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DG-CNN:Introducing Margin Information into Convolutional Neural Networks for Breast Cancer Diagnosis in Ultrasound Images

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Journal of computer science & Technology 2022年第2期37卷 277-294页

作者： Xiao-Zheng Xie Jian-Wei Niu Senior Member Xue-Feng Liu Qing-Feng Li Yong Wang Jie Han Shaojie Tang State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang UniversityBeijing 100191China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityHangzhou 310051China Department of Diagnostic Ultrasound National Cancer CenterChinese Academy of Medical SciencesPeking Union Medical CollegeBeijing 100021China Naveen Jindal School of Management The University of Texas at DallasRichardsonTX 75080-3021U.S.A.

Although using convolutional neural networks(CNNs)for computer-aided diagnosis(CAD)has made tremendous progress in the last few years,the small medical datasets remain to be the major bottleneck in this *** address this problem,researchers start looking for information out of the medical *** efforts mainly leverage information from natural images via transfer *** recent research work focuses on integrating knowledge from medical practitioners,either letting networks resemble how practitioners are trained,how they view images,or using extra *** this paper,we propose a scheme named Domain Guided-CNN(DG-CNN)to incorporate the margin information,a feature described in the consensus for radiologists to diagnose cancer in breast ultrasound(BUS)*** DG-CNN,attention maps that highlight margin areas of tumors are first generated,and then incorporated via different approaches into the *** have tested the performance of DG-CNN on our own dataset(including 1485 ultrasound images)and on a public *** results show that DG-CNN can be applied to different network structures like VGG and ResNet to improve their *** example,experimental results on our dataset show that with a certain integrating mode,the improvement of using DG-CNN over a baseline network structure ResNet 18 is 2.17%in accuracy,1.69%in sensitivity,2.64%in specificity and 2.57%in AUC(Area Under Curve).To the best of our knowledge,this is the first time that the margin information is utilized to improve the performance of deep neural networks in diagnosing breast cancer in BUS images.

关键词： medical consensus domain knowledge breast cancer diagnosis margin map deep neural network

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Five-user quantum virtual local area network with an AlGaAs entangled photon source

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science China(Physics,Mechanics & Astronomy) 2025年第3期68卷 61-73页

作者： Cheng Qian Hongkai Tian Xu Jing Yichen Liu Zhe Chen Huiran Luo Yuan Du Xiaodong Zheng Tangsheng Chen Yuechan Kong Hua-Lei Yin Dong Jiang Bin Niu Liangliang Lu Key Laboratory of State Manipulation and Advanced Materials in Provincial Universities School of Physical Science and TechnologyNanjing Normal UniversityNanjing210023China School of Internet Anhui UniversityHefei230039China National Key Laboratory of Solid-State Microwave Devices and Circuits Nanjing Chip Valley Industrial Technology InstituteNanjing Electronic Devices InstituteNanjing210016China Research Center for Quantum Optics and Quantum Communication School of ScienceQingdao University of TechnologyQingdao266520China Department of Physics and Beijing Key Laboratory of Opto-electronic Functional Materials and Micro-nano Devices Key Laboratory of Quantum State Construction and Manipulation(Ministry of Education)Renmin University of ChinaBeijing100872China Beijing Academy of Quantum Information Sciences Beijing100193China National Engineering Research Center of Agro-Ecological Big Data Analysis and Application Anhui UniversityHefei230601China National Laboratory of Solid-State Microstructures and School of Physics Nanjing UniversityNanjing210093China Hefei National Laboratory Hefei230088China

The promotion of quantum network applications demands the scalable connection of quantum *** is preferable to set up multiple logical networks coexisting on a single physical network infrastructure to accommodate a larger number of *** we present a quantum virtual network architecture that offers this level of scalability,without being constrained to a fixed physical-layer network relying solely on passive multiplexing *** architecture can be understood as arising from the superposition of a fully connected entanglement distribution network and port-based virtual local area network,which group multiusers by access *** terms of hardware,we leverage a semiconductor chip with a high figure-of-merit modal overlap to directly generate high-quality polarization entanglement,and a streamlined polarization analysis module,which requires only one single-photon detector for each end *** experimentally perform the BBM92 QKD protocol on the five-user quantum virtual network and demonstrate voice and image encryption on a campus area *** results may provide insights into the realization of large-scale quantum networks with integrated and cost-efficient photonic architecture.

