检索结果-内蒙古大学图书馆

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

Data preparation for artificial intelligence in medical imag...

2023 IEEE EMBS Special Topic Conference on Data science and Engineering in Healthcare, Medicine and Biology, IEEECONF 2023

作者： Kalokyri, Varvara Tachos, Nikolaos Sfakianakis, Stelios Nikiforaki, Katerina Karatzanis, Ioannis Kondylakis, Haridimos Mazzetti, Simone Regge, Daniele Papanikolaou, Nikolaos Marias, Konstantinos Fotiadis, Dimitrios Tsiknakis, Manolis FORTH-ICS Foundation of Research and Technology Hellas Institute of Computer Science Nik. Plastira 100 Crete Heraklion70013 Greece University Campus of Ioannina FORTH-BRI Ioannina45110 Greece University of Turin Department of Surgical Sciences Turin Italy Candiolo10060 Italy Champalimaud Foundation Champalimaud Research Computational Clinical Imaging Group Lisbon1400-038 Portugal FORTH-BRI Greece The Unit of Medical Technology and Intelligent Information Systems Materials Science and Engineering Department University Campus of Ioannina Ioannina45110 Greece

ISBN: (纸本)9798350383386

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. © 2023 IEEE.

关键词： Medical imaging

来源：评论

学校读者我要写书评

暂无评论

Constrained Generative Sampling of 6-DoF Grasps

Constrained Generative Sampling of 6-DoF Grasps

引用

IEEE/RSJ International Conference on intelligent Robots and systems (IROS)

作者： Jens Lundell Francesco Verdoja Tran Nguyen Le Arsalan Mousavian Dieter Fox Ville Kyrki KTH Royal Institute of Technology Sweden Department of Electrical Engineering and Automation Intelligent Robotics Group School of Electrical Engineering Aalto University Finland NVIDIA Corporation USA Paul G. Allen School of Computer Science & Engineering University of Washington Seattle USA

Most state-of-the-art data-driven grasp sampling methods propose stable and collision-free grasps uniformly on the target object. For bin-picking, executing any of those reachable grasps is sufficient. However, for completing specific tasks, such as squeezing out liquid from a bottle, we want the grasp to be on a specific part of the object's body while avoiding other locations, such as the cap. This work presents a generative grasp sampling network, VCGS, capable of constrained 6-Degrees of Freedom (DoF) grasp sampling. In addition, we also curate a new dataset designed to train and evaluate methods for constrained grasping. The new dataset, called CONG, consists of over 14 million training samples of synthetically rendered point clouds and grasps at random target areas on 2889 objects. VCGS is benchmarked against GraspNet, a state-of-the-art unconstrained grasp sampler, in simulation and on a real robot. The results demonstrate that VCGS achieves a 10-15% higher grasp success rate than the baseline while being 2–3 times as sample efficient. Supplementary material is available on our project website.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Memristor-Based Circuit of Sequential Associative Memory with Memory Interactions and Stimulus Similarity Effect

引用

IEEE Transactions on Cognitive and Developmental systems 2025年

作者： Xiong, Dongdong Wang, Xiaoping Jiang, Yiming Yang, Chao Jiang, Man Lai, Jingang Zeng, Zhigang Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan430074 China Hubei Key Laboratory of Braininspired Intelligent Systems Wuhan430074 China Huazhong University of Science and Technology Institute of Artificial Intelligence School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan430074 China Guangxi University of Science and Technology School of Computer Science and Technology Liuzhou545006 China Huazhong University of Science and Technology Department of Physiology School of Basic Medicine and Tongji Medical College Wuhan430030 China

Most proposed memristor-based circuits of associative memory consider various mechanisms in only one associative memory. Few works on circuit design of sequential associative memory have been reported. In this paper, a memristor-based circuit of sequential associative memory with memory interactions and stimulus similarity effect is designed. Two associative memories, the prior associative memory (PAM) and the later associative memory (LAM), are formed successively. The PAM modifies the rate of the formation of the LAM, and the LAM, in turn, affects the strength of the PAM, which are two interactions in sequential associative memory, called transfer and retroaction. In addition, the magnitudes of transfer and retroaction are determined by the similarity of the conditioned stimuli. The above functions are realized by the ring input processing module, memory module, transfer module and retroaction module. It has been identified in circuit analysis that the proposed circuit is power efficient and has good robustness. Furthermore, the proposed circuit has promising applications for fire rescue robots. © 2016 IEEE.

