability. Our contribution is threefold. (1) Analyzing all MICCAI segmentation papers (n = 221) published in 2023, we first observe that more than 50% of papers do not assess performance variability at all. Moreover, only one (0.5%) paper reported confidence intervals (CIs) for model performance. (2) To address the reporting bottleneck, we show that the unreported standard deviation (SD) in segmentation papers can be approximated by a second-order polynomial function of the mean Dice similarity coefficient (DSC). Based on external validation data from 56 previous MICCAI challenges, we demonstrate that this approximation can accurately reconstruct the CI of a method using information provided in publications. (3) Finally, we reconstructed 95% CIs around the mean DSC of MICCAI 2023 segmentation papers. The median CI width was 0.03 which is three times larger than the median performance gap between the first and second ranked method. For more than 60% of papers, the mean performance of the second-ranked method was within the CI of the first-ranked method. We conclude that current publications typically do not provide sufficient evidence to support which models could potentially be translated into clinical practice. © 2024, CC BY.

关键词： Medical imaging

来源：评论

学校读者我要写书评

暂无评论

MONAI: An open-source framework for deep learning in healthcare

arXiv

引用

arXiv 2022年

作者： Cardoso, M. Jorge Li, Wenqi Brown, Richard Ma, Nic Kerfoot, Eric Wang, Yiheng Murrey, Benjamin Myronenko, Andriy Zhao, Can Yang, Dong Nath, Vishwesh He, Yufan Xu, Ziyue Hatamizadeh, Ali Zhu, Wentao Liu, Yun Zheng, Mingxin Tang, Yucheng Yang, Isaac Zephyr, Michael Hashemian, Behrooz Alle, Sachidanand Darestani, Mohammad Zalbagi Budd, Charlie Modat, Marc Vercauteren, Tom Wang, Guotai Li, Yiwen Hu, Yipeng Fu, Yunguan Gorman, Benjamin Johnson, Hans Genereaux, Brad Erdal, Barbaros S. Gupta, Vikash Diaz-Pinto, Andres Dourson, Andre Maier-Hein, Lena Jaeger, Paul F. Baumgartner, Michael Kalpathy-Cramer, Jayashree Flores, Mona Kirby, Justin Cooper, Lee A.D. Roth, Holger R. Xu, Daguang Bericat, David Floca, Ralf Zhou, S. Kevin Shuaib, Haris Farahani, Keyvan Maier-Hein, Klaus H. Aylward, Stephen Dogra, Prerna Ourselin, Sebastien Feng, Andrew School of Biomedical Engineering & Imaging Sciences King’s College London London United Kingdom NVIDIA Corporation Santa Clara and Bethesda United States School of Electrical and Computer Engineering Rice University Houston United States Department of Engineering Science University of Oxford Oxford United Kingdom Department of Medical Physics and Biomedical Engineering University College London London United Kingdom Department of BioAI InstaDeep Ltd London United Kingdom Department of Electrical & Computer Engineering University of Iowa Iowa City United States Mayo Clinic Jacksonville United States Mars Incorporated United States Div. Intelligent Medical Systems German Cancer Research Center Heidelberg Germany Division of Medical Image Computing German Cancer Research Center Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany School of Biomedical Engineering Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou China Department of Medical Physics Guy’s & St Thomas’ NHS Foundation Trust London United Kingdom National Cancer Institute Bethesda United States Kitware Inc. Clifton Park United States Department of Ophthalmology University of Colorado Aurora United States Frederick National Laboratory for Cancer Research Frederick United States Department of Pathology Northwestern University Chicago United States

Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagnose, prognose, and intervene on human disease. For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e.g. geometry, physiology, physics) of medical data being processed. This work introduces MONAI, a freely available, community-supported, and consortium-led PyTorch-based framework for deep learning in healthcare. MONAI extends PyTorch to support medical data, with a particular focus on imaging, and provide purpose-specific AI model architectures, transformations and utilities that streamline the development and deployment of medical AI models. MONAI follows best practices for software-development, providing an easy-to-use, robust, well-documented, and well-tested software framework. MONAI preserves the simple, additive, and compositional approach of its underlying PyTorch libraries. MONAI is being used by and receiving contributions from research, clinical and industrial teams from around the world, who are pursuing applications spanning nearly every aspect of healthcare. © 2022, CC BY.

