检索结果-内蒙古大学图书馆

arXiv 2022年

作者： Tortora, Matteo Cordelli, Ermanno Sicilia, Rosa Nibid, Lorenzo Ippolito, Edy Perrone, Giuseppe Ramella, Sara Soda, Paolo Unit of Computer Systems & Bioinformatics Department of Engineering University Campus Bio-Medico of Rome Via Alvaro del Portillo 21 Rome00128 Italy Anatomical Pathology Department of Medicine University Campus Bio-Medico of Rome Via Alvaro del Portillo 21 Rome00128 Italy Radiation Oncology Department of Medicine University Campus Bio-Medico of Rome Via Alvaro del Portillo 21 Rome00128 Italy

The current cancer treatment practice collects multimodal data, such as radiology images, histopathology slides, genomics and clinical data. The importance of these data sources taken individually has fostered the recent raise of radiomics and pathomics, i.e. the extraction of quantitative features from radiology and histopathology images routinely collected to predict clinical outcomes or to guide clinical decisions using artificial intelligence algorithms. Nevertheless, how to combine them into a single multimodal framework is still an open issue. In this work we therefore develop a multimodal late fusion approach that combines hand-crafted features computed from radiomics, pathomics and clinical data to predict radiation therapy treatment outcomes for non-small-cell lung cancer patients. Within this context, we investigate eight different late fusion rules (i.e. product, maximum, minimum, mean, decision template, Dempster-Shafer, majority voting, and confidence rule) and two patient-wise aggregation rules leveraging the richness of information given by computer tomography images and whole-slide scans. The experiments in leave-one-patient-out cross-validation on an in-house cohort of 33 patients show that the proposed multimodal paradigm with an AUC equal to 90.9% outperforms each unimodal approach, suggesting that data integration can advance precision medicine. As a further contribution, we also compare the handcrafted representations with features automatically computed by deep networks, and the late fusion paradigm with early fusion, another popular multimodal approach. In both cases, the experiments show that the proposed multimodal approach provides the best results. © 2022, CC BY-NC-ND.

关键词： Data integration

来源：评论

学校读者我要写书评

暂无评论

Toward Enhanced Clinical Decision Support for Patients Undergoing a Hip or Knee Replacement: Focus Group and Interview Study With Surgeons

JMIR Perioperative Medicine

引用

JMIR Perioperative Medicine 2023年第1期6卷 e36172页

作者： Grant, Sabrina Tonkin, Emma Craddock, Ian Blom, Ashley Holmes, Michael Judge, Andrew Masullo, Alessandro Nieto, Miquel Perello Song, Hao Whitehouse, Michael Flach, Peter Gooberman-Hill, Rachael Musculoskeletal Research Unit University of Bristol Southmead Hospital Bristol Medical School Bristol United Kingdom Digital Health Faculty of Engineering Bristol United Kingdom Faculty of Medicine Dentistry and Health University of Sheffield Sheffield United Kingdom Intelligent Systems Laboratory Department of Computer Science University of Bristol Bristol United Kingdom

Background: The current assessment of recovery after total hip or knee replacement is largely based on the measurement of health outcomes through self-report and clinical observations at follow-up appointments in clinical settings. Home activity-based monitoring may improve assessment of recovery by enabling the collection of more holistic information on a continuous basis. Objective: This study aimed to introduce orthopedic surgeons to time-series analyses of patient activity data generated from a platform of sensors deployed in the homes of patients who have undergone primary total hip or knee replacement and understand the potential role of these data in postoperative clinical decision-making. Methods: Orthopedic surgeons and registrars were recruited through a combination of convenience and snowball sampling. Inclusion criteria were a minimum required experience in total joint replacement surgery specific to the hip or knee or familiarity with postoperative recovery assessment. Exclusion criteria included a lack of specific experience in the field. Of the 9 approached participants, 6 (67%) orthopedic surgeons and 3 (33%) registrars took part in either 1 of 3 focus groups or 1 of 2 interviews. Data were collected using an action-based approach in which stimulus materials (mock data visualizations) provided imaginative and creative interactions with the data. The data were analyzed using a thematic analysis approach. Results: Each data visualization was presented sequentially followed by a discussion of key illustrative commentary from participants, ending with a summary of key themes emerging across the focus group and interview data set. Conclusions: The limitations of the evidence are as follows. The data presented are from 1 English hospital. However, all data reflect the views of surgeons following standard national approaches and training. Although convenience sampling was used, participants’ background, skills, and experience were considered heterogeneous.