关键词： quantum network quantum entanglement distribution integrated photonics image and voice encryption

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Symptom Dimensions and Cognitive Impairments in Individuals at Clinical High Risk for Psychosis

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Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 2025年第6期10卷 646-655页

作者： Zhang, TianHong Xu, LiHua Wei, YanYan Cui, HuiRu Tang, XiaoChen Hu, YeGang Liu, HaiChun Wang, ZiXuan Chen, Tao Yi, ZhengHui Li, ChunBo Wang, JiJun Shanghai Mental Health Center Shanghai Jiaotong University School of Medicine Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention Shanghai Key Laboratory of Psychotic Disorders Shanghai China Department of Automation Shanghai Jiao Tong University Shanghai China Department of Psychology Shanghai Xinlianxin Psychological Counseling Center Shanghai China Big Data Research Laboratory University of Waterloo Waterloo ON Canada Labor and Worklife Program Harvard University Cambridge Massachusetts United States Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai China Institute of Psychology and Behavioral Science Shanghai Jiao Tong University Shanghai China

Background: Understanding the intricate relationships between symptom dimensions, clusters, and cognitive impairments is crucial for early detection and intervention in individuals at clinical high risk for psychosis. This study delves into this complex interplay in a clinical high risk sample with the aim of predicting the conversion to psychosis. Methods: A comprehensive cognitive assessment was performed in 744 clinical high risk individuals. The study included a 3-year follow-up period to allow assessment of conversion to psychosis. Symptom profiles were determined using the Structured Interview for Prodromal Syndromes. By applying factor analysis, symptom dimensions were categorized as dominant negative symptoms (NS), positive symptoms-stressful, and positive symptoms-odd. The factor scores were used to define 3 dominant symptom groups. Latent class analysis (LCA) and the factor mixture model (FMM) were employed to identify discrete clusters based on symptom patterns. The 3-class solution was chosen for the LCA and FMM analysis. Results: Individuals in the dominant NS group exhibited significantly higher conversion rates to psychosis than those in the other groups. Specific cognitive variables, including performance on the Brief Visuospatial Memory Test–Revised (odds ratio = 0.702, p = .001) and Neuropsychological Assessment Battery Mazes Test (odds ratio = 0.776, p = .024), significantly predicted conversion to psychosis. Notably, cognitive impairments associated with NS and positive symptoms-stressful groups affected different cognitive domains. LCA and FMM cluster 1, which was characterized by severe NS and positive symptoms-odd, exhibited more impairments in cognitive domains than other clusters. No significant difference in the conversion rate was observed among the LCA and FMM clusters. Conclusions: These findings highlight the importance of NS in the development of psychosis and suggest specific cognitive domains that are affected by symptom dimensions.

关键词： Negative symptoms Neurocognition Subtype Transition Ultra high risk

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A subjective questionnaire system based on privacy-friendly incentive protocols 2

A subjective questionnaire system based on privacy-friendly ...

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2nd International Symposium on computer Applications and Information Systems, ISCAIS 2023

作者： Liu, Xin Sun, Shiwen School of Information Engineering Shandong Youth University of Political Science Shandong Jinan250103 China New Technology Research and Development Center of Intelligent Information Controlling in Universities of Shandong Shandong Jinan250103 China Smart Healthcare Big Data Engineering and Ubiquitous Computing Characteristic Laboratory in Universities of Shandong Shandong Jinan250103 China

