检索结果-内蒙古大学图书馆

arXiv 2022年

作者： Karim, Md. Rezaul Islam, Tanhim Beyan, Oya Lange, Christoph Cochez, Michael Rebholz-Schuhmann, Dietrich Decker, Stefan Department of Data Science and Artificial Intelligence Fraunhofer FIT Germany Chair of Computer Science 5 - Information Systems and Databases RWTH Aachen University Germany University of Cologne Faculty of Medicine University Hospital Cologne Institute for Biomedical Informatics Germany Department of Computer Science Vrije Univeriteit Amsterdam Netherlands Elsevier Discovery Lab Amsterdam Netherlands ZBMED - Information Center for Life Sciences Germany Faculty of Medicine University of Cologne Germany

Artificial intelligence (AI) systems utilizing deep neural networks (DNNs) and machine learning (ML) algorithms are widely used for solving important problems in bioinformatics, biomedical informatics, and precision medicine. However, complex DNNs or ML models, which are often perceived as opaque and black-box, can make it difficult to understand the reasoning behind their decisions. This lack of transparency can be a challenge for both end-users and decision-makers, as well as AI developers. Additionally, in sensitive areas like healthcare, explainability and accountability are not only desirable but also legally required for AI systems that can have a significant impact on human lives. Fairness is another growing concern, as algorithmic decisions should not show bias or discrimination towards certain groups or individuals based on sensitive attributes. Explainable artificial intelligence (XAI) aims to overcome the opaqueness of black-box models and provide transparency in how AI systems make decisions. Interpretable ML models can explain how they make predictions and the factors that influence their outcomes. However, most state-of-the-art interpretable ML methods are domain-agnostic and evolved from fields like computer vision, automated reasoning, or statistics, making direct application to bioinformatics problems challenging without customization and domain-specific adaptation. In this paper, we discuss the importance of explainability in the context of bioinformatics, provide an overview of model-specific and model-agnostic interpretable ML methods and tools, and outline their potential caveats and drawbacks. Besides, we discuss how to customize existing interpretable ML methods for bioinformatics problems. Nevertheless, we demonstrate how XAI methods can improve transparency through case studies in bioimaging, cancer genomics, and text mining (GitHub repo: https://***/rezacsedu/XAI-for-bioinformatics. Our review aims to provide valuable insights and se

关键词： Bioinformatics

来源：评论

学校读者我要写书评

暂无评论

Zero-shot visual question answering using knowledge graph

arXiv

引用

arXiv 2021年

作者： Chen, Zhuo Chen, Jiaoyan Geng, Yuxia Pan, Jeff Z. Yuan, Zonggang Chen, Huajun College of Computer Science Hangzhou Innovation Center Zhejiang University AZFT Joint Lab for Knowledge Engine Department of Computer Science University of Oxford School of Informatics The University of Edinbur NAIE CTO Office Huawei Technologies Co. Ltd

Incorporating external knowledge to Visual Question Answering (VQA) has become a vital practical need. Existing methods mostly adopt pipeline approaches with different components for knowledge matching and extraction, feature learning, etc. However, such pipeline approaches suffer when some component does not perform well, which leads to error cascading and poor overall performance. Furthermore, the majority of existing approaches ignore the answer bias issue — many answers may have never appeared during training (i.e., unseen answers) in real-word application. To bridge these gaps, in this paper, we propose a Zero-shot VQA algorithm using knowledge graph and a mask-based learning mechanism for better incorporating external knowledge, and present new answer-based Zero-shot VQA splits for the F-VQA dataset. Experiments show that our method can achieve state-of-the-art performance in Zero-shot VQA with unseen answers, meanwhile dramatically augment existing end-to-end models on the normal F-VQA task. Copyright © 2021, The Authors. All rights reserved.

关键词： Pipelines

来源：评论

学校读者我要写书评

暂无评论

FreeTumor: Large-Scale Generative Tumor Synthesis in Computed Tomography Images for Improving Tumor Recognition

arXiv

引用

arXiv 2025年

作者： Wu, Linshan Zhuang, Jiaxin Zhou, Yanning He, Sunan Ma, Jiabo Luo, Luyang Wang, Xi Ni, Xuefeng Zhong, Xiaoling Wu, Mingxiang Zhao, Yinghua Duan, Xiaohui Vardhanabhuti, Varut Rajpurkar, Pranav Chen, Hao Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Tencent AI Lab Shenzhen China Department of Biomedical Informatics Harvard University Boston United States Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Department of Radiology Shenzhen People’s Hospital Shenzhen China Department of Radiology The Third Affiliated Hospital of Southern Medical University Guangzhou China Department of Radiology Sun Yat-Sen Memorial Hospital Sun Yat-Sen University Guangzhou China Department of Diagnostic Radiology Li Ka Shing Faculty of Medicine The University of Hong Kong Hong Kong Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong Division of Life Science The Hong Kong University of Science and Technology Hong Kong State Key Laboratory of Molecular Neuroscience The Hong Kong University of Science and Technology Hong Kong Shenzhen-Hong Kong Collaborative Innovation Research Institute The Hong Kong University of Science and Technology Shenzhen China

