检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Zhou, Lei Zhang, Yuzhong Zhang, Jiadong Qian, Xuejun Gong, Chen Sun, Kun Ding, Zhongxiang Wang, Xing Li, Zhenhui Liu, Zaiyi Shen, Dinggang The School of Health Science and Engineering University of Shanghai for Science and Technology Shanghai China The School of Biomedical Engineering ShanghaiTech University China The School of Computer Science and Engineering Nanjing University of Science and Technology China Ruijin Hospital Shanghai Jiaotong University School of Medicine China Department of Radiology Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province China The Shanghai General Hospital Shanghai JiaoTong University China Department of Radiology The Third Affiliated Hospital of Kunming Medical University China Department of Radiology Guangdong Provincial Peo-ple's Hospital Guangdong Academy of Medical Sciences Guangzhou510080 China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou510080 China School of Biomedical Engineering State Key Laboratory of Advanced Medical Materials and Devices ShanghaiTech University Shanghai201210 China Shanghai United Imaging Intelligence Co. Ltd. Shanghai200232 China Shanghai Clinical Research and Trial Center Shanghai201210 China

Automated breast tumor segmentation on the basis of dynamic contrast-enhancement magnetic resonance imaging (DCE-MRI) has shown great promise in clinical practice, particularly for identifying the presence of breast disease. However, accurate segmentation of breast tumor is a challenging task, often necessitating the development of complex networks. To strike an optimal tradeoff between computational costs and segmentation performance, we propose a hybrid network via the combination of convolution neural network (CNN) and transformer layers. Specifically, the hybrid network consists of a encoder-decoder architecture by stacking convolution and deconvolution layers. Effective 3D transformer layers are then implemented after the encoder subnetworks, to capture global dependencies between the bottleneck features. To improve the efficiency of hybrid network, two parallel encoder subnetworks are designed for the decoder and the transformer layers, respectively. To further enhance the discriminative capability of hybrid network, a prototype learning guided prediction module is proposed, where the category-specified prototypical features are calculated through online clustering. All learned prototypical features are finally combined with the features from decoder for tumor mask prediction. The experimental results on private and public DCE-MRI datasets demonstrate that the proposed hybrid network achieves superior performance than the state-of-the-art (SOTA) methods, while maintaining balance between segmentation accuracy and computation cost. Moreover, we demonstrate that automatically generated tumor masks can be effectively applied to identify HER2-positive subtype from HER2-negative subtype with the similar accuracy to the analysis based on manual tumor segmentation. The source code is available at https://***/ZhouLlab/PLHN. Copyright © 2024, The Authors. All rights reserved.

关键词： Multilayer neural networks

来源：评论

学校读者我要写书评

暂无评论

Efficient Federated Learning for AIoT applications Using Knowledge Distillation

arXiv

引用

arXiv 2021年

作者： Liu, Tian Ling, Zhiwei Xia, Jun Fu, Xin Yu, Shui Chen, Mingsong MoE Engineering Research Center of Software/Hardware Co-design Technology and Application East China Normal University Shanghai200062 China Department of Information Science and Engineering Zaozhuang University Zaozhuang277160 China Department of Electrical and Computer Engineering University of Houston HoustonTX77204 United States School of Computer Science University of Technology Sydney Australia

As a promising distributed machine learning paradigm, Federated Learning (FL) trains a central model with decentralized data without compromising user privacy, which makes it widely used by Artificial Intelligence Internet of Things (AIoT) applications. However, the traditional FL suffers from model inaccuracy, since it trains local models only using hard labels of data while useful information of incorrect predictions with small probabilities is ignored. Although various solutions try to tackle the bottleneck of the traditional FL, most of them introduce significant communication overhead, making the deployment of large-scale AIoT devices a great challenge. To address the above problem, this paper presents a novel Distillation-based Federated Learning (DFL) method that enables efficient and accurate FL for AIoT applications. By using Knowledge Distillation (KD), in each round of FL training, our approach uploads both the soft targets and local model gradients to the cloud server for aggregation, where the aggregation results are then dispatched to AIoT devices for the next round of local training. During the DFL local training, in addition to hard labels, the model predictions approximate soft targets, which can improve model accuracy by leveraging the knowledge of soft targets. To further improve our DFL model performance, we design a dynamic adjustment strategy of loss function weights for tuning the ratio of KD and FL, which can maximize the synergy between soft targets and hard labels. Comprehensive experimental results on well-known benchmarks show that our approach can significantly improve the model accuracy of FL without introducing significant communication overhead. Copyright © 2021, The Authors. All rights reserved.

