检索结果-内蒙古大学图书馆

arXiv 2022年

作者： Huang, Jiahao Fang, Yingying Nan, Yang Wu, Huanjun Wu, Yinzhe Gao, Zhifan Li, Yang Wang, Zidong Lio, Pietro Rueckert, Daniel Eldar, Yonina C. Yang, Guang The National Heart and Lung Institute Imperial College London Guy Scadding Building Dovehouse St LondonSW3 6LY United Kingdom The Department of Bioengineering Imperial College London South Kensington LondonSW7 2AZ United Kingdom The School of Biomedical Engineering Sun Yat-Sen University Guangdong Guangzhou510006 China The School of Automation Sciences and Electrical Engineering Beihang University Beijing China The Department of Computer Science Brunel University London LondonUB8 3PH United Kingdom The Computer Laboratory University of Cambridge CambridgeCB3 0FD United Kingdom The Department of Computing Imperial College London South Kensington Campus LondonSW7 2AZ United Kingdom Technical University of Munich Munich80333 Germany Faculty of Mathematics and Computer Science Weizmann Institute of Science Rehovot7610001 Israel

Research studies have shown no qualms about using data driven deep learning models for downstream tasks in medical image analysis, e.g., anatomy segmentation and lesion detection, disease diagnosis and prognosis, and treatment planning. However, deep learning models are not the sovereign remedy for medical image analysis when the upstream imaging is not being conducted properly (with artefacts). This has been manifested in MRI studies, where the scanning is typically slow, prone to motion artefacts, with a relatively low signal to noise ratio, and poor spatial and/or temporal resolution. Recent studies have witnessed substantial growth in the development of deep learning techniques for propelling fast MRI. This article aims to (1) introduce the deep learning based data driven techniques for fast MRI including convolutional neural network and generative adversarial network based methods, (2) survey the attention and transformer based models for speeding up MRI reconstruction, and (3) detail the research in coupling physics and data driven models for MRI acceleration. Finally, we will demonstrate through a few clinical applications, explain the importance of data harmonisation and explainable models for such fast MRI techniques in multicentre and multi-scanner studies, and discuss common pitfalls in current research and recommendations for future research directions. © 2022, CC BY-NC-ND.

关键词： Signal to noise ratio

来源：评论

学校读者我要写书评

暂无评论

The ACROBAT 2022 Challenge: Automatic Registration Of Breast Cancer Tissue

arXiv

引用

arXiv 2023年

作者： Weitz, Philippe Valkonen, Masi Solorzano, Leslie Carr, Circe Kartasalo, Kimmo Boissin, Constance Koivukoski, Sonja Kuusela, Aino Rasic, Dusan Feng, Yanbo Pouplier, Sandra Sinius Sharma, Abhinav Eriksson, Kajsa Ledesma Robertson, Stephanie Marzahl, Christian Gatenbee, Chandler D. Anderson, Alexander R.A. Wodzinski, Marek Jurgas, Artur Marini, Niccolò Atzori, Manfredo Müller, Henning Budelmann, Daniel Weiss, Nick Heldmann, Stefan Lotz, Johannes Wolterink, Jelmer M. De Santi, Bruno Patil, Abhijeet Sethi, Amit Kondo, Satoshi Kasai, Satoshi Hirasawa, Kousuke Farrokh, Mahtab Kumar, Neeraj Greiner, Russell Latonen, Leena Laenkholm, Anne-Vibeke Hartman, Johan Ruusuvuori, Pekka Rantalainen, Mattias Department of Medical Epidemiology and Biostatistics Karolinska Insitutet Stockholm Sweden Institute of Biomedicine University of Turku Turku Finland Institute of Biomedicine University of Eastern Finland Kuopio Finland Department of Surgical Pathology Zealand University Hospital Roskilde Denmark Department of Oncology and Pathology Karolinska Institutet Stockholm Sweden Gestalt Diagnostics Spokane United States Department of Integrated Mathematical Oncology Moffitt Cancer Center Tampa United States Informatics Institute University of Applied Sciences Western Switzerland Switzerland Department of Measurement and Electronics Agh University of Kraków Poland Department of Computer Science University of Geneva Geneva Switzerland Department of Neuroscience University of Padova Italy Medical Faculty University of Geneva Switzerland Fraunhofer Institute for Digital Medicine Mevis Lübeck Germany Department of Applied Mathematics Technical Medical Centre University of Twente Enschede Netherlands Multimodality Medical Imaging Technical Medical Centre University of Twente Enschede Netherlands Department of Electrical Engineering Indian Institute of Technology Bombay India Graduate School of Engineering Muroran Institute of Technology Hokkaido Japan Faculty of Medical Technology Niigata University of Health and Welfare Niigata Japan Forxai Business Operations Konica Minolta Inc. Osaka Japan Department of Computing Science University of Alberta EdmontonAB Canada Alberta Machine Intelligence Institute Edmonton Canada MedTechLabs BioClinicum Karolinska University Hospital Stockholm Sweden Faculty of Medicine and Health Technology Tampere University Tampere Finland

