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arXiv 2025年

作者： Lai, Haoran Jiang, Zihang Yao, Qingsong Wang, Rongsheng He, Zhiyang Tao, Xiaodong Wei, Wei Lv, Weifu Zhou, S. Kevin School of Biomedical Engineering Division of Life Sciences and Medicine University of Science and Technology of China Anhui Hefei230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Jiangsu Suzhou215123 China Stanford University Palo AltoCA94305 United States Medical Business Department iFlytek Co.Ltd. Anhui Hefei230088 China The First Affiliated Hospital of USTC Division of Life Sciences and Medicine University of Science and Technology of China Anhui Hefei230001 China Department of Radiology The First Affiliated Hospital of USTC Division of Life Sciences and Medicine University of Science and Technology of China Anhui Hefei230001 China Suzhou Institute for Advance Research USTC Jiangsu Suzhou215123 China State Key Laboratory of Precision and Intelligent Chemistry University of Science and Technology of China Anhui Hefei230026 China Institute of Computing Technology CAS Beijing100190 China

3D medical images such as Computed tomography (CT) are widely used in clinical practice, offering a great potential for automatic diagnosis. Supervised learning-based approaches have achieved significant progress but rely heavily on extensive manual annotations, limited by the availability of training data and the diversity of abnormality types. Vision-language alignment (VLA) offers a promising alternative by enabling zero-shot learning without additional annotations. However, we empirically discover that the visual and textural embeddings after alignment endeavors from existing VLA methods form two well-separated clusters, presenting a wide gap to be bridged. To bridge this gap, we propose a Bridged Semantic Alignment (BrgSA) framework. First, we utilize a large language model to perform semantic summarization of reports, extracting high-level semantic information. Second, we design a Cross-Modal Knowledge Interaction (CMKI) module that leverages a cross-modal knowledge bank as a semantic bridge, facilitating interaction between the two modalities, narrowing the gap, and improving their alignment. To comprehensively evaluate our method, we construct a benchmark dataset that includes 15 underrepresented abnormalities as well as utilize two existing benchmark datasets. Experimental results demonstrate that BrgSA achieves state-of-the-art performances on both public benchmark datasets and our custom-labeled dataset, with significant improvements in zero-shot diagnosis of underrepresented abnormalities. © 2025, CC BY.

关键词： Zero-shot learning

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A Cross-Domain Ontology Semantic Representation Based on NCBI-BlueBERT Embedding

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Chinese Journal of Electronics 2022年第5期31卷 860-869页

作者： ZHAO Lingling WANG Junjie WANG Chunyu GUO Maozu Faculty of Computing Harbin Institute of Technology Department of Medical Informatics School of Biomedical Engineering and InformaticsNanjing Medical University Beijing Key Laboratory of Intelligent Processing for Building Big Data School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture

A common but critical task in biological ontologies data analysis is to compare the difference between ontologies. There have been numerous ontologybased semantic-similarity measures proposed in specific ontology domain, but it still remains a challenge for crossdomain ontologies comparison. An ontology contains the scientific natural language description for the corresponding biological aspect. Therefore, we develop a new method based on natural language processing(NLP) representation model bidirectional encoder representations from transformers(BERT) for cross-domain semantic representation of biological ontologies. This article uses the BERT model to represent the word-level of the ontologies as a set of vectors, facilitating the semantic analysis or comparing the biomedical entities named in an ontology or associated with ontology terms. We evaluated the ability of our method in two experiments: calculating similarities of pair-wise disease ontology and human phenotype ontology terms and predicting the pair-wise of proteins interaction. The experimental results demonstrated the comparative performance. This gives promise to the development of NLP methods in biological data analysis.