关键词： Distillation

来源：评论

学校读者我要写书评

暂无评论

Long Short Term Memory Networks Based Anomaly Detection for KPIs

引用

computers, Materials & Continua 2019年第8期61卷 829-847页

作者： Haiqi Zhu Fanzhi Meng Seungmin Rho Mohan Li Jianyu Wang Shaohui Liu Feng Jiang School of Computer Science and Technology Harbin Institute of TechnologyHarbin150000China Institute of Computer Application China Academy of Engineer PhysicsMianyang621900China Department of Software Sejong UniversitySeoulKorea Cyberspace Institute of Advanced Technology Guangzhou UniversityGuangzhou510006China

In real-world many internet-based service companies need to closely monitor large amounts of data in order to ensure stable operation of their ***,anomaly detection for these data with various patterns and data quality has been a great challenge,especially without *** this paper,we adopt an anomaly detection algorithm based on Long Short-Term Memory(LSTM)Network in terms of reconstructing KPIs and predicting *** use the reconstruction error and prediction error respectively as the criteria for judging anomalies,and we test our method with real data from a company in the insurance industry and achieved good performance.

关键词： LSTM anomaly detection KPIs

来源：评论

学校读者我要写书评

暂无评论

Exploring ERP User Behaviour in Developing Countries: Integrating Self-efficacy and UTAUT Perspectives

Exploring ERP User Behaviour in Developing Countries: Integr...

引用

Advancement in Data science, E-learning and Information Systems (ICADEIS), International Conference on

作者： Dedy Syamsuar Muhammad Angga Oktaharisetia Deden Witarsyah Ahmad Luthfi Peter Dell Yudistira Sira Permana Darius Antoni Information Systems Department School of Information Systems Bina Nusantara University Indonesia Program Pasca Sarjana Universitas Bina Darma Palembang Indonesia School of Industrial Engineering Telkom University Bandung Indonesia Department of Informatics Faculty of Industrial Technology Islamic University of Indonesia School of Management and Marketing Faculty of Business and Law Curtin University Australia Teknik Informatika Politeknik Sekayu Sekayu Indonesia Faculty of Computer Science Universitas Indo Global Mandiri Palembang Indonesia

ERP stands for enterprise resource planning. It is an information system that is all rolled into one, is very flexible and adaptable, and optimizes business operations while also centralizing all of the company's data. This study aims to empirically investigate the elements that affect users' intention and usage of enterprise resource planning (ERP). This research incorporates self-efficacy into the unified theory of acceptance and use of technology (UTAUT2). A quantitative technique was taken, and as a result, 143 replies that might be used were effectively collected. After that, the information was put through a structural equation modelling partial least square (SEM-PLS) analysis. According to the findings of this research, self-efficacy appears to have a significant influence on all endogenous variables (EE, BI, and UB), as indicated by t-values of 3.951, 7.573, and 5.492, respectively. In addition, the analysis also indicated that PE (4.317), EE (2.397), and HM (3.084) are likewise statistically and substantively significant to BI. The result shows that the R2 for BI and UB indicate a moderate impact of 0.603 and 0.521, respectively. It is suggested that the model has valuable applications in academic and real-world contexts.

关键词：

来源：评论

学校读者我要写书评

暂无评论

FedDBL: Communication and Data Efficient Federated Deep-Broad Learning for Histopathological Tissue Classification

arXiv

引用

arXiv 2023年

作者： Deng, Tianpeng Huang, Yanqi Han, Guoqiang Shi, Zhenwei Lin, Jiatai Dou, Qi Liu, Zaiyi Guo, Xiao-Jing Chen, C.L. Philip Han, Chu The School of Computer Science and Engineering South China University of Technology Guangdong Guangzhou510006 China Southern Medical University Guangzhou510080 China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangzhou510080 China The Department of Computer Science and Engineering The Chinese University of Hong Kong 999077 Hong Kong The Department of Breast Pathology and Lab Tianjin Medical University Cancer Institute and Hospital National Clinical Research Center of Cancer Key Laboratory of Cancer Prevention and Therapy Tianjin Tianjin’s Clinical Research Center for Cancer Key Laboratory of Breast Cancer Prevention and Therapy Tianjin Medical University Ministry of Education Tianjin300060 China