The alignment of tissue between histopathological whole-slide-images (WSI) is crucial for research and clinical applications. Advances in computing, deep learning, and availability of large WSI datasets have revolutionised WSI analysis. Therefore, the current state-of-the-art in WSI registration is unclear. To address this, we conducted the ACROBAT challenge, based on the largest WSI registration dataset to date, including 4,212 WSIs from 1,152 breast cancer patients. The challenge objective was to align WSIs of tissue that was stained with routine diagnostic immunohistochemistry to its H&E-stained counterpart. We compare the performance of eight WSI registration algorithms, including an investigation of the impact of different WSI properties and clinical covariates. We find that conceptually distinct WSI registration methods can lead to highly accurate registration performances and identify covariates that impact performances across methods. These results establish the current state-of-the-art in WSI registration and guide researchers in selecting and developing methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Tissue

来源：评论

学校读者我要写书评

暂无评论

Investigation of hybrid energy storage system based on battery and supercapacitor

引用

AIP Conference Proceedings 2021年第1期2319卷

作者： Muhammad Bin Sahari Kar Wun Lim Kalaivanan A/L Vejaya Noah Lee Jia Yan Lim Chen Hon Nee Seong Shan Yap Abdul Kariem Bin Mohd Arof Normiza Bt Mohamad Nor Kwong Keong Tham Adam Foong Faculty of Engineering Multimedia University Cyberjaya Selangor Malaysia Department of Physics Faculty of Science University of Malaya Kuala Lumpur Malaysia School of Engineering and Computing First City University College Selangor Malaysia Force Technology AS Oslo Norway

Electrochemical cell for energy storage such as battery and supercapacitor are developing rapidly in recent years. Battery has high specific energy but moderate to low cyclic rate and long recharge time. On the other hand, electrostatic double-layered capacitors (EDLC) is characterized by their extremely fast charge and discharge capability, high power density but relative low energy density. The limitation can be mitigated by having a hybrid of theses energy storage system which is synchronized in accordance to the application. In this work, the characteristics of a EDLC and lead acid battery are characterized and modelled using MATLAB®. Subsequently, a hybrid energy system consists of a combination of (EDLC) and battery is studied. From the model, an energy management scheme is implemented by using an embedded system to manage the charge-discharge rate, to ensure it operate within the safety envelope and maximised operation lifespan.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Correction to: Deep CNN-based autonomous system for safety measures in logistics transportation

引用

Soft computing 2021年第18期25卷 12371-12371页

作者： Rouari, Abdelkarim Moussaoui, Abdelouahab Chahir, Youssef Rauf, Hafiz Tayyab Kadry, Seifedine Computer Science Department Faculty of Sciences Ferhat Abbas University Setif Setif Algeria Computer Science Department University of Caen Caen France Department of Computer Science Faculty of Engineering & Informatics University of Bradford Bradford UK Faculty of applied computing and technology Noroff University College Kristiansand Norway

来源：评论

学校读者我要写书评

暂无评论

Correction: New Extension of Fuzzy-Weighted Zero-Inconsistency and Fuzzy Decision by Opinion Score Method Based on Cubic Pythagorean Fuzzy Environment: A Benchmarking Case Study of Sign Language Recognition Systems