关键词： human phenotype ontology cross-domain semantic representation natural language description pair-wise disease ontology data analysis bidirectional encoder representations NLP protein interaction diseases proteins natural language processing ontologies (artificial intelligence) natural language processing representation neural nets bioinformatics semantic-similarity measures cross-domain ontology semantic representation semantic analysis NCBI-BlueBERT embedding biological ontology data analysis

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Reliability Analysis of Heterogeneous Sensor-Cloud Systems against Targeted Attacks

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China Communications 2022年第8期19卷 181-197页

作者： Hao Peng Zhen Qian Guangquan Xu Kejie Mao Kangtian Li Dandan Zhao Department of Computer Science and Engineering Zhejiang Normal UniversityJinhua Zhejiang 321004China Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province Zhejiang Normal UniversityJinhua Zhejiang 321004China The Big Data School Qingdao Huanghai UniversityQingdao Shandong 266427China College of Intelligence and Computing Tianjin UniversityTianjin 300072China

Based on the wide application of cloud computing and wireless sensor networks in various fields,the Sensor-Cloud System(SCS)plays an indispensable role between the physical world and the network ***,due to the close connection and interdependence between the physical resource network and computing resource network,there are security problems such as cascading failures between systems in the *** this paper,we propose a model with two interdependent networks to represent a sensor-cloud ***,based on the percolation theory,we have carried out a formulaic theoretical analysis of the whole process of cascading *** the system’s subnetwork presents a steady state where there is no further collapse,we can obtain the largest remaining connected subgroup components and the penetration ***,this result is the critical maximum that the coupled SCS can *** verify the correctness of the theoretical results,we further carried out actual simulation *** results show that a scale-free network priority attack’s percolation threshold is always less than that of ER network which is priority ***,when the scale-free network is attacked first,adding the power law exponentλcan be more intuitive and more effective to improve the network’s reliability.

关键词： sensor-cloud systems targeted attacks heterogeneous networks reliability analysis system security

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Representing Multiview Time-Series Graph Structures for Multivariate Long-Term Time-Series Forecasting

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2651-2662页

作者： Wang, Zehao Fan, Jin Wu, Huifeng Sun, Danfeng Wu, Jia Hangzhou Dianzi University Department of Computer Science and Technology Hangzhou310018 China Zhejiang Provincial Key Laboratory of Industrial Internet in Discrete Industries Hangzhou310018 China Zhejiang Normal University Laboratory of Intelligent Education Technology and Application of Zhejiang Province Hangzhou310018 China Macquarie University School of Computing SydneyNSW2113 Australia

Multivariate long-term time-series forecasting tasks are very challenging tasks in many real-world application areas. Recently, researchers focus on designing robust and effective methods, and have made considerable progress. However, there are several issues with existing models that need to be overcome. First, the lack of a relationship structure between multivariate variables needs to be addressed. Second, most models only have a weak ability to capture local dynamic changes across the entire long-term time-series. Third, current models suffer from high computational complexity and unsatisfactory accuracy. To figure out the abovementioned issues, we propose an effective and efficient method called multiview time-series graph structure representation (MTGSR). MTGSR uses GCNs to construct topological relationships in the multivariate time-series from three different perspectives: time, dimension, and crossing segments. Variation trends in different dimensions are extracted through a difference operation to construct topological maps that reflect the correlations between different dimensions. To capture the dynamically changing characteristics of fluctuation correlations between adjacent local sequences, MTGSR constructs a cross graph by calculating correlation coefficients between adjacent local sequences. Extensive experiments show that MTGSR reduces errors by 17.41% over the state of the art. In addition, memory use is decreased by 66.52% and the running time is reduced by 78.09%. © 2020 IEEE.

关键词： Forecasting

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Adaptive time-stepping and maximum-principle preserving Lagrangian schemes for gradient flows

arXiv

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arXiv 2025年

作者： Liu, Qianqian Chen, Wenbin Shen, Jie Cheng, Qing School of Mathematical Sciences Fudan University Shanghai200433 China Shanghai Key Laboratory for Contemporary Applied Mathematics School of Mathematical Sciences Fudan University Shanghai200433 China Eastern Institute of Technology Zhejiang Ningbo315200 China Key Laboratory of Intelligent Computing and Application Tongji University Ministry of Education Department of Mathematics Tongji University Shanghai200092 China

We develop in this paper an adaptive time-stepping approach for gradient flows with distinct treatments for conservative and non-conservative dynamics. For the non-conservative gradient flows in Lagrangian coordinates, we propose a modified formulation augmented by auxiliary terms to guarantee positivity of the determinant, and prove that the corresponding adaptive second-order Backward Difference Formulas (BDF2) scheme preserves energy stability and the maximum principle under the time-step ratio constraint 0 n ≤ rmax ≤ 32. On the other hand, for the conservative Wasserstein gradient flows in Lagrangian coordinates, we propose an adaptive BDF2 scheme which is shown to be energy dissipative, and positivity preserving under the time-step ratio constraint 0 n ≤ rmax ≤ 3+√217 in 1D and 0 n ≤ rmax ≤ 54 in 2D, respectively. We also present ample numerical simulations in 1D and 2D to validate the efficiency and accuracy of the proposed *** Codes 35K55, 35K65, 65M06, 65M12 Copyright © 2025, The Authors. All rights reserved.

