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Deep learning-based multi-modal data integration enhancing breast cancer disease-free survival prediction

Precision Clinical Medicine 2024年第2期7卷 132-145页

作者： Zehua Wang Ruichong Lin Yanchun Li Jin Zeng Yongjian Chen Wenhao Ouyang Han Li Xueyan Jia Zijia Lai Yunfang Yu Herui Yao Weifeng Su Guangdong Key Laboratory of Cross-Application of Data Science and Technology Beijing Normal University-Hong Kong Baptist University United International CollegeZhuhai 519087China Faculty of Innovation Engineering Macao University of Science and TechnologyTaipaMacao 999078China Department of Computer and Information Engineering Guangzhou Huali CollegeGuangzhou 511325China Department of Pathology Sun Yat-sen Memorial HospitalSun Yat-sen UniversityGuangzhou 510120China Guangzhou National Laboratory Guangzhou 510005China Dermatology and Venereology Division Department of Medicine SolnaCenter for Molecular MedicineKarolinska InstitutetStockholm 17177Sweden Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation Department of Medical OncologyBreast Tumor CentrePhase I Clinical Trial CentreSun Yat-sen Memorial HospitalSun Yat-sen UniversityGuangzhou 510120China The Second Clinical Medical College Southern Medical UniversityGuangzhou 510515China Faculty of Medicine Macao University of Science and TechnologyTaipaMacao 999078China

Background:The prognosis of breast cancer is often unfavorable,emphasizing the need for early metastasis risk detection and accurate treatment *** study aimed to develop a novel multi-modal deep learning model using preoperative data to predict disease-free survival(DFS).Methods:We retrospectively collected pathology imaging,molecular and clinical data from The Cancer Genome Atlas and one independent institution in *** developed a novel Deep Learning Clinical Medicine Based Pathological Gene Multi-modal(DeepClinMed-PGM)model for DFS prediction,integrating clinicopathological data with molecular *** patients included the training cohort(n=741),internal validation cohort(n=184),and external testing cohort(n=95).Result:Integrating multi-modal data into the DeepClinMed-PGM model significantly improved area under the receiver operating characteristic curve(AUC)*** the training cohort,AUC values for 1-,3-,and 5-year DFS predictions increased to 0.979,0.957,and 0.871,while in the external testing cohort,the values reached 0.851,0.878,and 0.938 for 1-,2-,and 3-year DFS predictions,*** DeepClinMed-PGM's robust discriminative capabilities were consistently evident across various cohorts,including the training cohort[hazard ratio(HR)0.027,95%confidence interval(CI)0.0016-0.046,P<0.0001],the internal validation cohort(HR 0.117,95%CI 0.041-0.334,P<0.0001),and the external cohort(HR 0.061,95%CI 0.017-0.218,P<0.0001).Additionally,the DeepClinMed-PGM model demonstrated C-index values of 0.925,0.823,and 0.864 within the three cohorts,***:This study introduces an approach to breast cancer prognosis,integrating imaging and molecular and clinical data for enhanced predictive accuracy,offering promise for personalized treatment strategies.

关键词： breast cancer multi-modality deep learning pathological disease-free survival

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Label Informed Contrastive Pretraining for Node Importance Estimation on Knowledge Graphs

arXiv

引用

arXiv 2024年

作者： Zhang, Tianyu Hou, Chengbin Jiang, Rui Zhang, Xuegong Zhou, Chenghu Tang, Ke Lv, Hairong The Ministry of Education Key Laboratory of Bioinformatics Bioinformatics Division Beijing National Research Center for Information Science and Technology Department of Automation Tsinghua University Beijing100084 China The School of Computer Science and Engineering Fuyao Institute of Technology Fuzhou350109 China The State Key Laboratory of Resources and Environmental Information Systems Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China The Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China The Fuzhou Institute of Data Technology Fuzhou350200 China