Histopathological tissue classification is a fundamental task in computational pathology. Deep learning-based models have achieved superior performance but centralized training with data centralization suffers from the privacy leakage problem. Federated learning (FL) can safeguard privacy by keeping training samples locally, but existing FL-based frameworks require a large number of well-annotated training samples and numerous rounds of communication which hinder their practicability in the real-world clinical scenario. In this paper, we propose a universal and lightweight federated learning framework, named Federated Deep-Broad Learning (FedDBL), to achieve superior classification performance with limited training samples and only one-round communication. By simply associating a pre-trained deep learning feature extractor, a fast and lightweight broad learning inference system and a classical federated aggregation approach, FedDBL can dramatically reduce data dependency and improve communication efficiency. Five-fold cross-validation demonstrates that FedDBL greatly outperforms the competitors with only one-round communication and limited training samples, while it even achieves comparable performance with the ones under multiple-round communications. Furthermore, due to the lightweight design and one-round communication, FedDBL reduces the communication burden from 4.6GB to only 276.5KB per client using the ResNet-50 backbone at 50-round training. Since no data or deep model sharing across different clients, the privacy issue is well-solved and the model security is guaranteed with no model inversion attack risk. Code is available at https://***/tianpeng-deng/FedDBL. © 2023, CC BY.

关键词： Tissue

来源：评论

学校读者我要写书评

暂无评论

Grand Challenge on Respiratory Sound Classification for SPRSound Dataset

Grand Challenge on Respiratory Sound Classification for SPRS...

引用

2023 IEEE Biomedical Circuits and Systems Conference, BioCAS 2023

作者： Zhang, Qing Zhang, Jing Yuan, Jiajun Huang, Huajie Zhang, Yuhang Chen, Changyan Lin, Jilei Zhang, Baoqin Lv, Gaomei Lin, Shuzhu Wang, Na Liu, Xin Tang, Mingyu Wang, Yahua Liu, Lu Ma, Hui Xie, Dan Wu, Lihua Yang, Haibo Yuan, Shuhua Chen, Mengjun Zhang, Bingxue Zhou, Hongyuan Zhao, Jian Li, Yongfu Yin, Yong Zhao, Liebin Wang, Guoxing Lian, Yong Shanghai Jiao Tong University Department of Micro-Nano Electronics MoE Key Lab of Artificial Intelligence China Shanghai Jiao Tong University School of Medicine Pediatric Ai Clinical Application and Research Center Shanghai Children's Medical Center China Shanghai Jiao Tong University School of Medicine Shanghai Children's Medical Center Department of Respiratory Medicine China Shanghai Children's Medical Center School of Medicine Shanghai China Shanghai University School of Computer Engineering and Science China Sanya Maternity and Child Care Hospital China Taicang Affiliated Hospital of Soochow University The First People's Hospital of Taicang Department of Paediatrics China Linyi City People Hospital China Fengcheng Hospital Department of Paediatrics China Linyi City Maternal and Child Health Hospital China Fifth Affiliated Hospital of Harbin Medical University Department of Pediatrics China Fujian Children's Hospital China The First Affiliated Hospital of Henan University of Traditional Chinese Medicine China Shanghai Tuoxiao Intelligent Technology Co. Ltd China

ISBN: (纸本)9798350300260

Globally, respiratory diseases are the leading cause of death, making it essential to develop an automatic respiratory sounds software to speed up diagnosis and reduce physician workload. A recent line of attempts have been proposed to predict accurately, but they have yet been able to provide a satisfactory generalization performance. In this contest, we invited the community to develop more accurate and generalized respiratory sound algorithms. A starter code is provided to standardize the submissions and lower the barrier. New testing set is prepared to evaluate the generalization performance of the submissions. Top 3 teams will present their work at IEEE BioCAS 2023 conference. © 2023 IEEE.

关键词： and machine-learning models data augmentation deep-learning models feature extractions grand challenge Open-source respiratory sound classification

来源：评论

学校读者我要写书评

暂无评论

Terahertz Landau Polaritons in Nano-slots: Ultrastrong Coupling under Extreme Spatial Confinement

Terahertz Landau Polaritons in Nano-slots: Ultrastrong Coupl...