关键词： Lagrange multipliers

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Flickr group recommendation via heterogeneous information networks 15

Flickr group recommendation via heterogeneous information ne...

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7th International Conference on Internet Multimedia computing and Service, ICIMCS 2015

作者： Wang, Yueyang Xia, Yuanfang Tang, Siliang Wu, Fei Zhuang, Yueting College of Computer Science and Technology Zhejiang University Key Laboratory of Big Data Intelligent Computing of Zhejiang Province Innovation Joint Research Center for ICPS China

ISBN: (纸本)9781450335287

As an important characteristic of social media (i.e. Flickr or Facebook), user communities or groups are beginning to attract increasing attention. Most of the previous studies on group recommendation only consider a homogeneous relationship, such as the rating of users to groups in social network. In real world, each group has its textual descriptions (e.g. the "Sky & Cloud" group in Flickr), and the describing words about a given group may mention particular entities as well as their belonging categories (e.g. "Nature"). In fact, the group recommendation can be conducted in a heterogeneous information network, where informative cues are in general multi-typed. Motivated by the assumption that different types of relationships in heterogeneous information network can be potentially used to boost the performance of the group recommendation. We propose to combine heterogeneous relationship information for users with implicit user feedback. In general, the group recommendation in this paper is formulated as a non-negative matrix factorization (NMF) method regularized with user-user similarity via heterogeneous information networks. Experiments show that our proposed approach outperforms other counterpart recommendation techniques. © 2015 ACM.

关键词： Non-negative matrix factorization

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A transformer-based framework for temporal health event prediction with graph-enhanced representations

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Journal of biomedical Informatics 2025年 166卷 104826页

作者： Liu, Tianci Liang, Lizhong Che, Chao Liu, Yunjiong Jin, Bo Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University Liaoning Dalian116622 China School of Software Engineering Dalian University Liaoning Dalian116622 China School of Computer Science and Engineering Sun Yat-Sen University Guangdong Guangzhou510006 China Affiliated Hospital of Guangdong Medical University Guangdong Zhanjiang524013 China The Marine Biomedical Research Institute School of Ocean and Tropical Medicine Guangdong Medical University Guangdong Zhanjiang524023 China School of Innovation and Entrepreneurship Dalian University of Technology Liaoning Dalian116024 China

Objective: Deep learning approaches have demonstrated significant potential in predicting temporal health events in recent years. However, existing methods have not fully leveraged the complex interactions among comorbidities and have overlooked imbalances and temporal irregularities in admission records. Methods: This study proposes GLT-Net, a deep learning approach that combines Graph Learning with Transformer framework to tackle these challenges. GLT-Net begins by constructing a patient association graph to generate unique representations for each individual. At the same time, the hierarchical structure of diagnosis codes is utilized to pre-train the diagnosis code embeddings. Subsequently, a comorbidity association matrix is created to illustrate the relationships between comorbidities, and graph neural networks are employed to enhance the feature representations of diagnosis codes. Finally, a Transformer-Encoder framework captures the dependencies in historical admission records by incorporating time information. Results: We demonstrate our approach on two tasks in temporal health event predcition. Experimental results on real-world datasets show that GLT-Net outperforms baseline models in forecasting temporal health events. Additionally, a case study demonstrates the effectiveness of GLT-Net in predicting health events. Conclusion: Understanding progression patterns over time, comorbidity associations, and patient characterization is essential for predicting temporal health events. Our study provides new insights and methods for a deeper understanding of patient health status and disease trends. Moreover, our model can be extended to other data sources, enhancing its versatility. © 2025 Elsevier Inc.