关键词： Clinical research

来源：评论

学校读者我要写书评

暂无评论

An efficient domain cascading approach for linear discontinuous Galerkin schemes 12

An efficient domain cascading approach for linear discontinu...

引用

12th European Conference on Antennas and Propagation, EuCAP 2018

作者： Dogan, Doganay Kuzuoglu, Mustafa Electrical and Electronics Engineering Department Middle East Technical University Ankara Turkey Radar and Electronic Warfare Systems Div. Aselsan A.S. Ankara Turkey

ISBN: (纸本)9781785617911

A discontinuous Galerkin finite element method based domain decomposition technique is presented for solving Maxwell's Equations. The method can be implemented with either time domain or frequency domain solutions for sub-domains. The problem in each sub domain is solved using open boundaries. A connection in frequency domain is then established between the solved outward and inward fluxes of the sub-domains touching each other. No field continuity is imposed giving the technique high computational efficiency due to the lack of Schwarz iterations. The sub-domains are cascaded in a non-iterative manner. The methods' core elements are verified by comparing them to commercial solvers using a two-element antenna problem and a periodic boundary problem in 2D space. © Institution of engineering and *** Rights Reserved.

关键词： Maxwell equations

来源：评论

学校读者我要写书评

暂无评论

A W-Band Butterfly Shaped Series-Fed Circular Polarized Phased Array Antenna for CubeSats

A W-Band Butterfly Shaped Series-Fed Circular Polarized Phas...

引用

IEEE International Symposium on Phased Array systems and Technology

作者： Ghanshyam Mishra Satish K. Sharma Jia-Chi S. Chieh Electrical and Computer Engineering San Diego State University San Diego CA USA Adv. Integrated Circuits and Sensors Div. SPAWAR Systems Center Pacific San Diego CA USA

ISBN: (数字)9781728130491

ISBN: (纸本)9781728130507

A novel Butterfly-shaped series-fed circularly polarized phased array antenna integrated with a beamforming board is proposed for a 1U surface (10 cm ×10 cm) of a CubeSat at 86 GHz. The proposed CubeSat beacon will allow a high data rate downlink as well as channel characterization at W-band. Right-hand circular polarization (RHCP) is generated using a new sequentially rotated series-fed radiating element known as the Butterfly antenna. The series-fed Butterfly antenna allows symmetric single plane beam steering with low cross-polarization. A planar array consisting of eight 1 ×24 series-fed Butterfly antenna is designed. The proposed phased array demonstrates a peak simulated RHCP realized gain of 26 dBic and symmetric beam steering of ±34 ° in the φ = 0 ° plane for a 3-dB reduction in the gain. A 4-bit phase shifter design is analyzed and developed on Global Foundries silicon-germanium 9HP technology. Monte-Carlo analyses are also performed to investigate the changes in the radiation patterns caused by random errors in the 4-bit phase shifter and random element failures. The integrated phased array with the beamforming board has been fabricated. The assembly of the phase shifters and the low noise amplifiers are to be done. If completed, the integrated phased array would be measured in the mini-compact anechoic chamber.

关键词： Phased arrays Gain Beam steering Phase shifters

来源：评论

学校读者我要写书评

暂无评论

An improved evolutionary approach for document clustering 17

An improved evolutionary approach for document clustering

引用

2017 International Conference on Research in Adaptive and Convergent systems, RACS 2017

作者： Akter, Ruksana Chung, Yoojin Div. of Computer and Electronic Systems Engineering Hankuk University of Foreign Studies Wangsan Mohyun Yongin17035 Korea Republic of

ISBN: (纸本)9781450350273

Traditional approaches representing chromosomes based on cluster centroids do not allow div.ding cluster centroids during crossover operations. Hence, significant div.rsity may not be achieved while the algorithm iterates from generation to generation. In our approach presented in this paper, we allow a crossover point to be even inside a cluster centroid, modifying some cluster centroids during the crossover operations. Such modifications also guide the algorithm to get rid of the local minima. Thus the proposed approach can find a better solution than the traditional approaches. © 2017 Association for Computing Machinery.