Tumor is a leading cause of death worldwide, with an estimated 10 million deaths attributed to tumor-related diseases every year. AI-driven tumor recognition unlocks new possibilities for more precise and intelligent tumor screening and diagnosis. However, the progress is heavily hampered by the scarcity of annotated datasets, which demands extensive annotation efforts by radiologists. To tackle this challenge, we introduce FreeTumor, an innovative Generative AI (GAI) framework to enable large-scale tumor synthesis for mitigating data scarcity. Specifically, FreeTumor effectively leverages a combination of limited labeled data and large-scale unlabeled data for tumor synthesis training. Unleashing the power of large-scale data, FreeTumor is capable of synthesizing a large number of realistic tumors on images for augmenting training datasets. To this end, we create the largest training dataset for tumor synthesis and recognition by curating 161,310 publicly available Computed Tomography (CT) volumes from 33 sources, with only 2.3% containing annotated tumors. To validate the fidelity of synthetic tumors, we engaged 13 board-certified radiologists in a Visual Turing Test to discern between synthetic and real tumors. Rigorous clinician evaluation validates the high quality of our synthetic tumors, as they achieved only 51.1% sensitivity and 60.8% accuracy in distinguishing our synthetic tumors from real ones. Through high-quality tumor synthesis, FreeTumor scales up the recognition training datasets by over 40 times, showcasing a notable superiority over state-of-the-art AI methods including various synthesis methods and foundation models. On average, FreeTumor improves the segmentation Dice scores by 6.7% and early tumor detection sensitivity by 16.4%. These findings indicate promising prospects of FreeTumor in clinical applications, potentially advancing tumor treatments and improving the survival rates of patients. Copyright © 2025, The Authors. All rights reserved.

关键词： computerized tomography

来源：评论

学校读者我要写书评

暂无评论

Mitosis detection and classification for breast cancer diagnosis: What we know and what is next

引用

computers in Biology and Medicine 2025年 191卷 110057-110057页

作者： Khalil, Rafi Ullah Sajjad, Muhammad Dhahbi, Sami Bourouis, Sami Hijji, Mohammad Muhammad, Khan Digital Image Processing Lab Department of Computer Science Islamia College Peshawar Peshawar25000 Pakistan Applied College of Mahail Aseer King Khalid University Muhayil Aseer62529 Saudi Arabia Department of Information Technology College of Computers and Information Technology Taif University Taif21944 Saudi Arabia Faculty of Computers and Information Technology University of Tabuk Tabuk71491 Saudi Arabia Department of Applied AI School of Convergence College of Computing and Informatics Sungkyunkwan University Seoul03063 Korea Republic of

Breast cancer is the second most deadly malignancy in women, behind lung cancer. Despite significant improvements in medical research, breast cancer is still accurately diagnosed with histological analysis. During this procedure, pathologists examine a physical sample for the presence of mitotic cells, or dividing cells. However, the high resolution of histopathology images and the difficulty of manually detecting tiny mitotic nuclei make it particularly challenging to differentiate mitotic cells from other types of cells. Numerous studies have addressed the detection and classification of mitosis, owing to increasing capacity and developments in automated approaches. The combination of machine learning and deep learning techniques has greatly revolutionized the process of identifying mitotic cells by offering automated, precise, and efficient solutions. In the last ten years, several pioneering methods have been presented, advancing towards practical applications in clinical settings. Unlike other forms of cancer, breast cancer and gliomas are categorized according to the number of mitotic divisions. Numerous papers have been published on techniques for identifying mitosis due to easy access to datasets and open competitions. Convolutional neural networks and other deep learning architectures can precisely identify mitotic cells, significantly decreasing the amount of labor that pathologists must perform. This article examines the techniques used over the past decade to identify and classify mitotic cells in histologically stained breast cancer hematoxylin and eosin images. Furthermore, we examine the benefits of current research techniques and predict forthcoming developments in the investigation of breast cancer mitosis, specifically highlighting machine learning and deep learning. © 2025 Elsevier Ltd