引用

International Journal of Fuzzy Systems 2022年第7期24卷 3348-3348页

作者： Alamoodi, A. H. Albahri, O. S. Zaidan, A. A. AlSattar, H. A. Ahmed, Mohamed A. Pamucar, Dragan Zaidan, B. B. Albahri, A. S. Mahmoud, Mohammed S. Department of Computing Faculty of Arts Computing and Creative Industry Universiti Pendidikan Sultan Idris (UPSI) Perak Malaysia Computer Techniques Engineering Department Mazaya University College Nasiriyah Iraq Faculty of Engineering & IT The British University Dubai UAE Department of Business Administration College of Administrative Science The University of Mashreq Baghdad Iraq Department of Logistics the University of Defence in Belgrade Belgrade Serbia Future Technology Research Center National Yunlin University of Science and Technology Douliou Taiwan Informatics Institute for Postgraduate Studies (IIPS) Iraqi Commission for Computers and Informatics (ICCI) Baghdad Iraq Department of Computer Science Computer Science and Mathematics College Tikrit University Tikrit Iraq

来源：评论

学校读者我要写书评

暂无评论

Wireless Body Area Networks for Child Healthcare Monitoring: A Review 21

Wireless Body Area Networks for Child Healthcare Monitoring:...

引用

21st International Multi Topic Conference, INMIC 2018

作者： Aqeel-Ur-Rehman Abdullah, Irfan Ur Rehman, Sadiq Department of Computing HIET Faculty of Engineering Science and Technology Hamdard University Karachi Pakistan Department of Electrical Engineering HIET Faculty of Engineering Science and Technology Hamdard University Karachi Pakistan

ISBN: (纸本)9781538675366

The need for patients monitoring remotely has been increased in the last few decades as the lifestyle has been changed tremendously. The mobility of people has been increased very much. The development in medical sciences has also raised the issue to remotely monitor the activity of the patient to provide him/her the medical assistance and facilities as and when required. The use of WBAN technology helps to monitor the patient's activities remotely without restricting his normal routine life. In this paper, we have mainly focused the child healthcare monitoring as a lot of works need to be done in this area. We have discussed varies challenges to monitor the child healthcare, what are the different ways and technologies used to monitor the child's activities and the application of WBAN in child healthcare. © 2018 IEEE.

关键词： Body sensor networks

来源：评论

学校读者我要写书评

暂无评论

Starch and polyvinyl alcohol encapsulated biodegradable nanocomposites for environment friendly slow release of urea fertilizer

引用

Chemical engineering Journal Advances 2021年 7卷

作者： Zafar, Nida Niazi, Muhammad Bilal Khan Sher, Farooq Khalid, Ushna Jahan, Zaib Shah, Ghulam Abbas Zia, Munir Department of Chemical Engineering School of Chemical and Materials Engineering National University of Sciences and Technology Islamabad 44000 Pakistan School of Mechanical Aerospace and Automotive Engineering Faculty of Engineering Environmental and Computing Coventry University Coventry CV1 5FB United Kingdom Department of Chemistry University of Agriculture Faisalabad 38040 Pakistan International Society of Engineering Science and Technology Coventry United Kingdom Department of Agronomy PMAS-Arid Agriculture University Murree Road Rawalpindi Pakistan Research and Development Department Fauji Fertilizer Company Limited 156-The Mall Rawalpindi Pakistan

Low nitrogen (N) use efficiency from urea fertilizers due to environmental losses results in high cost of fertilizers for agricultural productions. Coating of urea with biodegradable polymers makes them effective for control and efficient N release. In this study, starch and polyvinyl alcohol (PVA) were used in combination with acrylic acid (AA), citric acid (CA) and maleic acid (MA) for the coating of urea prills. Different formulations of the coating were prepared and applied on urea prills such as urea coated with starch (10%) and PVA (5%) with acrylic acid: 2, 4 and 6% (USP-A2, USP-A4, USP-A6), with citric acid: 2, 4 and 6% (USP-C2, USP-C4, USP-C6), and with maleic acid: 2, 4 and 6% (USP-M2, USP-M4, USP-M6). After urea coating in fluidized bed coater, all uncoated and coated urea samples were characterized by scanning electron spectroscopy (SEM), Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), crushing strength and UV-Vis spectroscopy. The morphological and XRD analysis indicated that a new uniform coating with no new phase transformation occurred. Among all urea coated samples, USP-A2 and USP-C2 showed the highest crushing strengths: 12.08 and 13.67 N with nitrogen release efficiency of 70.10 and 50.74% respectively. All coated urea samples improved the spinach plants’ foliage yield, chlorophyll content, N-uptake and apparent nitrogen recovery (ANR) than uncoated urea and control plants. However, USP-A2 and USP-C2 provided promising results among all coated samples with dry foliage yield (2208 ± 92 and 2428 ± 83 kg/ha), chlorophyll (34 ± 0.6 and 34 ± 0.4 mg/g), N-uptake (88 ± 4 and 95 ± 6 kg/ha) and ANR (59 ± 4 and 67 ± 6%). Therefore, urea prills coated with a combination of biodegradable polymers can be a better choice for the farmers to enhance agronomical productions by controlling the fertilizer nutrient release rate. © 2021