关键词： Electronic health record

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Fair ultrasound diagnosis via adversarial protected attribute aware perturbations on latent embeddings

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npj Digital Medicine 2025年第1期8卷 1-9页

作者： Xu, Zikang Tang, Fenghe Quan, Quan Yao, Qingsong Kong, Qingpeng Ding, Jianrui Ning, Chunping Zhou, S. Kevin School of Biomedical Engineering Division of Life Sciences and Medicine University of Science and Technology of China (USTC) Hefei Anhui China Center for Medical Imaging Robotics Analytic Computing and Learning (MIRACLE) Suzhou Institute for Advance Research USTC Suzhou Jiangsu China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China School of Computer Science and Technology Harbin Institute of Technology Harbin Heilongjiang China Ultrasound Department The Affiliated Hospital of Qingdao University Qingdao Shangdong China State Key Laboratory of Precision and Intelligent Chemistry University of Science and Technology of China Hefei Anhui China

Deep learning techniques have significantly enhanced the convenience and precision of ultrasound image diagnosis, particularly in the crucial step of lesion segmentation. However, recent studies reveal that both train-from-scratch models and pre-trained models often exhibit performance disparities across sex and age attributes, leading to biased diagnoses for different subgroups. In this paper, we propose APPLE, a novel approach designed to mitigate unfairness without altering the parameters of the base model. APPLE achieves this by learning fair perturbations in the latent space through a generative adversarial network. Extensive experiments on both a publicly available dataset and an in-house ultrasound image dataset demonstrate that our method improves segmentation and diagnostic fairness across all sensitive attributes and various backbone architectures compared to the base models. Through this study, we aim to highlight the critical importance of fairness in medical segmentation and contribute to the development of a more equitable healthcare system. © The Author(s) 2025.

关键词： Image segmentation

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Flickr group recommendation with auxiliary information in heterogeneous information networks

Flickr group recommendation with auxiliary information in he...

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作者： Wang, Yueyang Xia, Yuanfang Tang, Siliang Wu, Fei Zhuang, Yueting Key Laboratory of Big Data Intelligent Computing of Zhejiang Province Innovation Joint Research Center for ICPS College of Computer Science and Technology Zhejiang University Hangzhou China

Over the past few years, the appropriate utilization of user communities or image groups in social networks (i.e., Flickr or Facebook) has drawn a great deal of attention. In this paper, we are particularly interested in recommending preferred groups to users who may favor according to auxiliary information. In real world, the images captured by mobile equipments explicitly record a lot of contextual information (e.g., locations) about users generating images. Meanwhile, several words are employed to describe the particular theme of each group (e.g., "Dogs for Fun Photos" image group in Flickr), and the words may mention particular entities as well as their belonging categories (e.g., "Animal"). In fact, the group recommendation can be conducted in heterogeneous information networks, where informative cues are in general multi-typed. Motivated by the assumption that the auxiliary information (visual features of images, mobile contextual information and entity-category information of groups in this paper) in heterogeneous information networks will boost the performance of the group recommendation, this paper proposes to combine auxiliary information with implicit user feedback for group recommendation. In general, the group recommendation in this paper is formulated as a non-negative matrix factorization (NMF) method regularized with user–user similarity via visual features and heterogeneous information networks. Experiments show that our proposed approach outperforms other counterpart recommendation approaches. © 2016, Springer-Verlag Berlin Heidelberg.

关键词： Non-negative matrix factorization

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Decomposition equation of basis images with consideration of global illumination

Decomposition equation of basis images with consideration of...

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1st International Conference on Computational Visual Media, CVM 2012

作者： Qin, Xueying Zhang, Rui Lin, Lili Zhong, Fan Xing, Guanyu Peng, Qunsheng School of Computer Science and Technology Shandong University China Shandong Provincial Key Laboratory of Network Based Intelligent Computing China State Key Laboratory of CAD and CG Zhejiang University China

ISBN: (纸本)9783642342622

In augmented reality, it is required to sense the changing of the light condition for achieving illumination consistency. In this paper, we build up the decomposition equation of basis images of static scenes for this purpose. It is proved that the basis images are invariants of the scene, which is the global illumination effects of a distributed light source with unit power, and it is unnecessary to assume that the reflectance of appearance of objects in scenes is ideal diffuse. Our method can also be applied in image understanding and compressing. © 2012 Springer-Verlag.

关键词： Augmented reality

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