关键词： Lung cancer

来源：评论

学校读者我要写书评

暂无评论

"Chatty Devices" and edge-based activity classification

引用

Discover Internet of Things 2021年第1期1卷 1-15页

作者： Lakoju, Mike Javed, Amir Rana, Omer Burnap, Pete Atiba, Samuelson T. Cherkaoui, Soumaya Cardiff School of Technologies Cardiff Metropolitan University Western Avenue CardiffCF5 2YB United Kingdom School of Computer Science & amp Informatics Cardiff University 5 The Parade CardiffCF24 3AA United Kingdom Interlab Lab. Dept. of Electical & ampComp. Engineering Université de Sherbrooke Sherbrooke QuebecJ1K 2R1 Canada

With increasing automation of manufacturing processes (focusing on technologies such as robotics and human-robot interaction), there is a realisation that the manufacturing process and the artefacts/products it produces can be better connected post-production. Built on this requirement, a "chatty" factory involves creating products which are able to send data back to the manufacturing/production environment as they are used, whilst still ensuring user privacy. The intended use of a product during design phase may different significantly from actual usage. Understanding how this data can be used to support continuous product refinement, and how the manufacturing process can be dynamically adapted based on the availability of this data provides a number of opportunities. We describe how data collected on product use can be used to: (i) classify product use;(ii) associate a label with product use using unsupervised learning—making use of edge-based analytics;(iii) transmission of this data to a cloud environment where labels can be compared across different products of the same type. Federated learning strategies are used on edge devices to ensure that any data captured from a product can be analysed locally (ensuring data privacy). © The Author(s) 2021.

关键词： Edge computing

来源：评论

学校读者我要写书评

暂无评论

Optimization of institutional incentives for cooperation in structured populations

arXiv

引用

arXiv 2023年

作者： Wang, Shengxian Chen, Xiaojie Xiao, Zhilong Szolnoki, Attila Vasconcelos, Vítor V. School of Mathematical Sciences University of Electronic Science and Technology of China Chengdu611731 China Faculty of Science and Engineering University of Groningen Groningen9747 AG Netherlands School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China Institute of Technical Physics and Materials Science Centre for Energy Research P.O. Box 49 BudapestH-1525 Hungary Computational Science Lab Informatics Institute University of Amsterdam Amsterdam1098XH Netherlands Institute for Advanced Study University of Amsterdam Amsterdam1012 GC Netherlands

The application of incentives, such as reward and punishment, is a frequently applied way for promoting cooperation among interacting individuals in structured populations. However, how to properly use the incentives is still a challenging problem for incentive-providing institutions. In particular, since the implementation of incentive is costly, to explore the optimal incentive protocol, which ensures the desired collective goal at a minimal cost, is worthy of study. In this work, we consider the positive and negative incentives respectively for a structured population of individuals whose conflicting interactions are characterized by a prisoner’s dilemma game. We establish an index function for quantifying the cumulative cost during the process of incentive implementation, and theoretically derive the optimal positive and negative incentive protocols for cooperation on regular networks. We find that both types of optimal incentive protocols are identical and time-invariant. Moreover, we compare the optimal rewarding and punishing schemes concerning implementation cost and provide a rigorous basis for the usage of incentives in the game-theoretical framework. We further perform computer simulations to support our theoretical results and explore their robustness for different types of population structures, including regular, random, small-world, and scale-free networks. Copyright © 2023, The Authors. All rights reserved.

关键词： Game theory

来源：评论

学校读者我要写书评

暂无评论

Keep CALM and improve visual feature attribution

arXiv

引用

arXiv 2021年

作者： Kim, Jae Myung Choe, Junsuk Akata, Zeynep Oh, Seong Joon University of Tübingen Department of Computer Science and Engineering Sogang University Max Planck Institute for Intelligent Systems Max Planck Institute for Informatics NAVER AI Lab

The class activation mapping, or CAM, has been the cornerstone of feature attribution methods for multiple vision tasks. Its simplicity and effectiveness have led to wide applications in the explanation of visual predictions and weakly-supervised localization tasks. However, CAM has its own shortcomings. The computation of attribution maps relies on ad-hoc calibration steps that are not part of the training computational graph, making it difficult for us to understand the real meaning of the attribution values. In this paper, we improve CAM by explicitly incorporating a latent variable encoding the location of the cue for recognition in the formulation, thereby subsuming the attribution map into the training computational graph. The resulting model, class activation latent mapping, or CALM, is trained with the expectation-maximization algorithm. Our experiments show that CALM identifies discriminative attributes for image classifiers more accurately than CAM and other visual attribution baselines. CALM also shows performance improvements over prior arts on the weakly-supervised object localization benchmarks. Our code is available at https://***/naver-ai/calm. Copyright © 2021, The Authors. All rights reserved.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

