检索结果-内蒙古大学图书馆

Lecture Notes in Networks and Systems 2023年 750 LNNS卷 v-vi页

作者： Bringas, Pablo García García, Hilde Pérez de Pisón, Francisco Javier Martínez Álvarez, Francisco Martínez Lora, Alicia Troncoso Herrero, Álvaro Rolle, José Luis Calvo Quintián, Héctor Corchado, Emilio Faculty of Engineering University of Deusto Bilbao Spain School of Industrial Computer University of Leon León Spain Department of Mechanical Engineering University of La Rioja Logroño Spain Data Science and Big Data Lab Pablo de Olavide Uni versity Seville Spain Applied Computational Intelligence University of Burgos Burgos Spain Department of Industrial Engineering University of A Coruña A Coruña Spain Faculty of Science University of Salamanca Salamanca Spain

来源：评论

学校读者我要写书评

暂无评论

Learning with Group Noise

arXiv

引用

arXiv 2021年

作者： Wang, Qizhou Yao, Jiangchao Gong, Chen Liu, Tongliang Gong, Mingming Yang, Hongxia Han, Bo Department of Computer Science Hong Kong Baptist University Hong Kong Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of MoE School of Computer Science and Engineering Nanjing University of Science and Technology China Data Analytics and Intelligence Lab Alibaba Group China Department of Computing Hong Kong Polytechnic University Hong Kong Trustworthy Machine Learning Lab School of Computer Science University of Sydney Australia School of Mathematics and Statistics University of Melbourne Australia

Machine learning in the context of noise is a challenging but practical setting to plenty of real-world applications. Most of the previous approaches in this area focus on the pairwise relation (casual or correlational relationship) with noise, such as learning with noisy labels. However, the group noise, which is parasitic on the coarse-grained accurate relation with the fine-grained uncertainty, is also universal and has not been well investigated. The challenge under this setting is how to discover true pairwise connections concealed by the group relation with its fine-grained noise. To overcome this issue, we propose a novel Max-Matching method for learning with group noise. Specifically, it utilizes a matching mechanism to evaluate the relation confidence of each object (cf. Figure 1) w.r.t. the target, meanwhile considering the Non-IID characteristics among objects in the group. Only the most confident object is considered to learn the model, so that the fine-grained noise is mostly dropped. The performance on a range of real-world datasets in the area of several learning paradigms demonstrates the effectiveness of Max-Matching. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Preface

Lecture Notes in Networks and Systems

引用

Lecture Notes in Networks and Systems 2023年 749 LNNS卷 v-vi页

作者： Rolle, José Luis Calvo de Pisón, Francisco Javier Martínez Bringas, Pablo García García, Hilde Pérez Álvarez, Francisco Martínez Lora, Alicia Troncoso Herrero, Álvaro Quintián, Héctor Corchado, Emilio Department of Industrial Engineering University of A Coruña A Coruña Spain Department of Mechanical Engineering University of La Rioja Logroño Spain Faculty of Engineering University of Deusto Bilbao Spain School of Industrial Computer University of Leon León Spain Data Science and Big Data Lab Pablo de Olavide University Seville Spain Applied Computational Intelligence University of Burgos Burgos Spain Faculty of Science University of Salamanca Salamanca Spain

来源：评论

学校读者我要写书评

暂无评论

PIMIP: An open source platform for pathology information management and integration

arXiv

引用

arXiv 2021年

作者： Wu, Jialun Mao, Anyu Bao, Xinrui Zhang, Haichuan Gao, Zeyu Wang, Chunbao Gong, Tieliang Li, Chen School of Computer Science and Technology Xi’an Jiaotong University Xi’an China National Engineering Lab for Big Data Analytics Xi’an Jiaotong University Xi’an China School of Automation Science and Engineering Xi’an Jiaotong University Xi’an China School of Electrical Engineering and Computer Science Pennsylvania State University University ParkPA United States Department of Pathology First Affiliated Hospital of Xi’an Jiaotong University Xi’an China

Digital pathology plays a crucial role in the development of artificial intelligence in the medical field. The digital pathology platform can make the pathological resources digital and networked, and realize the permanent storage of visual data and the synchronous browsing processing without the limitation of time and space. It has been widely used in various fields of pathology. However, there is still a lack of an open and universal digital pathology platform to assist doctors in the management and analysis of digital pathological sections, as well as the management and structured description of relevant patient information. Most platforms cannot integrate image viewing, annotation and analysis, and text information management. To solve the above problems, we propose a comprehensive and extensible platform, PIMIP (Pathology Information Management & Integration Platform). Our PIMIP has developed the image annotation functions based on the visualization of digital pathological sections. Our annotation functions support multi-user collaborative annotation and multi-device annotation, and realize the automation of some annotation tasks. In the annotation task, we invited a professional pathologist for guidance. We introduce a machine learning module for image analysis. The data we collected included public data from local hospitals and clinical examples. Our platform is more clinical and suitable for clinical use. In addition to image data, we also structured the management and display of text information. So our platform is comprehensive. The platform framework is built in a modular way to support users to add machine learning modules independently, which makes our platform extensible. © 2021, CC BY.