关键词： Associative storage

来源：评论

学校读者我要写书评

暂无评论

Fully implantable and battery-free wireless optoelectronic system for modulable cancer therapy and real-time monitoring

引用

npj Flexible Electronics 2023年第1期7卷 119-130页

作者： Kiho Kim In Sik Min Tae Hee Kim Do Hyeon Kim Seungwon Hwang Kyowon Kang Kyubeen Kim Sangun Park Jongmin Lee Young Uk Cho Jung Woo Lee Woon-Hong Yeo Young Min Song Youngmee Jung Ki Jun Yu Functional Bio-integrated Electronics and Energy Management Lab School of Electrical and Electronic EngineeringYonsei University50 Yonsei-roSeodaemun-guSeoul 03722Republic of Korea Center for Biomaterials Biomedical Research InstituteKorea Institute of Science and Technology(KIST)Seoul 02792Republic of Korea Department of Fusion Research and Collaboration Biomedical Research InstituteSeoul National University HospitalSeoul 03080Republic of Korea School of Electrical Engineering and Computer Science(EECS) Gwangju Institute of Science and Technology(GIST)Gwangju 61005Republic of Korea PA1 Foundry BusinessSamsung Electronics1 Samsungjeonja-roHwaseong-siGyeonggi-doRepublic of Korea R&D Center DNA Lab PharmaresearchSeongnam 13486Republic of Korea KU-KIST Graduate School of Converging Science and Technology Korea UniversitySeoul 136-705Republic of Korea School of Materials Science and Engineering Energy Materials for Soft Electronics LaboratoryPusan National UniversityBusan 46241Republic of Korea IEN Center for Human-Centric Interfaces and Engineering and George W.Woodruff School of Mechanical Engineering Georgia Institute of TechnologyAtlantaGA 30332USA Wallace H.Coulter Department of Biomedical Engineering Georgia Institute of Technology and Emory University School of MedicineAtlantaGA 30332USA Institute for Robotics and Intelligent Machines Parker H.Petit Institute for Bioengineering and BiosciencesInstitute for MaterialsNeural Engineering CenterGeorgia Institute of TechnologyAtlantaGA 30332USA School of Electrical and Electronic Engineering YU-Korea Institute of Science and Technology(KIST)InstituteYonsei University50Yonsei-roSeodaemun-guSeoul 03722Republic of Korea

Photodynamic therapy(PDT)is attracting attention as a next-generation cancer treatment that can selectively destroy malignant tissues,exhibit fewer side effects,and lack pain during *** PDT systems have recently been developed to resolve the issues of bulky and expensive conventional PDT systems and to implement continuous and repetitive *** implantable PDT systems,however,are not able to perform multiple functions simultaneously,such as modulating light intensity,measuring,and transmitting tumor-related data,resulting in the complexity of cancer ***,we introduce a flexible and fully implantable wireless optoelectronic system capable of continuous and effective cancer treatment by fusing PDT and hyperthermia and enabling tumor size monitoring in *** system exploits micro inorganic light-emitting diodes(μ-LED)that emit light with a wavelength of 624 nm,designed not to affect surrounding normal tissues by utilizing a fully programmable light intensity ofμ-LED and precisely monitoring the tumor size by Si phototransistor during a long-term implantation(2–3 weeks).The superiority of simultaneous cancer treatment and tumor size monitoring capabilities of our system operated by wireless power and data transmissions with a cell phone was confirmed through in vitro experiments,ray-tracing simulation results,and a tumor xenograft mouse model in *** all-in-one single system for cancer treatment offers opportunities to not only enable effective treatment of tumors located deep in the tissue but also enable precise and continuous monitoring of tumor size in real-time.

关键词： optoelectronic enable battery

来源：评论

学校读者我要写书评

暂无评论

Towards Spectrum-Efficient 6G: Revolutionizing Beamforming with Vision and Deep Learning

引用

IEEE Network 2025年

作者： Mirza, Ahmad Faisal Hassan, Tayyib Ul Ullah, Syed Asad Basheer, Shakila Chauhdary, Sajjad Hussain Dev, Kapal Hassan, Syed Ali Islamabad44000 Pakistan Quetta87300 Pakistan Princess Nourah bint Abdulrahman University College of Computer and Information Science Department of Information Systems P.O. BOX 84428 Riyadh11671 Saudi Arabia Munster Technological University Department of Computer Science Bishopstown CorkT12 P928 Ireland University of Johannesburg Department of institute of intelligent systems Auckland Park 2006 South Africa Chitkara University Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Punjab Rajpura140401 India