Node Importance Estimation (NIE) is a task of inferring importance scores of the nodes in a graph. Due to the availability of richer data and knowledge, recent research interests of NIE have been dedicating to knowledge graphs for predicting future or missing node importance scores. Existing state-of-the-art NIE methods train the model by available labels, and they consider every interested node equally before training. However, the nodes with higher importance often require or receive more attention in real-world scenarios, e.g., people may care more about the movies or webpages with higher importance. To this end, we introduce Label Informed ContrAstive Pretraining (LICAP) to the NIE problem for being better aware of the nodes with high importance scores. Specifically, LICAP is a novel type of contrastive learning framework that aims to fully utilize the continuous labels to generate contrastive samples for pretraining embeddings. Considering the NIE problem, LICAP adopts a novel sampling strategy called top nodes preferred hierarchical sampling to first group all interested nodes into a top bin and a non-top bin based on node importance scores, and then divide the nodes within top bin into several finer bins also based on the scores. The contrastive samples are generated from those bins, and are then used to pretrain node embeddings of knowledge graphs via a newly proposed Predicate-aware Graph Attention Networks (PreGAT), so as to better separate the top nodes from non-top nodes, and distinguish the top nodes within top bin by keeping the relative order among finer bins. Extensive experiments demonstrate that the LICAP pretrained embeddings can further boost the performance of existing NIE methods and achieve the new state-of-the-art performance regarding both regression and ranking metrics. The source code for reproducibility is available at https://***/zhangtia16/LICAP Copyright © 2024, The Authors. All rights reserved.

关键词： Knowledge graph

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Evaluating Trade-Offs in IVA of Multimodal Neuroimaging using Cross-Platform Multidataset Independent Subspace Analysis

Evaluating Trade-Offs in IVA of Multimodal Neuroimaging usin...

引用

IEEE International Symposium on Biomedical Imaging

作者： Xinhui Li Daniel Khosravinezhad Vince D. Calhoun Rogers F. Silva Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology and Emory University Atlanta GA USA Department of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA

Multidataset independent subspace analysis (MISA) unifies multiple linear blind source separation methods to analyze joint and unique information across multiple datasets. MISA can jointly analyze large multimodal neuroimaging datasets to advance our understanding of the brain from multiple perspectives. However, a systematic evaluation of the trade-offs between problem scale and sample size is still absent in the literature. aiming to support flexibility and replicability of deep latent variable modeling, and equip practitioners with crucial tools and usage guidelines, we developed a MISA PyTorch module incorporating the linked multi-network architecture and loss function of the original MISA MATLAB. We then investigated critical performance trade-offs between latent space and sample sizes in independent vector analysis (IVA) problems. Both platforms were highly similar in hundreds of simulation settings, demonstrating successful replication of the original framework and flexibility to evaluate multiple configurations. We observed that a larger sample size, fewer datasets and fewer sources can lead to better IVA model performance. We then performed an IVA experiment on a large multimodal neuroimaging dataset and observed high cross-modal correlation linkage among the identified sources in both platforms, supporting MISA’s effectiveness for replicable multimodal linkage detection.

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A Dynamics Theory of Implicit Regularization in Deep Low-Rank Matrix Factorization

arXiv

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arXiv 2022年

作者： Cao, Jian Qian, Chen Huang, Yihui Chen, Dicheng Gao, Yuncheng Dong, Jiyang Guo, Di Qu, Xiaobo Department of Electronic Science Biomedical Intelligent Cloud R&D Center Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance Xiamen University Xiamen361005 China School of Computer and Information Engineering Fujian Engineering Research Center for Medical Data Mining and Application Xiamen University of Technology Xiamen361024 China

Implicit regularization is an important way to interpret neural networks. Recent theory starts to explain implicit regularization with the model of deep matrix factorization (DMF) and analyze the trajectory of discrete gradient dynamics in the optimization process. These discrete gradient dynamics are relatively small but not infinitesimal, thus fitting well with the practical implementation of neural networks. Currently, discrete gradient dynamics analysis has been successfully applied to shallow networks but encounters the difficulty of complex computation for deep networks. In this work, we introduce another discrete gradient dynamics approach to explain implicit regularization, i.e. landscape analysis. It mainly focuses on gradient regions, such as saddle points and local minima. We theoretically establish the connection between saddle point escaping (SPE) stages and the matrix rank in DMF. We prove that, for a rank-R matrix reconstruction, DMF will converge to a second-order critical point after R stages of SPE. This conclusion is further experimentally verified on a low-rank matrix reconstruction problem. This work provides a new theory to analyze implicit regularization in deep learning. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep learning

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research on Sustainable development of green energy and manufacturing in smart agriculture based on big data analysis

Research on Sustainable development of green energy and manu...