关键词： Information management

来源：评论

学校读者我要写书评

暂无评论

Location embedding based pairwise distance learning for fine-grained diagnosis of urinary stones

arXiv

引用

arXiv 2024年

作者： Jin, Qiangguo Huang, Jiapeng Sun, Changming Cui, Hui Xuan, Ping Su, Ran Wei, Leyi Wu, Yu-Jie Wu, Chia-An Duh, Henry B.L. Lu, Yueh-Hsun School of Software Northwestern Polytechnical University Shaanxi China Yangtze River Delta Research Institute of NPU Suzhou China CSIRO Data61 Sydney Australia Department of Computer Science and Information Technology La Trobe University Melbourne Australia School of Engineering Shantou University Guangdong China College of Intelligence and Computing Tianjin University Tianjin China School of Information Xiamen University Xiamen China AIDD Faculty of Applied Science Macao Polytechnic University China Department of Radiology Shuang-Ho Hospital Taipei Medical University Taipei Taiwan Master of Public Health Program National Yang Ming Chiao Tung University Taipei Taiwan School of Design The Hong Kong Polytechnic University Hong Kong Department of Radiology School of Medicine College of Medicine Taipei Medical University Taipei Taiwan

The precise diagnosis of urinary stones is crucial for devising effective treatment strategies. The diagnostic process, however, is often complicated by the low contrast between stones and surrounding tissues, as well as the variability in stone locations across different patients. To address this issue, we propose a novel location embedding based pairwise distance learning network (LEPD-Net) that leverages low-dose abdominal X-ray imaging combined with location information for the fine-grained diagnosis of urinary stones. LEPD-Net enhances the representation of stone-related features through context-aware region enhancement, incorporates critical location knowledge via stone location embedding, and achieves recognition of fine-grained objects with our innovative fine-grained pairwise distance learning. Additionally, we have established an in-house dataset on urinary tract stones to demonstrate the effectiveness of our proposed approach. Comprehensive experiments conducted on this dataset reveal that our framework significantly surpasses existing state-of-the-art methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

A Secure Federated data-Driven Evolutionary Multi-objective Optimization Algorithm

arXiv

引用

arXiv 2022年

作者： Liu, Qiqi Yan, Yuping Ligeti, Péter Jin, Yaochu Trustworthy and General AI Lab School of Engineering Westlake University China Faculty of Technology Bielefeld University Bielefeld33619 Germany Faculty of Informatics Department of Computeralgebra Eötvös Loránd University Hungary Smart Data Group E-Group ICT Software Zrt Hungary Department of Computer Science University of Surrey Surrey GuildfordGU2 7XH United Kingdom

data-driven evolutionary algorithms usually aim to exploit the information behind a limited amount of data to perform optimization, which have proved to be successful in solving many complex real-world optimization problems. However, most data-driven evolutionary algorithms are centralized, causing privacy and security concerns. Existing federated Bayesian optimization algorithms and data-driven evolutionary algorithms mainly protect the raw data on each client. To address this issue, this paper proposes a secure federated data-driven evolutionary multiobjective optimization algorithm to protect both the raw data and the newly infilled solutions obtained by optimizing the acquisition function conducted on the server. We select the query points on a randomly selected client at each round of surrogate update by calculating the acquisition function values of the unobserved points on this client, thereby reducing the risk of leaking the information about the solution to be sampled. In addition, since the predicted objective values of each client may contain sensitive information, we mask the objective values with Diffie-Hellman-based noise, and then send only the masked objective values of other clients to the selected client via the server. Since the calculation of the acquisition function also requires both the predicted objective value and the uncertainty of the prediction, the predicted mean objective and uncertainty are normalized to reduce the influence of noise. Experimental results on a set of widely used multi-objective optimization benchmarks show that the proposed algorithm can protect privacy and enhance security with only negligible sacrifice in the performance of federated data-driven evolutionary optimization. Copyright © 2022, The Authors. All rights reserved.