关键词： Genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

A Survey on Control Configuration Selection and New Challenges in Relation to Wireless Sensor and Actuator Networks

引用

IFAC-PapersOnLine 2017年第1期50卷 8810-8825页

作者： Castaño Arranz M. Birk W. Nikolakopoulos G. Control Engineering Group Div. of Signals and Systems Dept. of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå SE-971 87 Sweden

This survey on Control Configuration Selection (CCS) includes methods based on relative gains, gramian-based interaction measures, methods based on optimization schemes, plantwide control, and methods for the reconfiguration of control systems. The CCS problem is discussed, and a set of desirable properties of a CCS method are defined. Open questions and research tracks are discussed, with the focus on new challenges in relation to the emerging area of Wireless Sensors and Actuator Networks. © 2017

关键词：

来源：评论

学校读者我要写书评

暂无评论

Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy

arXiv

引用

arXiv 2020年

作者： Ali, Sharib Dmitrieva, Mariia Ghatwary, Noha Bano, Sophia Polat, Gorkem Temizel, Alptekin Krenzer, Adrian Hekalo, Amar Guo, Yun Bo Matuszewski, Bogdan Gridach, Mourad Voiculescu, Irina Yoganand, Vishnusai Chavan, Arnav Raj, Aryan Nguyen, Nhan T. Tran, Dat Q. Huynh, Le Duy Boutry, Nicolas Rezvy, Shahadate Chen, Haijian Choi, Yoon Ho Subramanian, Anand Balasubramanian, Velmurugan Gao, Xiaohong W. Hu, Hongyu Liao, Yusheng Stoyanov, Danail Daul, Christian Realdon, Stefano Cannizzaro, Renato Lamarque, Dominique Tran-Nguyen, Terry Bailey, Adam Braden, Barbara East, James Rittscher, Jens Institute of Biomedical Engineering and Big Data Institute Old Road Campus University of Oxford Oxford United Kingdom Translational Gastroenterology Unit Experimental Medicine Div. John Radcliffe Hospital University of Oxford Oxford United Kingdom Oxford NIHR Biomedical Research Centre Oxford United Kingdom Université de Versailles St-Quentin en Yvelines Hôpital Ambroise Paré France Instituto Onclologico Veneto IOV-IRCCS Padova Italy CRO Centro Riferimento Oncologico IRCCS Aviano Italy Computer Engineering Department Arab Academy for Science and Technology Alexandria Egypt Department of Computer Science University College London London United Kingdom CRAN UMR 7039 University of Lorraine CNRS Nancy France Graduate School of Informatics Middle East Technical University Ankara Turkey Department of Artificial Intelligence and Knowledge Systems University of Würzburg Germany School of Engineering University of Central Lancashire United Kingdom Department of Computer Science University of Oxford United Kingdom Mimyk Medical Simulations Pvt Ltd Indian Institute of Science Bengaluru India Dhanbad India Hanoi Viet Nam Le Kremlin-BicêtreF-94270 France School of Science and Technology Middlesex University London United Kingdom Department of Computer Science School of Informatics Xiamen University China Sungkyunkwan University Seoul Korea Republic of Claritrics India Pvt Ltd Chennai India School of Medical Science and Technology Indian Institute of Technology KharagpurWest Bengal India Shanghai Jiaotong University Shanghai China Ibn Zohr University Computer Science HIT Agadir Morocco

The Endoscopy computer Vision Challenge (EndoCV) is a crowd-sourcing initiative to address eminent problems in developing reliable computer aided detection and diagnosis endoscopy systems and suggest a pathway for clinical translation of technologies. Whilst endoscopy is a widely used diagnostic and treatment tool for hollow-organs, there are several core challenges often faced by endoscopists, mainly: 1) presence of multi-class artefacts that hinder their visual interpretation, and 2) difficulty in identifying subtle precancerous precursors and cancer abnormalities. Artefacts often affect the robustness of deep learning methods applied to the gastrointestinal tract organs as they can be confused with tissue of interest. EndoCV2020 challenges are designed to address research questions in these remits. In this paper, we present a summary of methods developed by the top 17 teams and provide an objective comparison of state-of-the-art methods and methods designed by the participants for two sub-challenges: i) artefact detection and segmentation (EAD2020), and ii) disease detection and segmentation (EDD2020). Multi-center, multi-organ, multi-class, and multi-modal clinical endoscopy datasets were compiled for both EAD2020 and EDD2020 sub-challenges. The out-of-sample generalization ability of detection algorithms was also evaluated. Whilst most teams focused on accuracy improvements, only a few methods hold credibility for clinical usability. The best performing teams provided solutions to tackle class imbalance, and variabilities in size, origin, modality and occurrences by exploring data augmentation, data fusion, and optimal class thresholding techniques. © 2020, CC BY-NC-SA.