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IEEE International Conference on engineering Education: Innovative Practices and Future Trends (aiCERA)

作者： Maryam Ghassan Majeed Adnan Allwiftaiet Laith Fouad Naseer Ali Hussien Saad Qasim Abbas Technical Computer Engineering Department Al-Kunooze University College Basrah Iraq Department of Medical Devices Engineering Technologies National University of Science and Technology Dhi Qar Nasiriyah Iraq Medical instruments engineering techniques Al-farahidi University Baghdad Iraq Information and Communication Technology Research Group Scientific Research Center Al-Ayen University Thi-Qar Iraq Computer Technologies Engineering AI-Turath University College Baghdad Iraq

In response to global environmental changes, smart agriculture (SA) has emerged as a means of boosting agricultural output and the income of low-income communities. This article assesses the efficacy of SA strategies for advancing sustainable agriculture to ensure the availability of safe, nutritious food globally. It looks at how different aspects of SA work together to further the goals of sustainable agriculture. A deeper understanding of the intricate agricultural ecosystems is required to control the expanding issues in agricultural output. It's possible with today's digital technology, which can constantly monitor the physical surroundings and generate massive volumes of data at breakneck speeds. Farmers and companies might enhance output via the study of data. While big data analysis has been widely used in other sectors, it is still uncommon in agriculture. The study aims to examine current agricultural studies and research, which applies the newest big data analysis practices to deal with many important green energy challenges in smart agriculture by analyzing big data (GE-SABDA). data analytics and the internet could boost safety and product quality while reducing production downtime. The experimental results show the proposed method achieves a rising green energy level ratio of 94.3 %, overall accuracy of smart agriculture ratio of 90.7%, crop disease identification ratio of 98.1 %, daily productivity ratio of 450, and production wastage ratio of 18.5% compared to other methods.

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Privacy-Preserving Decentralized Signature-Based Access Control

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Han, Jinguang Chen, Liqun Susilo, Willy Southeast University School of Cyber Science and Engineering Jiangsu Nanjing210096 China Ministry of Education Engineering Research Center of Blockchain Application China University of Surrey Department of Computer Science GuildfordGU2 7XH United Kingdom University of Wollongong School of Computing & Information Technology New South Walse WollongongNSW2522 Australia

Decentralized access control reduces trust on the central authority. Considering privacy issues, privacy-preserving decentralized access control (PPDAC) schemes were proposed. However, existing schemes did not consider the unforgeability and aggregtability of access permissions and the traceability of illegal users. In this paper, we propose a privacy-preserving decentralized signature-based access control (PPDSBAC) scheme to fix the above problems. In our scheme, each access permission is a multi- signatures on a keyword, time period and pseudonym, hence is unforgeable and aggregatable. Services are encrypted by using different keywords and time periods. A user can use a pseudonym to access a service directly if he/she gets required access permissions from required authorization authorities. Furthermore, in the case that trace is required, a trace authority can de-anonymise users and identify their real identities. We formalize both the definition and security model of our scheme, and propose a concrete construction. Then, the implementation and evaluation are conducted to show its efficiency. Finally, the security of our scheme is formally proven. The novelty is to construct a PPDAC scheme with unlinkable access requests, unforgeable and aggregatable access permissions, non-interactive authentication and traceability. © 2004-2012 IEEE.

关键词： Authentication

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Bloch Equation Enables Physics-informed Neural Network in Parametric Magnetic Resonance Imaging

arXiv

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arXiv 2023年

作者： Cai, Qingrui Zhu, Liuhong Zhou, Jianjun Qian, Chen Guo, Di Qu, Xiaobo National Integrated Circuit Industry Education Integration Innovation Platform School of Electronic Science and Engineering National Model Microelectronics College Xiamen University Xiamen361102 China Fudan University Xiamen361015 China Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance Xiamen University Xiamen361102 China School of Computer and Information Engineering Fujian Engineering Research Center for Medical Data Mining and Application Xiamen University of Technology Xiamen361024 China

Magnetic resonance imaging (MRI) is an important non-invasive imaging method in clinical diagnosis. Beyond the common image structures, parametric imaging can provide the intrinsic tissue property thus could be used in quantitative evaluation. The emerging deep learning approach provides fast and accurate parameter estimation but still encounters the lack of network interpretation and enough training data. Even with a large amount of training data, the mismatch between the training and target data may introduce errors. Here, we propose one way that solely relies on the target scanned data and does not need a pre-defined training database. We provide a proof-of-concept that embeds the physical rule of MRI, the Bloch equation, into the loss of physics-informed neural network (PINN). PINN enables learning the Bloch equation, estimating the T2 parameter, and generating a series of physically synthetic data. Experimental results are conducted on phantom and cardiac imaging to demonstrate its potential in quantitative MRI. Copyright © 2023, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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applications of Transformer Attention Mechanisms in Information Security: Current Trends and Prospects

Applications of Transformer Attention Mechanisms in Informat...