关键词： Biodegradable coating formulations and Spinach foliage yield Coated urea Environmental management Nitrogen release rate Slow-release fertilizers

来源：评论

学校读者我要写书评

暂无评论

Review of e-learning service framework based on cloud computing

引用

AIP Conference Proceedings 2023年第1期2706卷

作者： Purwono Hendradi Moh Khanapi Abd Ghani Siti Nur Mahfuzah Mohamad Karima Aissaoui Uky Yudatama Teknik Informatika Universitas Muhammadiyah Magelang Magelang Indonesia Biomedical Computing and Engineering Technologies (BIOCORE) Applied Research Group Faculty of Information and Communication Technology Universiti Teknikal Malaysia Melaka Melaka Malaysia Department of Interactive Media Faculty of Information and Communication Technology Universiti Teknikal Malaysia Melaka Melaka Malaysia Artificial Intelligence Data Science and Emergent Systems Laboratory ENSAF Sidi Mohammed Ben Abdellah University FEZ Morocco

The impact of Covid-19 on education is the result of online learning from home as part of the Work from Home (WFH) protocol. There are at least three essential roles in WFH, namely Institutions, Teachers and Students, each of which uses personal resources. This literature begins with a review of the primary literature with the theme of e-learning frameworks and cloud computing. It is found that two crucial things are Integration and measuring the level of success. On that basis, in this literature review research, keywords are taken from the theme, including the word equation by using AND OR operators on the research database site over a span of the last five years. Each literature will be explored in stages focusing on the problem of Integration and or Measurement Level measurement. These results become material for systematic discussion so that an overview is obtained for the development of an E-learning Service Framework based on Cloud computing using a six-layer model with the Business Process layer being the layer used in system integration, both with other service systems and similar systems in the education ecosystem.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Explainable AI: Definition and attributes of a good explanation for health AI

arXiv

引用

arXiv 2024年

作者： Kyrimi, Evangelia McLachlan, Scott Wohlgemut, Jared M. Perkins, Zane B. Lagnado, David A. Marsh, William Gimson, Alexander Shafti, Ali Ercole, Ari Banerjee, Amitava Glocker, Ben Schafer, Burkhard Gatsonis, Constantine Grosan, Crina Sent, Danielle Berman, David S. Glass, David O'Regan, Declan P. Letsios, Dimitrios Morrissey, Dylan Pisirir, Erhan Leofante, Francesco Soyel, Hamit Williamson, Jon Grieman, Keri Dube, Kudakwashe Mardsen, Max Nagendran, Myura Tai, Nigel Kostopoulou, Olga Jones, Owain Curzon, Paul Stoner, Rebecca S. Tandle, Sankalp Joshi, Shalmali Mossadegh, Somayyeh Buijsman, Stefan Miller, Tim Madai, Vince Istvan School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom School of Nursing Midwifery and Palliative Care King's College London London United Kingdom Centre for Trauma Sciences Blizard Institute Queen Mary University of London London United Kingdom Royal London Hospital Barts Health NHS Trust London United Kingdom Department of Experiment Psychology University College London London United Kingdom Cambridge Liver Unit Addenbrooke's Hospital Cambridge University Hospitals Hills Road Box 210 Cambridge United Kingdom University of Cambridge United Kingdom Cambridge Consultants Ltd. United Kingdom Division of Anaesthesia Cambridge Centre for AI in Medicine University of Cambridge Cambridge United Kingdom Magdelene College University of Cambridge United Kingdom Institute of Health Informatics University College London London United Kingdom Department of Computing Imperial College London London United Kingdom SCRIPT Centre for IT and IP Law School of Law University of Edinburgh United Kingdom Center for Statistical Sciences School of Public Health Brown University Providence United States Research Group Artificial Intelligence HU University of Applied Sciences Utrecht Netherlands Jheronimus Academy of Data Science Tilburg University Eindhoven University of Technology s-Hertogenbosch Netherlands Centre for Theoretical Physics Queen Mary University of London London United Kingdom School of Computing Ulster University Belfast United Kingdom MRC Laboratory of Medical Sciences Imperial College London London United Kingdom Department of Informatics King's College London United Kingdom Physiotherapy Department Barts Health NHS Trust London United Kingdom Sport and Exercise Medicine Queen Mary University of London London United Kingdom School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Department of Computing Imperial College London United Kingdom Departmen