引用

International Conference on Infrared and Millimeter Waves

作者： Dasom Kim Sunghwan Kim Dukhyung Lee Shuang Liang Fuyang Tay Michael J. Manfra Dai-Sik Kim Junichiro Kono Department of Electrical and Computer Engineering Rice University Houston TX USA Applied Physics Graduate Program Smalley-Curl Institute Rice University Houston TX USA Department of Physics Long-wavelength Nanotechnology Laboratory and Quantum Photonics Institute Ulsan National Institute of Science and Technology (UNIST) Ulsan South Korea Department of Physics and Astronomy Purdue University West Lafayette IN USA Birck Nanotechnology Center Purdue University West Lafayette IN USA Elmore Family School of Electrical and Computer Engineering Purdue University West Lafayette IN USA School of Materials Engineering Purdue University West Lafayette IN USA Department of Physics and Astronomy Rice University Houston TX USA Department of Material Science and NanoEngineering Rice University Houston TX USA

We report ultrastrong coupling of the cyclotron resonance of a two-dimensional electron gas in a GaAs quantum well to terahertz cavity photons under extreme confinement to the nanometer scale. This was realized by an array of nano-slots fabricated close to the quantum well layer on the GaAs substrate. Our terahertz magnetospectroscopic measurements revealed two polariton branches as a function of applied magnetic field. While the photon-like part of the two polariton branches was clearly observed, the matter-like part was generally elusive. Our electromagnetic simulations quantitatively reproduce our observation for the upper-polariton branch. However, for the lower-polariton branch, our simulations predict a pronounced resonance, which was absent in experimental spectra. This discrepancy may be a consequence of the breakdown of the dipole approximation in this extreme confinement situation.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Weakly Supervised Joint Whole-Slide Segmentation and Classification in Prostate Cancer

arXiv

引用

arXiv 2023年

作者： Pati, Pushpak Jaume, Guillaume Ayadi, Zeineb Thandiackal, Kevin Bozorgtabar, Behzad Gabrani, Maria Goksel, Orcun IBM Research Europe Switzerland Department of Pathology Brigham and Women’s Hospital Harvard Medical School United States Department of Pathology Massachusetts General Hospital Harvard Medical School United States Cancer Program Broad Institute of Harvard MIT United States Data Science Program Dana-Farber Harvard Cancer Center United States Computer-Assisted Applications in Medicine ETH Zurich Switzerland Signal Processing Laboratory 5 EPFL Switzerland EPFL Switzerland Department of Information Technology Uppsala University Sweden

The segmentation and automatic identification of histological regions of diagnostic interest offer a valuable aid to pathologists. However, segmentation methods are hampered by the difficulty of obtaining pixel-level annotations, which are tedious and expensive to obtain for Whole-Slide images (WSI). To remedy this, weakly supervised methods have been developed to exploit the annotations directly available at the image level. However, to our knowledge, none of these techniques is adapted to deal with WSIs. In this paper, we propose WHOLESIGHT, a weakly-supervised method, to simultaneously segment and classify WSIs of arbitrary shapes and sizes. Formally, WHOLESIGHT first constructs a tissue-graph representation of the WSI, where the nodes and edges depict tissue regions and their interactions, respectively. During training, a graph classification head classifies the WSI and produces node-level pseudo labels via post-hoc feature attribution. These pseudo labels are then used to train a node classification head for WSI segmentation. During testing, both heads simultaneously render class prediction and segmentation for an input WSI. We evaluated WHOLESIGHT on three public prostate cancer WSI datasets. Our method achieved state-of-the-art weakly-supervised segmentation performance on all datasets while resulting in better or comparable classification with respect to state-of-the-art weakly-supervised WSI classification methods. Additionally, we quantify the generalization capability of our method in terms of segmentation and classification performance, uncertainty estimation, and model calibration. © 2023, CC BY-NC-ND.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

Mechanically reprogrammable Pancharatnam-Berry metasurface for microwaves

引用

Advanced Photonics 2022年第1期4卷 69-79页

作者： Quan Xu Xiaoqiang Su Xueqian Zhang Lijuan Dong Lifeng Liu Yunlong Shi Qiu Wang Ming Kang Andrea Alù Shuang Zhang Jiaguang Han Weili Zhang Tianjin University Center for Terahertz Waves and College of Precision Instrument and Optoelectronics EngineeringKey Laboratory of Optoelectronic Information Technology(Ministry of Education of China)TianjinChina Shanxi Datong University Institute of Solid State Physics and College of Physics and Electronic ScienceShanxi Province Key Laboratory of Microstructure Electromagnetic Functional MaterialsDatongChina Wuhan University of Technology School of Information EngineeringWuhanChina Tianjin Normal University College of Physics and Materials ScienceTianjinChina City University of New York Advanced Science Research CenterPhotonics InitiativeNew YorkUnited States City University of New York Graduate CenterPhysics ProgramNew YorkUnited States University of Hong Kong Faculty of ScienceDepartment of PhysicsHong KongChina University of Hong Kong Department of Electrical and Electronic EngineeringHong KongChina Guilin University of Electronic Technology Guangxi Key Laboratory of Optoelectronic Information ProcessingSchool of Optoelectronic EngineeringGuilinChina Oklahoma State University School of Electrical and Computer EngineeringStillwaterOklahomaUnited States