ISBN: (纸本)9781510666665

Questionnaire survey systems are a kind of computer-aided survey tool, which are helpful for the questionnaire publisher to collect the opinions and appeals of the respondents. Nevertheless, it is difficult for the questionnaire publisher to solve tricky questions such as respondents’ inactivity in answering questions, especially in the subjective questionnaire. The paper extends the privacy-friendly incentive protocols of Camenisch et al. based on the technique of signatures of knowledge and the Au-Susilo-Mu signature and put forward a subjective questionnaire system oriented to professional course teaching. The new system controls the range of participants by specific attributes. It also allows participants to remain anonymous at all phases, so they can truly feedback on their subjective evaluations. Moreover, when students are not within the scope of the survey, they are encouraged to help to review the collected subjective questionnaire answers. Furthermore, the new system introduces a token mechanism to stimulate students’ participation. Security analysis shows that the new system satisfies correctness, balance, anonymity, and unlinkability. © 2023 SPIE.

关键词： Network security

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Unsupervised Domain Adaptive Person Re-Identification with Adaptive Structure Learning

Unsupervised Domain Adaptive Person Re-Identification with A...

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IEEE International Conference on Image Processing

作者： Yang Peng Ping Liu Xiyuan Yang Pan Zhou Yawei Luo Jingen Liu The Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Center for Frontier AI Research A*STAR Singapore School of Software Technology ZheJiang University Disney Streaming USA

Unsupervised domain adaptive person re-identification has garnered considerable attention due to its practical significance. Previous research has focused on utilizing the teacher-student framework within the clustering and fine-tuning paradigm to reduce the domain gap between different person re-identification datasets. Building upon these recent advancements, we propose a novel approach to further explore the structural information of the teacher and student networks from multiple perspectives, including adaptive sample updating, global feature arrangement, and local discrepancy learning. By integrating these three components, we introduce the Adaptive Structure Learning Framework (ASL) for unsupervised domain adaptive person re-identification. Compared to the baseline method, our approach achieves a significant improvement of 7.3% in mean Average Precision (mAP) on the Market2MSMT task. Furthermore, our experimental results across three benchmark datasets provide further evidence of the effectiveness of our proposed method.

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GoMatching: a simple baseline for video text spotting via long and short term matching 24

GoMatching: a simple baseline for video text spotting via lo...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Haibin He Maoyuan Ye Jing Zhang Juhua Liu Bo Du Dacheng Tao School of Computer Science National Engineering Research Center for Multimedia Software and Institute of Artificial Intelligence Wuhan University China College of Computing & Data Science at Nanyang Technological University

ISBN: (纸本)9798331314385

Beyond the text detection and recognition tasks in image text spotting, video text spotting presents an augmented challenge with the inclusion of tracking. While advanced end-to-end trainable methods have shown commendable performance, the pursuit of multi-task optimization may pose the risk of producing sub-optimal outcomes for individual tasks. In this paper, we identify a main bottleneck in the state-of-the-art video text spotter: the limited recognition capability. In response to this issue, we propose to efficiently turn an off-the-shelf query-based image text spotter into a specialist on video and present a simple baseline termed GoMatching, which focuses the training efforts on tracking while maintaining strong recognition performance. To adapt the image text spotter to video datasets, we add a rescoring head to rescore each detected instance's confidence via efficient tuning, leading to a better tracking candidate pool. Additionally, we design a long-short term matching module, termed LST-Matcher, to enhance the spotter's tracking capability by integrating both long- and short-term matching results via Transformer. Based on the above simple designs, GoMatching delivers new records on ICDAR15-video, DSText, BOVText, and our proposed novel test set with arbitrary-shaped text termed ArTVideo, which demonstrates GoMatching's capability to accommodate general, dense, small, arbitrary-shaped, Chinese and English text scenarios while saving considerable training budgets.