关键词： Evolutionary algorithms

来源：评论

学校读者我要写书评

暂无评论

Multimodal Priors Guided Segmentation of Liver Lesions in MRI Using Mutual Information Based Graph Co-Attention Networks 23rd

Multimodal Priors Guided Segmentation of Liver Lesions in MR...

引用

23rd International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2020

作者： Mo, Shaocong Cai, Ming Lin, Lanfen Tong, Ruofeng Chen, Qingqing Wang, Fang Hu, Hongjie Iwamoto, Yutaro Han, Xian-Hua Chen, Yen-Wei College of Computer Science and Technology Zhejiang University Hangzhou China Department of Radiology Sir Run Run Shaw Hospital Hangzhou China College of Information Science and Engineering Ritsumeikan University Kusatsu Japan Research Center for Healthcare Data Science Zhejiang Lab Hangzhou China Artificial Intelligence Research Center Yamaguchi University Yamaguchi Japan

ISBN: (纸本)9783030597184

Segmentation of focal liver lesions serves as an essential preprocessing step for initial diagnosis, stage differentiation, and post-treatment efficacy evaluation. Multimodal MRI scans (e.g., T1WI, T2WI) provide complementary information on liver lesions and is widely used for diagnosis. However, some modalities (e.g., T1WI) have high resolution but lack of important visual information (e.g., edge) belonged to other modalities (T2WI), it is significant to enhance tissue lesion quality in T1WI using other modality priors (T2WI) and improve segmentation performance. In this paper, we propose a graph learning based approach with the motivation of extracting modality-specific features efficiently and establishing the regional correspondence effectively between T1WI and T2WI. We first project deep features into a graph domain and employ graph convolution to propagate information across all regions for extraction of modality-specific features. Then we propose a mutual information based graph co-attention module to learn weight coefficients of one bipartite graph, which is constructed by the fully-connection of graphs with different modalities in the graph domain. At last, we get the final refined features for segmentation by re-projection and residual connection. We validate our method on a multimodal MRI liver lesion dataset. Experimental results show that the proposed approach achieves improvement of liver lesion segmentation in T1WI by learning guided features from multimodal priors (T2WI) compared to existing methods. © 2020, Springer Nature Switzerland AG.

关键词： Medical imaging

来源：评论

学校读者我要写书评

暂无评论

Entropy-Based Characterization of Influence Pathways in Traditional and Social Media

Entropy-Based Characterization of Influence Pathways in Trad...

引用

International Conference on Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom)

作者： Ozlem Ozmen Garibay Niloofar Yousefi Kevin Aslett Jacopo Baggio Erik Hemberg Chathura Jayalath Alexander Mantzaris Bruce Miller Una-May O’Reilly William Rand Chathurani Senevirathna Ivan Garibay Industrial Engineering & Management Systems University of Central Florida Orlando FL USA School of Politics Security and International Affairs University of Central Florida Orlando FL USA Computer Science & Artificial Intelligence Lab Massachusetts Institute of Technology Cambridge MA USA Statistics & Data Science Department University of Central Florida Orlando FL USA Poole School of Management North Carolina State University Raleigh NC USA

Despite much work on social media, analysis of individual influence campaigns, messages, and platforms, we lack the tools and techniques and fundamental research to effectively understand the information flows and their effects on the dynamics of the entire information ecosystem. For example, how information is amplified or dampened as it moves from one online community to another, how information is spinned or framed into narratives that favor or malign viewpoints organically or by foreign actors, how disinformation flows from fringe to mainstream communities, etc. We postulate that the information ecosystem is an attention economy, and that influence-the ability to gather attention towards a particular message or messages- is its currency. As a result, we model the information ecosystem as a complex network of influences flowing between actors, communities and platforms. This paper advocate for the use of information-theoretic entropic methods to model and characterize this complex network of influences over time: Influence Cascades Ecosystem (ICE). We envision leveraging the concept of influence cascades in conjunction with a novel geopolitical news-centric model of the information ecosystem in order to better understand the influence pathways by which various types of information (new articles from trusted, untrusted, fringe or mainstream sources) propagate across the social and traditional hybrid media environment.