Relational Message Passing for Fully Inductive Knowledge Graph Completion

arXiv

引用

arXiv 2022年

作者： Geng, Yuxia Chen, Jiaoyan Pan, Jeff Z. Chen, Mingyang Jiang, Song Zhang, Wen Chen, Huajun College of Computer Science and Technology Zhejiang University Hangzhou China Donghai Laboratory Zhoushan China Department of Computer Science The University of Manchester Manchester United Kingdom School of Informatics The University of Edinburgh Edinburgh United Kingdom NAIE PDU Huawei Technologies Co. Ltd Xi’an China Zhejiang University Hangzhou China Donghai Laboratory AZFT Joint Lab for Knowledge Engine Zhoushan China

In knowledge graph completion (KGC), predicting triples involving emerging entities and/or relations, which are unseen when the KG embeddings are learned, has become a critical challenge. Subgraph reasoning with message passing is a promising and popular solution. Some recent methods have achieved good performance, but they (i) usually can only predict triples involving unseen entities alone, failing to address more realistic fully inductive situations with both unseen entities and unseen relations, and (ii) often conduct message passing over the entities with the relation patterns not fully utilized. In this study, we propose a new method named RMPI which uses a novel Relational Message Passing network for fully Inductive KGC. It passes messages directly between relations to make full use of the relation patterns for subgraph reasoning with new techniques on graph transformation, graph pruning, relation-aware neighborhood attention, addressing empty subgraphs, etc., and can utilize the relation semantics defined in the KG’s ontological schema. Extensive evaluation on multiple benchmarks has shown the effectiveness of RMPI’s techniques and its better performance compared with the existing methods that support fully inductive KGC. RMPI is also comparable to the state-of-the-art partially inductive KGC methods with very promising results achieved. Our codes, data and some supplementary experiment results are available at https://***/zjukg/RMPI. © 2022, CC BY.

关键词： Knowledge graph

来源：评论

学校读者我要写书评

暂无评论

Parking Occupancy Prediction and Traffic Assignment in a University Environment

Parking Occupancy Prediction and Traffic Assignment in a Uni...

引用

Japan-Egypt International Conference on Electronics, Communications and computers (JEC-ECC)

作者： Mohamed Farag Amr Hilal Samy El-Tawab Arab Academy for Science Technology & Maritime Transport Alexandria Egypt Virginia Tech Transportation Institute Virginia Tech Blacksburg VA USA Informatics Lab Virginia Tech Blacksburg VA USA Department of Computer and Systems Engineering Alexandria University Egypt College of Integrated Science & Engineering James Madison University Harrisonburg VA USA

ISBN: (纸本)9781665464642

The fourth industrial revolution has given rise to large-scale data-driven models like smart cities and Intelligent transportation. Within these models, applications like smart parking have been growing rapidly in research and industry. However, different scenarios and environments (e.g., shopping areas, residential places, and business complexes) can require special handling due to the various factors impacting people’s schedules and behavior. In this paper, we provide an initial investigation of traffic assignment based on parking prediction for a mid-size university environment where parking is concentrated in three parking garages around the campus. Our initial investigation includes results for parking prediction using a statistical method and plans for an augmenting study using variations of Neural Networks. On top of the parking prediction layer, we propose an application layer that directs and fuses the model predictions to produce the parking options provided to the application user. The presented investigation can help the university administration in their consideration of building additional garages.

关键词： Industries Schedules Statistical analysis Smart cities Fuses Computational modeling Neural networks

来源：评论

学校读者我要写书评

暂无评论

Multi-level MMPP as a Model of Fractal Traffic 1

引用

19th International Conference on Information Technologies and Mathematical Modelling, ITMM 2020

作者： Nazarov, Anatoly Moiseev, Alexander Lapatin, Ivan Paul, Svetlana Lizyura, Olga Pristupa, Pavel Peng, Xi Chen, Li Bai, Bo Institute of Applied Mathematics and Computer Science National Research Tomsk State University 36 Lenina Avenue Tomsk634050 Russia Theory Lab Central Research Center 2012Labs Huawei Tech. Investment Co. Ltd. 8/F Bio-informatics Center No. 2 Science Park West Avenue Hong Kong Science Park Pak Shek Kok Shatin N.T. Hong Kong

ISBN: (数字)9783030722470

ISBN: (纸本)9783030722463

In this paper, we propose the multi-level Markov modulated Poisson process with arbitrary distribution of the packet length as a model of fractal traffic. For the total amount of information received in multi-level MMPP, we investigate the probability distribution and present the algorithm of calculating the first and the second moments. Using asymptotic analysis method, we build Gaussian approximation of aforementioned distribution. We show that the convergence time of the probability distribution to the Gaussian distribution forms the period where the Hurst parameter is stable and reflects the self-similarity of the multi-level MMPP. © 2021, Springer Nature Switzerland AG.

关键词： Asymptotic analysis

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：