As the world transitions to 6G networks, wireless communication is set for transformative advancements, enabling applications such as autonomous transportation, immersive augmented reality, and smart cities. These networks demand ultra-reliable, low-latency, and high-bandwidth communication, coupled with unprecedented spectrum efficiency and adaptability. This magazine explores how vision-assisted beamforming (VB) leverages visual data from base station (BS) cameras combined with advanced machine learning (ML) techniques to enhance signal quality, connectivity, and adaptability in dynamic environments. By enabling intelligent, real-time beam alignment, this approach ensures stronger, more reliable connections while mitigating inefficiencies in exhaustive beam training. Adapting seamlessly to rapidly changing scenarios, VB provides scalable and efficient communication systems, paving the way for smarter networks. Using real-world datasets, deep learning (DL) models demonstrate how visual context significantly enhances beamforming accuracy and efficiency. A case study on vehicleto- everything (V2X) communication showcases the paradigm’s ability to address critical 6G challenges by achieving precise beamforming without the need for channel state information (CSI). Unlike traditional methods reliant on sequential frames, this approach achieves accurate predictions using a single frame, reducing complexity and latency while delivering robust, high quality connectivity in real time. © 1986-2012 IEEE.

关键词： Beamforming

来源：评论

学校读者我要写书评

暂无评论

Preliminaries to artificial consciousness: a multidimensional heuristic approach.

arXiv

引用

arXiv 2024年

作者： Evers, K. Farisco, M. Chatila, R. Earp, B.D. Freire, I.T. Hamker, F. Nemeth, E. Verschure, P.F.M.J. Khamassi, M. Centre for Research Ethics and Bioethics Uppsala University Uppsala Sweden Ariano Irpino Italy Institute of Intelligent Systems and Robotics Sorbonne University CNRS Paris France Uehiro Centre for Practical Ethics University of Oxford Oxford United Kingdom Centre for Biomedical Ethics National University of Singapore Singapore Artificial Intelligence Computer Science Chemnitz University of Technology Germany Alicante Institute of Neuroscience Department of Health Psychology Universidad Miguel Hernandez Spain

The pursuit of artificial consciousness requires conceptual clarity to navigate its theoretical and empirical challenges. This paper introduces a composite, multilevel, and multidimensional model of consciousness as a heuristic framework to guide research in this field. Consciousness is treated as a complex phenomenon, with distinct constituents and dimensions that can be operationalized for study and for evaluating their replication. We argue that this model provides a balanced approach to artificial consciousness research by avoiding binary thinking (e.g., conscious vs. non-conscious) and offering a structured basis for testable hypotheses. To illustrate its utility, we focus on "awareness" as a case study, demonstrating how specific dimensions of consciousness can be pragmatically analyzed and targeted for potential artificial instantiation. By breaking down the conceptual intricacies of consciousness and aligning them with practical research goals, this paper lays the groundwork for a robust strategy to advance the scientific and technical understanding of artificial consciousness. © 2024, CC BY.

关键词：

来源：评论

学校读者我要写书评

暂无评论

The Fragile Nature of Road Transportation Networks

arXiv

引用

arXiv 2024年

作者： Sun, Linghang Zhang, Yifan Axenie, Cristian Grossi, Margherita Kouvelas, Anastasios Makridis, Michail A. Institute for Transport Planning and Systems ETH Zurich Zurich8093 Switzerland Computer Science Department Center for Artificial Intelligence Technische Hochschule Nürnberg Nürnberg90489 Germany Intelligent Cloud Technologies Lab Huawei Munich Research Center Munich80992 Germany

Major cities worldwide experience problems with the performance of their road transportation networks, and the continuous increase in traffic demand presents a substantial challenge to the optimal operation of urban road networks and the efficiency of traffic control strategies. The operation of transportation systems is widely considered to display fragile property, i.e., the loss in performance increases exponentially with the linearly growing magnitude of disruptions. Meanwhile, the risk engineering community is embracing the novel concept of antifragility, enabling systems to learn from past events and exhibit improved performance under disruptions of previously unseen magnitudes. In this study, based on established traffic flow theory knowledge, namely macroscopic fundamental diagrams, we first conducted a rigorous mathematical analysis to theoretically prove the fragile nature of road transportation networks. Subsequently, we propose a skewness-based indicator that can be readily applied to cross-compare the degree of fragility for different networks solely dependent on the MFD-related parameters. Finally, by taking real-world stochasticity into account, we implemented a numerical simulation with realistic network data to bridge the gap between the theoretical proof and the real-world operations, to reflect the potential impact of uncertainty on the fragility of the networks. This work aims to demonstrate the fragile nature of road transportation networks and help researchers better comprehend the necessity to consider explicitly antifragile design for future networks and traffic control strategies. © 2024, CC BY-NC-ND.