关键词： Endoscopy

来源：评论

学校读者我要写书评

暂无评论

Development of uninterruptible auxiliary power supply through main transformer in dead section of AC feeding system 19

Development of uninterruptible auxiliary power supply throug...

引用

19th International Conference on Electrical Machines and systems, ICEMS 2016

作者： Yuki, Kazuaki Shimizu, Yosuke Terai, Masafumi Mochizuki, Yasufumi Yasuoka, Ikuo Hongguang, Gao Zheng, Trillion Q. Transport Solutions Dept. Railway Systems Div. Toshiba Corp. Japan Power and Industrial Systems RandD Center Toshiba Corp. Japan Fuchu Operations-Social Infrastructure Systems Toshiba Corp. Japan Rolling-Stock Electronics Designing Section Toshiba Transport Engineering Inc. Japan CRRC DALIAN Co. LTD. China Electrical Engineering Beijing Jiaotong University China

ISBN: (纸本)9784886860989

This paper concerns AC feeding system and the power supply in the dead section for auxiliary equipment. We developed the uninterruptible method using regenerative power of traction circuits through main transformer. To verify the proposed method, we executed the running tests using high power electrical locomotive. We show these outlines. © 2016 The Institute of Electrical Engineers of Japan.

关键词： Auxiliary equipment

来源：评论

学校读者我要写书评

暂无评论

Estimation of the CoM and CoP using a 3D body scanning systems: Validation with force plate: A case study

Estimation of the CoM and CoP using a 3D body scanning syste...

引用

IEEE International Workshop on Medical Measurement and Applications (MEMEA)

作者： D. Borzelli L. Gastaldi S. Pastorelli T. M. M. Vieira A. Botter J. Takagi R. Takeda S. Tadano Dept. of Mechanical and Aerospace Engineering Politecnico di Torino Italy Department of Electronics and Telecommunications Politecnico di Torino Italy School of Physical Education Federal University of Rio de Janeiro Brasil Div. of Human Mechanical Systems and Design Hokkaido University Hokkaido Japan

ISBN: (纸本)9781467391733

Human body Center of Mass (CoM) and Center of Pressure (CoP) kinematics are related to stability and balance control. However their evaluation can only be performed in motion analysis lab using different techniques. One of the more accurate is based on the segmental method, in which the body is modelled as a set of rigid bodies, each of which has different mass properties. In this study, we proposed an alternative estimation of the CoM and CoP, based on the segmental method and the 3D scanning of the human body. Knowing the CoM of each segment, the position of the overall CoM and CoP for poses different from the one used during the body acquisition could be predicted. The proposed method was validated by asking to a subject to assume different postures while standing on a force platform by comparing the resulting CoP with the one estimated with the segmental model. Although the approximations introduced in the model results were comparable.

关键词： Motion segmentation Force Three-dimensional displays Solids Foot Torso Legged locomotion

来源：评论

学校读者我要写书评

暂无评论

Development of Uninterruptible Auxiliary Power Supply through Main Transformer in Dead Section of AC feeding system

Development of Uninterruptible Auxiliary Power Supply throug...

引用

International Conference on Electrical Machines and systems

作者： Kazuaki Yuki Yosuke Shimizu Masafumi Terai Yasufumi Mochizuki Ikuo Yasuoka Gao Hongguang Trillion Q. Zheng Transport Solutions Dept. Railway Systems Div. Toshiba Corp. Japan Power and Industrial Systems R&D Center Toshiba Corp. Japan Fuchu Operations-Social Infrastructure Systems Toshiba Corp. Japan Rolling-Stock Electronics Designing Section Toshiba Transport Engineering Inc. Japan CRRC DALIAN Co. LTD. China Electrical Engineering Beijing Jiaotong University China

ISBN: (纸本)9781467388634

关键词： AC feeding System Dead Section Uninterruptible Power Supply Electrical Locomotive

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：