关键词： Social networking (online) Ecosystems Collaboration Complex networks Media Internet Currencies

来源：评论

学校读者我要写书评

暂无评论

Automatic Diagnosis of Myocarditis Disease in Cardiac MRI Modality using Deep Transformers and Explainable Artificial intelligence

arXiv

引用

arXiv 2022年

作者： Jafari, Mahboobeh Shoeibi, Afshin Ghassemi, Navid Heras, Jonathan Ling, Sai Ho Beheshti, Amin Zhang, Yu-Dong Wang, Shui-Hua Alizadehsani, Roohallah Gorriz, Juan M. Acharya, U. Rajendra Rokny, Hamid Alinejad Internship in BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia Data Science and Computational Intelligence Institute University of Granada Spain Department of Mathematics and Computer Science University of La Rioja La Rioja Spain Australia Data Analytics Lab Department of Computing Macquarie University SydneyNSW2109 Australia School of Computing and Mathematical Sciences University of Leicester Leicester United Kingdom Deakin University VIC3217 Australia Department of Psychiatry University of Cambridge United Kingdom School of Mathematics Physics and Computing University of Southern Queensland Springfield Australia BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia UNSW Data Science Hub The University of New South Wales SydneyNSW2052 Australia Research Centre Macquarie University Sydney2109 Australia

Myocarditis is a significant cardiovascular disease (CVD) that poses a threat to the health of many individuals by causing damage to the myocardium. The occurrence of microbes and viruses, including the likes of HIV, plays a crucial role in the development of myocarditis disease (MCD). The images produced during cardiac magnetic resonance imaging (CMRI) scans are low contrast, which can make it challenging to diagnose cardiovascular diseases. In other hand, checking numerous CMRI slices for each CVD patient can be a challenging task for medical doctors. To overcome the existing challenges, researchers have suggested the use of artificial intelligence (AI)-based computer-aided diagnosis systems (CADS). The presented paper outlines a CADS for the detection of MCD from CMR images, utilizing deep learning (DL) methods. The proposed CADS consists of several steps, including dataset, preprocessing, feature extraction, classification, and post-processing. First, the Z-Alizadeh dataset was selected for the experiments. Subsequently, the CMR images underwent various preprocessing steps, including denoising, resizing, as well as data augmentation (DA) via CutMix and MixUp techniques. In the following, the most current deep pre-trained and transformer models are used for feature extraction and classification on the CMR images. The findings of our study reveal that transformer models exhibit superior performance in detecting MCD as opposed to pre-trained architectures. In terms of DL architectures, the Turbulence Neural Transformer (TNT) model exhibited impressive accuracy, reaching 99.73% utilizing a 10-fold cross-validation approach. Additionally, to pinpoint areas of suspicion for MCD in CMRI images, the Explainable-based Grad Cam method was employed. © 2022, CC BY-NC-ND.

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges

arXiv

引用

arXiv 2021年

作者： Ding, Jian Xue, Nan Xia, Gui-Song Bai, Xiang Yang, Wen Yang, Michael Ying Belongie, Serge Luo, Jiebo Datcu, Mihai Pelillo, Marcello Zhang, Liangpei The State Key Lab LIESMARS Wuhan University Wuhan430079 China The National Engineering Research Center for Multimedia Software School of Computer Science and Institute of Artificial Intelligence Wuhan University Wuhan430072 China The National Engineering Research Center for Multimedia Software School of Computer Science and Institute of Artificial Intelligence The State Key Lab. LIESMARS Wuhan University Wuhan430072 China The School of Electronic Information Huazhong University of Science and Technology Wuhan430079 China The School of Electronic Information The State Key Lab. LIESMARS Wuhan University Wuhan430072 China University of Twente Netherlands Department of Computer Science Cornell University Cornell Tech United States Department of Computer Science University of Rochester RochesterNY14627 United States 82234 Germany Romania DAIS Ca' Foscari University of Venice Italy

In the past decade, object detection has achieved significant progress in natural images but not in aerial images, due to the massive variations in the scale and orientation of objects caused by the bird’s-eye view of aerial images. More importantly, the lack of large-scale benchmarks has become a major obstacle to the development of object detection in aerial images (ODAI). In this paper, we present a large-scale dataset of Object deTection in Aerial images (DOTA) and comprehensive baselines for ODAI. The proposed DOTA dataset contains 1,793,658 object instances of 18 categories of oriented-bounding-box annotations collected from 11,268 aerial images. Based on this large-scale and well-annotated dataset, we build baselines covering 10 state-of-the-art algorithms with over 70 configurations, where the speed and accuracy performances of each model have been evaluated. Furthermore, we provide a code library for ODAI and build a website for evaluating different algorithms. Previous challenges run on DOTA have attracted more than 1300 teams worldwide. We believe that the expanded large-scale DOTA dataset, the extensive baselines, the code library and the challenges can facilitate the designs of robust algorithms and reproducible research on the problem of object detection in aerial images. Copyright © 2021, The Authors. All rights reserved.

关键词： Object detection

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：