关键词： Stochastic systems

来源：评论

学校读者我要写书评

暂无评论

A hierarchical fusion strategy of deep learning networks for detection and segmentation of hepatocellular carcinoma from computed tomography images

引用

Cancer Imaging 2024年第1期24卷 1页

作者： Lee, I-Cheng Tsai, Yung-Ping Lin, Yen-Cheng Chen, Ting-Chun Yen, Chia-Heng Chiu, Nai-Chi Hwang, Hsuen-En Liu, Chien-An Huang, Jia-Guan Lee, Rheun-Chuan Chao, Yee Ho, Shinn-Ying Huang, Yi-Hsiang Division of Gastroenterology and Hepatology Department of Medicine Taipei Veterans General Hospital Taipei Taiwan School of Medicine National Yang Ming Chiao Tung University Taipei Taiwan Institute of Bioinformatics and Systems Biology National Yang Ming Chiao Tung University Hsinchu Taiwan Institute of Computer Science and Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Radiology Taipei Veterans General Hospital Taipei Taiwan National Taiwan University School of Medicine Taipei Taiwan Cancer Center Taipei Veterans General Hospital Taipei Taiwan Department of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu Taiwan Center for Intelligent Drug Systems and Smart Bio-devices (IDS 2 B) National Yang Ming Chiao Tung University Hsinchu Taiwan College of Health Sciences Kaohsiung Medical University Kaohsiung Taiwan Institute of Clinical Medicine National Yang Ming Chiao Tung University Taipei Taiwan Healthcare and Service Center Taipei Veterans General Hospital Taipei Taiwan

Background: Automatic segmentation of hepatocellular carcinoma (HCC) on computed tomography (CT) scans is in urgent need to assist diagnosis and radiomics analysis. The aim of this study is to develop a deep learning based network to detect HCC from dynamic CT images. Methods: Dynamic CT images of 595 patients with HCC were used. Tumors in dynamic CT images were labeled by radiologists. Patients were randomly divided into training, validation and test sets in a ratio of 5:2:3, respectively. We developed a hierarchical fusion strategy of deep learning networks (HFS-Net). Global dice, sensitivity, precision and F1-score were used to measure performance of the HFS-Net model. Results: The 2D DenseU-Net using dynamic CT images was more effective for segmenting small tumors, whereas the 2D U-Net using portal venous phase images was more effective for segmenting large tumors. The HFS-Net model performed better, compared with the single-strategy deep learning models in segmenting small and large tumors. In the test set, the HFS-Net model achieved good performance in identifying HCC on dynamic CT images with global dice of 82.8%. The overall sensitivity, precision and F1-score were 84.3%, 75.5% and 79.6% per slice, respectively, and 92.2%, 93.2% and 92.7% per patient, respectively. The sensitivity in tumors < 2 cm, 2–3, 3–5 cm and > 5 cm were 72.7%, 92.9%, 94.2% and 100% per patient, respectively. Conclusions: The HFS-Net model achieved good performance in the detection and segmentation of HCC from dynamic CT images, which may support radiologic diagnosis and facilitate automatic radiomics analysis. © The Author(s) 2024.

关键词： Computed tomography Deep learning Detection Hepatocellular carcinoma Segmentation

来源：评论

学校读者我要写书评

暂无评论

Fluorine Ions Assist Titanium-Based Perylene Diimide to Achieve Over 500h Photocatalytic Stability

SSRN

引用

SSRN 2025年

作者： Li, Junqiang Fang, Jiongchong Liang, Huiqiang Wei, Zhenghao Fang, Jun Qiu, Longbin Lu, Xinxin Yang, Fuyuan Zeng, Guosong Shenzhen Key Laboratory of Intelligent Robotics and Flexible Manufacturing Systems Southern University of Science and Technology Shenzhen518055 China Department of Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen518055 China PetroChina Shenzhen New Energy Research Institute Co. Ltd. Shenzhen518055 China School of Vehicle and Mobility Tsinghua University Beijing100084 China SUSTech Energy Institute for Carbon Neutrality Southern University of Science and Technology Shenzhen518055 China