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2022 International Conference on Artificial Intelligence of Things and Crowdsensing (aioTCs)

作者： M. Vubangsi Sarumi Usman Abidemi Olukayode Akanni Auwalu Saleh Mubarak Fadi Al-Turjman Artificial Intelligence Engineering Dept. AI and Robotics Institute Near East University Mersin Turkey Department of Computer Science HTTTC Bambili Computational materials Science lab University of Bamenda NWR Cameroon Research Center for AI and IoT Faculty of Engineering University of Kyrenia Mersin Turkey

In this work, we present a comprehensive survey on applications of the most recent transformer architecture based on attention in information security. Our review reveals three primary areas of application: Intrusion detection, Anomaly Detection and Malware Detection. We have presented an overview of attention-based mechanisms and their application in each cybersecurity use case, and discussed open grounds for future trends in Artificial Intelligence enabled information security.

关键词： Neural networks Intrusion detection Transformers Market research Real-time systems Malware Internet of Things

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Cloud-magnetic resonance imaging system: In the era of 6G and artificial intelligence

引用

Magnetic Resonance Letters 2025年 57-68页

作者： Yirong Zhou Yanhuang Wu Yuhan Su Jing Li Jianyu Cai Yongfu You Jianjun Zhou Di Guo Xiaobo Qu Department of Electronic Science Intelligent Medical Imaging R&D Center Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Xiamen University Department of Electronic Science Key Laboratory of Digital Fujian on IoT Communication Xiamen University Shanghai Electric Group Co. Ltd China Telecom Group China Mobile Group Department of Radiology Zhongshan Hospital (Xiamen) Fudan University Xiamen Municipal Clinical Research Center for Medical Imaging Fujian Province Key Clinical Specialty Construction Project (Medical Imaging Department) Xiamen Key Laboratory of Clinical Transformation of Imaging Big Data and Artificial Intelligence School of Computer and Information Engineering Xiamen University of Technology

Magnetic resonance imaging(MRI) plays an important role in medical diagnosis,generating petabytes of image data annually in large *** voluminous data stream requires a significant amount of network bandwidth and extensive storage ***,local data processing demands substantial manpower and hardware *** isolation across different healthcare institutions hinders crossinstitutional collaboration in clinics and *** this work,we anticipate an innovative MRI system and its four generations that integrate emerging distributed cloud computing,6G bandwidth,edge computing,federated learning,and blockchain *** system is called Cloud-MRI,aiming at solving the problems of MRI data storage security,transmission speed,artificial intelligence(ai) algorithm maintenance,hardware upgrading,and collaborative *** workflow commences with the transformation of k-space raw data into the standardized Imaging Society for Magnetic Resonance in Medicine Raw data(ISMRMRD) ***,the data are uploaded to the cloud or edge nodes for fast image reconstruction,neural network training,and automatic ***,the outcomes are seamlessly transmitted to clinics or research institutes for diagnosis and other *** Cloud-MRI system will save the raw imaging data,reduce the risk of data loss,facilitate inter-institutional medical collaboration,and finally improve diagnostic accuracy and work efficiency.

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Fisheye Multiple Object Tracking by Learning Distortions Without Dewarping

Fisheye Multiple Object Tracking by Learning Distortions Wit...

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IEEE International Conference on Image Processing

作者： Ping-Yang Chen Jun-Wei Hsieh Ming-Ching Chang Munkhjargal Gochoo Fang-Pang Lin Yong-Sheng Chen Department of Computer Science National Yang Ming Chiao Tung University Taiwan College of AI and Green Energy National Yang Ming Chiao Tung University Taiwan Department of Computer Science and Engineering National Taiwan Ocean University Taiwan Department of Computer Science University at Albany State University of New York USA College of Information Technology United Arab Emirates University UAE Emirates Center for Mobility Research United Arab Emirates University UAE National Center for High-Performance Computing Taiwan

We develop a new Multiple Object Tracking (MOT) scheme for fisheye cameras that can directly perform vehicle detection, re-identification, and tracking under fisheye distortions without explicit dewarping. Fisheye cameras provide omnidirectional coverage that is wider than traditional cameras, reducing fewer need of cameras to monitor road intersections. However, the problem of distorted views introduces new challenges for fisheye MOT. In this paper, we propose a Fish-Eye Multiple Object Tracking (FEMOT) approach with two novelties. We develop the Distorted Fisheye Image Augmentation (DFIA) method to improve object detection and re-identification on fisheye cameras, where fisheye model training can be performed on existing datasets of traditional cameras via fisheye data synthesis and augmentation. We also develop the Hybrid data Association (HDA) method to perform tracking directly on fisheye views, without the need of de-warping. The developed FEMOT framework provides practical design and advancement that enables large-scale use of fisheye cameras in smart city and surveillance applications.

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