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A deep learning fusion model for accurate classification of brain tumours in Magnetic Resonance images

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 790-804页

作者： Nechirvan Asaad Zebari Chira Nadheef Mohammed Dilovan Asaad Zebari Mazin Abed Mohammed Diyar Qader Zeebaree Haydar Abdulameer Marhoon Karrar Hameed Abdulkareem Seifedine Kadry Wattana Viriyasitavat Jan Nedoma Radek Martinek Department of Information Technology Lebanese French UniversityErbilIraq Department of Computer Science University of ZakhoZakhoKurdistan RegionIraq Department of Computer Science College of ScienceNawroz UniversityDuhokKurdistan RegionIraq Department of Artificial Intelligence College of Computer Science and Information TechnologyUniversity of AnbarRamadiIraq Department of Telecommunications VSB‐Technical University of OstravaOstravaCzech Republic Department of Cybernetics and Biomedical Engineering VSB‐Technical University of OstravaOstravaCzech Republic Department of Computer Network and Information Security Technical College of Informatics-AkreDuhok Polytechnic UniversityDuhokIraq Information and Communication Technology Research Group Scientific Research CenterAl‐Ayen UniversityThi‐QarIraq College of Computer Sciences and Information Technology University of KerbalaKarbalaIraq College of Agriculture Al‐Muthanna UniversitySamawahIraq Department of Applied Data Science Noroff University CollegeKristiansandNorway Faculty of Commerce and Accountancy Chulalongkorn Business SchoolChulalongkorn UniversityBangkokThailand

Detecting brain tumours is complex due to the natural variation in their location, shape, and intensity in images. While having accurate detection and segmentation of brain tumours would be beneficial, current methods still need to solve this problem despite the numerous available approaches. Precise analysis of Magnetic Resonance Imaging (MRI) is crucial for detecting, segmenting, and classifying brain tumours in medical diagnostics. Magnetic Resonance Imaging is a vital component in medical diagnosis, and it requires precise, efficient, careful, efficient, and reliable image analysis techniques. The authors developed a Deep Learning (DL) fusion model to classify brain tumours reliably. Deep Learning models require large amounts of training data to achieve good results, so the researchers utilised data augmentation techniques to increase the dataset size for training models. VGG16, ResNet50, and convolutional deep belief networks networks extracted deep features from MRI images. Softmax was used as the classifier, and the training set was supplemented with intentionally created MRI images of brain tumours in addition to the genuine ones. The features of two DL models were combined in the proposed model to generate a fusion model, which significantly increased classification accuracy. An openly accessible dataset from the internet was used to test the model's performance, and the experimental results showed that the proposed fusion model achieved a classification accuracy of 98.98%. Finally, the results were compared with existing methods, and the proposed model outperformed them significantly.

关键词： brain tumour deep learning feature fusion model MRI images multi‐classification

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Nitrate ameliorates myelin loss and cognitive impairment in Alzheimer’s disease through upregulation of neuronal sialin and subsequent inhibition of TPPP phosphorylation

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science Bulletin 2025年第8期70卷 1224-1229页

作者： Xinyue Chen Geng Hu Lirong Chang Xiaoyu Li Yi Tang Yan Wu Ran Zhang Chunmei Zhang Jinsong Wang Xue Wang Songlin Wang Department of Biochemistry and Molecular Biology School of Basic Medical SciencesBeijing Laboratory of Oral HealthCapital Medical UniversityBeijing 100069China Key Laboratory of Big Data-Based Precision Medicine School of Engineering MedicineBeihang UniversityBeijing 100191China Beijing Key Laboratory of Neural Regeneration and Repair Department of AnatomySchool of Basic Medical SciencesBeijing Institute of Brain DisordersCapital Medical UniversityBeijing 100069China Innovation Center for Neurological Disorders Department of NeurologyXuanwu HospitalCapital Medical University Beijing Key Laboratory of Traditional Chinese Veterinary Medicine Department of Veterinary MedicineAnimal Science and Technology CollegeBeijing University of AgricultureBeijing 102206China Department of Oral Pathology Peking University School and Hospital of Stomatology&National Center of Stomatology&National Clinical Research Center for Oral Diseases&National Engineering Research Center of Oral Biomaterials and Digital Medical DevicesBeijing 100081China National Human Brain Bank for Development and Function Institute of Basic Medical Sciences Chinese Academy of Medical SciencesSchool of Basic Medicine Peking Union Medical CollegeBeijing 10005China Laboratory of Homeostatic Medicine School of MedicineSouthern University of Science and TechnologyShenzhen 518055China Research Units of Tooth Development and Regeneration Chinese Academy of Medical SciencesBeijing 100700China

Alzheimer’s disease(AD)is a progressive neurodegenerative disorder characterized by cognitive *** pathological hallmarks of AD include the deposition of b-amyloid(Ab)plaques,hyperphosphorylation of tau protein,synaptic loss,and reactive astrogliosis in the brain[1].As there is still no effective therapy for AD,the development of novel treatment strategies is imperative.