Photocatalytic water splitting using semiconductor catalysts for green hydrogen production is a critical step toward global carbon neutrality. However, the rapid decline in catalytic activity due to unstable molecular structure and photo-corrosion limits the practical application of many organic semiconductors. In this study, two titanium-coordinated perylene diimide catalysts (TiF-PDI and TiC-PDI) were synthesized via a wet chemical approach, demonstrating impressive hydrogen evolution reaction (HER) activities of 584 and 510 μmol·g−1·h−1 under near-atmospheric conditions,respectively, representing a 7-8-fold enhancement compared to H2PDI. Particularly, TiF-PDI retained ~80% of its initial catalytic activity after 500 hours of continuous illumination under room temperature and near atmospheric pressure, demonstrating the highest HER long-term stability of perylene-based semiconductors to date. The introduction of Ti4+ ions facilitated charge separation and transfer, while F- ions played a vital role in further enhancing system polarization, stabilizing photogenerated excitons, and mitigating Ti4+ ions leaching by intercalating into the molecular lattice. This cooperative regulation of dual ions improved both photocatalytic performance and durability. This work provides valuable insights into structural modification and charge stabilization in designing high-performance and long-lived photocatalysts for sustainable energy applications. © 2025, The Authors. All rights reserved.

关键词： Atmospheric corrosion

来源：评论

学校读者我要写书评

暂无评论

Enhanced electroencephalography effective connectivity in frontal low-gamma band correlates of emotional regulation after mindfulness training

引用

Journal of Neuroscience Research 2023年第6期101卷 901-915页

作者： Ng, Hei-Yin Hydra Wu, Changwei W. Huang, Feng-Ying Huang, Chih-Mao Hsu, Chia-Fen Chao, Yi-Ping Jung, Tzyy-Ping Chuang, Chun-Hsiang Research Center for Education and Mind Sciences College of Education National Tsing Hua University Hsinchu Taiwan Institute of Information Systems and Applications College of Electrical Engineering and Computer Science National Tsing Hua University Hsinchu Taiwan Department of Educational Psychology and Counseling College of Education National Tsing Hua University Hsinchu Taiwan Graduate Institute of Mind Brain and Consciousness Taipei Medical University Taipei Taiwan Brain and Consciousness Research Center Taipei Medical University-Shuang Ho Hospital New Taipei Taiwan Department of Education National Taipei University of Education Taipei Taiwan Department of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu Taiwan Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B) National Yang Ming Chiao Tung University Hsinchu Taiwan Graduate Institute of Behavioral Sciences Chang Gung University Taoyuan Taiwan Department of Child Psychiatry Chang Gung Memorial Hospital at Linkou Taoyuan Taiwan Department of Computer Science and Information Engineering Chang Gung University Taoyuan Taiwan Department of Otolaryngology-Head and Neck Surgery Chang Gung Memorial Hospital at Linkou Taoyuan Taiwan Institute for Neural Computation and Institute of Engineering in Medicine University of California San Diego La Jolla CA United States

Practicing mindfulness, focusing attention on the internal and external experiences occurring in the present moment with open and nonjudgement stance, can lead to the development of emotional regulation skills. Yet, the effective connectivity of brain regions during mindfulness has been largely unexplored. Studies have shown that mindfulness practice promotes functional connectivity in practitioners, potentially due to improved emotional regulation abilities and increased connectivity in the lateral prefrontal areas. To examine the changes in effective connectivity due to mindfulness training, we analyzed electroencephalogram (EEG) signals taken before and after mindfulness training, focusing on training-related effective connectivity changes in the frontal area. The mindfulness training group participated in an 8-week mindfulness-based stress reduction (MBSR) program. The control group did not take part. Regardless of the specific mindfulness practice used, low-gamma band effective connectivity increased globally after the mindfulness training. High-beta band effective connectivity increased globally only during Breathing. Moreover, relatively higher outgoing effective connectivity strength was seen during Resting and Breathing and Body-scan. By analyzing the changes in outgoing and incoming connectivity edges, both F7 and F8 exhibited strong parietal connectivity during Resting and Breathing. Multiple regression analysis revealed that the changes in effective connectivity of the right lateral prefrontal area predicted mindfulness and emotional regulation abilities. These results partially support the theory that the lateral prefrontal areas have top-down modulatory control, as these areas have high outflow effective connectivity, implying that mindfulness training cultivates better emotional regulation. © 2023 Wiley Periodicals LLC.

关键词： direct directed transfer function effective connectivity emotional regulation MBSR mindfulness

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：