Metasurfaces have enabled the realization of several optical functionalities over an ultrathin platform,fostering the exciting field of flat *** metasurfaces are achieved by arranging a layout of static meta-atoms to imprint a desired operation on the impinging wavefront,but their functionality cannot be *** and programmability of metasurfaces are the next important step to broaden their impact,adding customized on-demand functionality in which each meta-atom can be individually *** demonstrate a mechanical metasurface platform with controllable rotation at the meta-atom level,which can implement continuous Pancharatnam–Berry phase control of circularly polarized *** the proof-of-concept experiments,we demonstrate metalensing,focused vortex beam generation,and holographic imaging in the same metasurface template,exhibiting versatility and superior *** dynamic control of electromagnetic waves using a single,low-cost metasurface paves an avenue towards practical applications,driving the field of reprogrammable intelligent metasurfaces for a variety of applications.

关键词： reprogrammable metasurfaces Pancharatnam-Berry phase mechanical metasurfaces microwaves

来源：评论

学校读者我要写书评

暂无评论

Multimodal Whole Slide Foundation Model for Pathology

arXiv

引用

arXiv 2024年

作者： Ding, Tong Wagner, Sophia J. Song, Andrew H. Chen, Richard J. Lu, Ming Y. Zhang, Andrew Vaidya, Anurag J. Jaume, Guillaume Shaban, Muhammad Kim, Ahrong Williamson, Drew F.K. Chen, Bowen Almagro-Perez, Cristina Doucet, Paul Sahai, Sharifa Chen, Chengkuan Komura, Daisuke Kawabe, Akihiro Ishikawa, Shumpei Gerber, Georg Peng, Tingying Le, Long Phi Mahmood, Faisal Department of Pathology Mass General Brigham Harvard Medical School BostonMA United States Data Science Program Dana-Farber Cancer Institute BostonMA United States Cancer Program Broad Institute of Harvard and MIT CambridgeMA United States John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA United States Helmholtz Munich German Research Center for Environment and Health Munich Germany School of Computation Information and Technology Technical University of Munich Munich Germany Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Harvard-MIT Division of Health Sciences and Technology Massachusetts Institute of Technology CambridgeMA United States Department of Pathology Pusan National University Busan Korea Republic of Department of Pathology and Laboratory Medicine Emory University School of Medicine AtlantaGA United States Harvard Data Science Initiative Harvard University CambridgeMA United States Department of Systems Biology Harvard Medical School BostonMA United States Department of Preventive Medicine Graduate School of Medicine The University of Tokyo Tokyo Japan Division of Pathology National Cancer Center Exploratory Oncology Research & Clinical Trial Center Chiba Japan

The field of computational pathology has been transformed with recent advances in foundation models that encode histopathology region-of-interests (ROIs) into versatile and transferable feature representations via self-supervised learning (SSL). However, translating these advancements to address complex clinical challenges at the patient and slide level remains constrained by limited clinical data in disease-specific cohorts, especially for rare clinical conditions. We propose TITAN, a multimodal whole slide foundation model pretrained using 335,645 WSIs via visual self-supervised learning and vision-language alignment with corresponding pathology reports and 423,122 synthetic captions generated from a multimodal generative AI copilot for pathology. Without any finetuning or requiring clinical labels, TITAN can extract general-purpose slide representations and generate pathology reports that generalize to resource-limited clinical scenarios such as rare disease retrieval and cancer prognosis. We evaluate TITAN on diverse clinical tasks and find that TITAN outperforms both ROI and slide foundation models across machine learning settings such as linear probing, few-shot and zero-shot classification, rare cancer retrieval and cross-modal retrieval, and pathology report generation. The model is publicly accessible at https://***/mahmoodlab/TITAN © 2024, CC BY-NC-ND.

关键词： Self-supervised learning

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：