Proposals of artificial intelligence (AI) solutions based on increasingly complex and accurate predictive models are becoming ubiquitous across many disciplines. As the complexity of these models grows, transparency and users' understanding often diminish. This suggests that accurate prediction alone is insufficient for making an AI-based solution truly useful. In the development of healthcare systems, this introduces new issues related to accountability and safety. Understanding how and why an AI system makes a recommendation may require complex explanations of its inner workings and reasoning processes. Although research on explainable AI (XAI) has significantly increased in recent years and there is high demand for XAI in medicine, defining what constitutes a good explanation remains ad hoc, and providing adequate explanations continues to be challenging. To fully realize the potential of AI, it is critical to address two fundamental questions about explanations for safety-critical AI applications, such as health-AI: (1) What is an explanation in health-AI? and (2) What are the attributes of a good explanation in health-AI? In this study, we examined published literature and gathered expert opinions through a two-round Delphi study. The research outputs include (1) a definition of what constitutes an explanation in health-AI and (2) a comprehensive list of attributes that characterize a good explanation in health-AI. © 2024, CC BY.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Reliable deep learning–based detection of misplaced chest electrodes during electrocardiogram recording: Algorithm development and validation

引用

JMIR Medical Informatics 2021年第4期9卷 e25347页

作者： Rjoob, Khaled Bond, Raymond Finlay, Dewar McGilligan, Victoria Leslie, Stephen J. Rababah, Ali Iftikhar, Aleeha Guldenring, Daniel Knoery, Charles McShane, Anne Peace, Aaron Faculty of Computing Engineering & Built Environment Ulster University Jordanstown United Kingdom Faculty of Life & Health Sciences Centre for Personalised Medicine Ulster University Londonderry United Kingdom Department of Diabetes & Cardiovascular Science University of the Highlands and Islands Inverness United Kingdom HS Kempten Kempten Hochschule Kempten Kempten Germany Emergency Department Letterkenny University Hospital Donegal Ireland Western Health and Social Care Trust Londonderry United Kingdom

Background: A 12-lead electrocardiogram (ECG) is the most commonly used method to diagnose patients with cardiovascular diseases. However, there are a number of possible misinterpretations of the ECG that can be caused by several different factors, such as the misplacement of chest electrodes. Objective: The aim of this study is to build advanced algorithms to detect precordial (chest) electrode misplacement. Methods: In this study, we used traditional machine learning (ML) and deep learning (DL) to autodetect the misplacement of electrodes V1 and V2 using features from the resultant ECG. The algorithms were trained using data extracted from high-resolution body surface potential maps of patients who were diagnosed with myocardial infarction, diagnosed with left ventricular hypertrophy, or a normal ECG. Results: DL achieved the highest accuracy in this study for detecting V1 and V2 electrode misplacement, with an accuracy of 93.0% (95% CI 91.46-94.53) for misplacement in the second intercostal space. The performance of DL in the second intercostal space was benchmarked with physicians (n=11 and age 47.3 years, SD 15.5) who were experienced in reading ECGs (mean number of ECGs read in the past year 436.54, SD 397.9). Physicians were poor at recognizing chest electrode misplacement on the ECG and achieved a mean accuracy of 60% (95% CI 56.09-63.90), which was significantly poorer than that of DL (P<.001). Conclusions: DL provides the best performance for detecting chest electrode misplacement when compared with the ability of experienced physicians. DL and ML could be used to help flag ECGs that have been incorrectly recorded and flag that the data may be flawed, which could reduce the number of erroneous diagnoses. ©Khaled Rjoob, Raymond Bond, Dewar Finlay, Victoria McGilligan, Stephen J Leslie, Ali Rababah, Aleeha Iftikhar, Daniel Guldenring, Charles Knoery, Anne McShane, Aaron Peace.

关键词： Cardiovascular disease Deep learning ECG ECG interpretation Electrode misplacement engineering Feature engineering Machine learning Myocardial Myocardial infarction Physicians

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：