关键词： arthroplasty clinical decision-making hip replacement home monitoring knee replacement orthopedic surgery postoperative follow-up video wearables

来源：评论

学校读者我要写书评

暂无评论

Biomedical data and AI

引用

Science China Life Sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Matching Using Sufficient Dimension Reduction for Heterogeneity Causal Effect Estimation 24

Matching Using Sufficient Dimension Reduction for Heterogene...

引用

24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Zhao, Haoran Zhang, Yinghao Cheng, Debo Li, Chen Feng, Zaiwen College of Science Huazhong Agricultural University Wuhan430070 China College of Computer Science and Engineering Guangxi Normal University Guilin541000 China University of South Australia Stem Australia Kyushu Institute of Technology Department of Computer Science and Systems Engineering Iizuka820-8502 Japan College of Informatics Huazhong Agricultural University Wuhan430070 China Huazhong Agricultural University Hubei Key Laboratory of Agricultural Bioinformatics Wuhan430070 China Huazhong Agricultural University Hubei Hongshan Laboratory Wuhan430070 China Huazhong Agricultural University National Key Laboratory of Crop Genetic Improvement Wuhan430070 China Wuhan University State Key Laboratory of Hybrid Rice 299 Bayi Rd Wuhan430070 China Macro Agricultural Research Institute Huazhong Agricultural University Wuhan430070 China

ISBN: (纸本)9798350319934

Causal inference plays an important role in understanding the underlying mechanisation of the data generation process across various domains. It is challenging to estimate the average causal effect and individual causal effects from observational data with high-dimensional covariates due to the curse of dimension and the problem of data sufficiency. The existing matching methods can not effectively estimate individual causal effect or solve the problem of dimension curse in causal inference. To address this challenge, in this work, we prove that the reduced set by sufficient dimension reduction (SDR) is a balance score for confounding adjustment. Under the theorem, we propose to use an SDR method to obtain a reduced representation set of the original covariates and then the reduced set is used for the matching method. In detail, a non-parametric model is used to learn such a reduced set and to avoid model specification errors. The experimental results on real-world datasets show that the proposed method outperforms the compared matching methods. Moreover, we conduct an experiment analysis and the results demonstrate that the reduced representation is enough to balance the imbalance between the treatment group and control group individuals. © 2022 IEEE.

关键词： Causal Inference Individual Causal Effect Matching Sufficient Dimension Reduction

来源：评论

学校读者我要写书评

暂无评论

Multimodal Explainability via Latent Shift applied to COVID-19 stratification

arXiv

引用

arXiv 2022年

作者： Guarrasi, Valerio Tronchin, Lorenzo Albano, Domenico Faiella, Eliodoro Fazzini, Deborah Santucci, Domiziana Soda, Paolo Unit of Computer Systems and Bioinformatics Department of Engineering University Campus Bio-Medico of Rome Rome Italy IRCCS Istituto Ortopedico Galeazzi Milan Italy Department of Biomedical Surgical and Dental Sciences Università degli Studi di Milano Milan Italy Department of Radiology Sant’Anna Hospital San Fermo della Battaglia Como Italy Department of Diagnostic Imaging and Stereotactic Radiosurgey Centro Diagnostico Italiano S.p.A. Milan Italy Department of Diagnostics and Intervention Radiation Physics Biomedical Engineering Umeå University Umeå Sweden

We are witnessing a widespread adoption of artificial intelligence in healthcare. However, most of the advancements in deep learning in this area consider only unimodal data, neglecting other modalities. Their multimodal interpretation necessary for supporting diagnosis, prognosis and treatment decisions. In this work we present a deep architecture, which jointly learns modality reconstructions and sample classifications using tabular and imaging data. The explanation of the decision taken is computed by applying a latent shift that, simulates a counterfactual prediction revealing the features of each modality that contribute the most to the decision and a quantitative score indicating the modality importance. We validate our approach in the context of COVID-19 pandemic using the AIforCOVID dataset, which contains multimodal data for the early identification of patients at risk of severe outcome. The results show that the proposed method provides meaningful explanations without degrading the classification performance. © 2022, CC BY.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

Optical Fiber Sensor for the Detection of Decarboxylation Products of Amino Acids 27

Optical Fiber Sensor for the Detection of Decarboxylation Pr...