关键词： reactive astrogliosis development novel treatment strategies cognitive impairment alzheimer s disease ad myelin loss neuronal sialin Alzheimers disease nitrate

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A Triangular Stable Node Network based on Self-supervised Learning for personalized prediction

A Triangular Stable Node Network based on Self-supervised Le...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Qing Gao, Qian Fan, Jun Zhang, Zhiqiang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan250014 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan250014 China China Telecom Digital Intelligence Techonology Co Ltd. No.1999 Shunhua road Shandong Jinan250101 China

ISBN: (纸本)9798350368741

In recent years, research has illuminated the potency of implicit data processing in enhancing user preferences. Nevertheless, barriers remain in breaking through the constraints of implicit information. This study aims to bridge this gap by firstly constructing a triangular stable node network model, tailored to manage implicit information with precision. Recognizing the challenge of pinpointing novel structures within large-scale graphs, we propose SLTSNN-a triangular stable node network based on self-supervised learning for personalized prediction. SLTSNN innovates by maximizing mutual information between graph-level and patch-level representations, while augmenting graph representations through extended enhanced representations. Additionally, it incorporates triangular data associations and introduces a triangular allocation attention network, which emphasizes strongly correlated preference features among similar users. Furthermore, SLTSNN employs contrastive learning to maximize mutual information between graph vectors and hidden representations, distinguishing between high-order global and local representations. The model's effectiveness in enhancing user preferences and capturing new graph structures is evidenced by its performance on hit rate and normalized discounted cumulative gain metrics across three datasets. © 2025 IEEE.

关键词： graph contrastive learning graph neural networks recommender systems self-supervised learning

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Exploring the Potential of Large Vision-Language Models for Unsupervised Text-Based Person Retrieval 39

Exploring the Potential of Large Vision-Language Models for ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Li, Zongyi Jianbo, Li Shi, Yuxuan Chen, Jiazhong Huang, Shijuan Tu, Linnan Shen, Fei Ling, Hefei Department of Computer Science and Technology Huazhong University of Science and Technology China National Engineering Research Center of Educational Big Data The Faculty of Artificial Intelligence in Education Central China Normal University China Nanjing University of Science and Technology China

ISBN: (纸本)157735897X

The aim of text-based person retrieval is to identify pedestrians using natural language descriptions within a large-scale image gallery. Traditional methods rely heavily on manually annotated image-text pairs, which are resource-intensive to obtain. With the emergence of Large Vision-Language Models (LVLMs), the advanced capabilities of contemporary models in image understanding have led to the generation of highly accurate captions. Therefore, this paper explores the potential of employing Large Vision-Language Models for unsupervised text-based pedestrian image retrieval and proposes a Multi-grained Uncertainty Modeling and Alignment framework (MUMA). Initially, multiple Large Vision-Language Models are employed to generate diverse and hierarchically structured pedestrian descriptions across different styles and granularities. However, the generated captions inevitably introduce noise. To address this issue, an uncertainty-guided sample filtration module is proposed to estimate and filter out unreliable image-text pairs. Additionally, to simulate the diversity of styles and granularities in captions, a multi-grained uncertainty modeling approach is applied to model the distributions of captions, with each caption represented as a multivariate Gaussian distribution. Finally, a multi-level consistency distillation loss is employed to integrate and align the multi-grained captions, aiming to transfer knowledge across different granularities. Experimental evaluations conducted on three widely-used datasets demonstrate the significant advancements achieved by our approach. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Gaussian distribution

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