引用

27th International Conference on Optical Fiber Sensors, OFS 2022

作者： Vasconcelos, Helena De Almeida, José M. M. M. Mendes, João Dias, Bernardo Jorge, Pedro A. S. Saraiva, Cristina Coelho, Luís C. C. School of Agrarian and Veterinary Sciences University of Trás-os-Montes E Alto Douro Vila Real5001-801 Portugal Faculty of Sciences INESC TEC-Institute for Systems and Computer Engineering University of Porto Technology and Science Porto4169-007 Portugal CIQUP - Chemistry Research Unit Chemistry and Biochemistry Department Faculty of Sciences University of Porto Porto4169-007 Portugal Department of Physics and Astronomy Faculty of Sciences University of Porto Porto4169-007 Portugal Department of Physics School of Science and Technology University of Trás-os-Montes E Alto Douro Vila Real5001-801 Portugal

Long period fiber gratings coated with TiO2 and poly(ethylene-co-vinyl acetate) (PEVA), a polymeric structure permeable biogenic amines found in foodstuff, were used to detect these compounds through the wavelength sh... 详细信息

ISBN: (纸本)9781957171142

关键词： Ethylene

来源：评论

学校读者我要写书评

暂无评论

A novel ensemble method for enhancing Internet of Things device security against botnet attacks

引用

Decision Analytics Journal 2023年 8卷

作者： Arshad, Amina Jabeen, Maira Ubaid, Saqib Raza, Ali Abualigah, Laith Aldiabat, Khaled Jia, Heming Institute of Computer Science Khwaja Fareed University of Engineering and Information Technology Rahim Yar Khan 64200 Pakistan Computer Science Department Prince Hussein Bin Abdullah Faculty for Information Technology Al al-Bayt University Mafraq 25113 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos 13-5053 Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan MEU Research Unit Middle East University Amman 11831 Jordan Applied science research center Applied science private university Amman 11931 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang 11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya 27500 Malaysia Department of Management Information Systems Ajloun National University Jordan School of Information Engineering Sanming University Sanming 365004 China

The growing number of connected Internet of Things (IoT) devices has led to the daily growth of network botnet attacks. The networks of compromised devices controlled by a single entity can be used for malicious purposes such as denial of service distributed IoT botnet attacks and theft of personal information. The weak security measures of many IoT devices make them easy targets for compromise and inclusion in botnets. In this research, we propose a system for detecting botnet attacks. We develop an ensemble learning system to detect botnets in network traffic with high-performance scores. The system will analyze the traffic and identify any suspicious behavior that may indicate the presence of a botnet. For this purpose, we use the benchmark CTU-13 dataset to build the applied machine learning and deep learning techniques for comparison. We propose a novel ensemble technique, K-neighbors, Decision tree, and Random forest (KDR), to achieve high performance for botnet attack detection. Study results show that the proposed KDR gives 99.7% accuracy in 12.99 s. Hyperparameter optimization and k-fold cross-validation are employed to substantiate the performance. Our research study contributes to the body of knowledge on the detection of botnet attacks and provides a practical solution for securing IoT devices against botnet attacks. © 2023 The Author(s)

关键词： Botnet attacks Cyber security Deep learning Ensemble learning IoT devices Network security

来源：评论

学校读者我要写书评

暂无评论

Retrospective for the Dynamic Sensorium Competition for predicting large-scale mouse primary visual cortex activity from videos 38

Retrospective for the Dynamic Sensorium Competition for pred...

引用

38th Conference on Neural Information Processing systems, NeurIPS 2024

作者： Turishcheva, Polina Fahey, Paul G. Vystrčilová, Michaela Hansel, Laura Froebe, Rachel Ponder, Kayla Qiu, Yongrong Willeke, Konstantin F. Bashiri, Mohammad Baikulov, Ruslan Zhu, Yu Ma, Lei Yu, Shan Huang, Tiejun Li, Bryan M. De Wulf, Wolf Kudryashova, Nina Hennig, Matthias H. Rochefort, Nathalie L. Onken, Arno Wang, Eric Ding, Zhiwei Tolias, Andreas S. Sinz, Fabian H. Ecker, Alexander S. Institute of Computer Science and Campus Institute Data Science University of Göttingen Germany Department of Neuroscience Center for Neuroscience and Artificial Intelligence Baylor College of Medicine HoustonTX United States Department of Ophthalmology Byers Eye Institute Stanford University School of Medicine StanfordCA United States Stanford Bio-X Stanford University StanfordCA United States Wu Tsai Neurosciences Institute Stanford University StanfordCA United States International Max Planck Research School for Intelligent Systems Tübingen Germany Institute for Bioinformatics and Medical Informatics Tübingen University Germany lRomul Russia Institute of Automation Chinese Academy of Sciences China Beijing Academy of Artificial Intelligence China The Alan Turing Institute United Kingdom School of Informatics University of Edinburgh United Kingdom Centre for Discovery Brain Sciences University of Edinburgh United Kingdom Simons Initiative for the Developing Brain University of Edinburgh United Kingdom Department of Electrical Engineering Stanford University StanfordCA United States Max Planck Institute for Dynamics and Self-Organization Göttingen Germany

Understanding how biological visual systems process information is challenging because of the nonlinear relationship between visual input and neuronal responses. Artificial neural networks allow computational neuroscientists to create predictive models that connect biological and machine vision. Machine learning has benefited tremendously from benchmarks that compare different models on the same task under standardized conditions. However, there was no standardized benchmark to identify state-of-the-art dynamic models of the mouse visual system. To address this gap, we established the SENSORIUM 2023 Benchmark Competition with dynamic input, featuring a new large-scale dataset from the primary visual cortex of ten mice. This dataset includes responses from 78,853 neurons to 2 hours of dynamic stimuli per neuron, together with behavioral measurements such as running speed, pupil dilation, and eye movements. The competition ranked models in two tracks based on predictive performance for neuronal responses on a held-out test set: one focusing on predicting in-domain natural stimuli and another on out-of-distribution (OOD) stimuli to assess model generalization. As part of the NeurIPS 2023 Competition Track, we received more than 160 model submissions from 22 teams. Several new architectures for predictive models were proposed, and the winning teams improved the previous state-of-the-art model by 50%. Access to the dataset as well as the benchmarking infrastructure will remain online at ***. © 2024 Neural information processing systems foundation. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Graph Machine Learning for Design of High-Octane Fuels

arXiv

引用

arXiv 2022年

作者： Rittig, Jan G. Ritzert, Martin Schweidtmann, Artur M. Winkler, Stefanie Weber, Jana M. Morsch, Philipp Heufer, K. Alexander Grohe, Martin Mitsos, Alexander Dahmen, Manuel Aachen52074 Germany Aarhus University Department of Computer Science Aarhus Denmark Delft University of Technology Department of Chemical Engineering Delft2629 HZ Netherlands RWTH Aachen University Chair of Computer Science 7 Aachen52074 Germany Delft University of Technology Delft Bioinformatics Lab Intelligent Systems Delft2628 XE Netherlands RWTH Aachen University High Pressure Gas Dynamics Aachen52074 Germany Forschungszentrum Jülich GmbH Institute for Energy and Climate Research IEK-10: Energy Systems Engineering Jülich52425 Germany JARA-ENERGY Aachen52056 Germany

Fuels with high-knock resistance enable modern spark-ignition engines to achieve high efficiency and thus low CO2 emissions. Identification of molecules with desired autoignition properties indicated by a high research octane number and a high octane sensitivity is therefore of great practical relevance and can be supported by computer-aided molecular design (CAMD). Recent developments in the field of graph machine learning (graph-ML) provide novel, promising tools for CAMD. We propose a modular graph-ML CAMD framework that integrates generative graph-ML models with graph neural networks and optimization, enabling the design of molecules with desired ignition properties in a continuous molecular space. In particular, we explore the potential of Bayesian optimization and genetic algorithms in combination with generative graph-ML models. The graph-ML CAMD framework successfully identifies well-established high-octane components. It also suggests new candidates, one of which we experimentally investigate and use to illustrate the need for further auto-ignition training data. © 2022, CC BY.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Data preparation for artificial intelligence in medical imaging: Experiences from the ProCAncer-I initiative

Data preparation for artificial intelligence in medical imag...

引用

Data Science and engineering in Healthcare, Medicine and Biology, IEEE EMBS Special Topic Conference on

作者： Varvara Kalokyri Nikolaos Tachos Stelios Sfakianakis Katerina Nikiforaki Ioannis Karatzanis Haridimos Kondylakis Simone Mazzetti Daniele Regge Nikolaos Papanikolaou Konstantinos Marias Dimitrios Fotiadis Manolis Tsiknakis Institute of Computer Science Foundation of Research and Technology Hellas FORTH-ICS Nik. Heraklion 70013 Crete Greece FORTH-BRI University Campus of Ioannina 45110 Ioannina Greece Department of Surgical Sciences University of Turin Turin Italy Radiology Unit Candiolo Cancer Institute FPO-IRCCS Strada Provinciale 10060 Candiolo(TO) Italy Computational Clinical Imaging Group Champalimaud Research Champalimaud Foundation 1400-038 Lisbon Portugal Greece and the Unit of Medical Technology and Intelligent Information Systems Materials Science and Engineering Department FORTH-BRI University Campus of Ioannina 45110 Ioannina Greece

The ProCAncer-I project, an important initiative in the field of prostate cancer research, is exploiting the power of Artificial Intelligence (AI) to advance prostate cancer diagnosis, prognosis, and treatment. The success of AI in prostate cancer imaging within this context relies on meticulous data preparation, especially when dealing with clinical data that need to be integrated with imaging data. This paper explores the importance of data preparation to implement AI tools for prostate cancer imaging, and outlines the approach followed and challenges needed to be addressed in the context of ProCAncer-I, for ensuring data quality, privacy, and reliability.

关键词：

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：