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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

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STGCN: A Spatial-Temporal Aware Graph Learning Method for POI Recommendation

STGCN: A Spatial-Temporal Aware Graph Learning Method for PO...

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IEEE International Conference on Data Mining (ICDM)

作者： Haoyu Han Mengdi Zhang Min Hou Fuzheng Zhang Zhongyuan Wang Enhong Chen Hongwei Wang Jianhui Ma Qi Liu Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China Meituan-Dianping Group Stanford University

ISBN: (数字)9781728183169

ISBN: (纸本)9781728183176

Point-of-Interest (POI) recommendation helps users find their interested places to visit based on the time and user location. Unlike traditional recommendation tasks, POI recommendation is personalized, spatial-aware, and temporally dependent. Although many previous works have tried modeling spatial and temporal characteristics, most of them suffer from the following two limitations: For the spatial aspect, existing works only consider the user-POI distance or POI-POI distance. However, we find that a user prefers different regions at different times, which is known as user-region periodic pattern. For the temporal aspect, most works treat user and time as two independent factors. However, different users may prefer the same POI in different time periods, which is known as user-POI periodic pattern. To address the limitation of existing works, we propose a novel Spatial-Temporal aware Graph Convolutional Neural Network (STGCN) for POI recommendation. Specifically, we first design a user record multigraph to fuse all the context information into a unified graph. Then, we propose a time-based neighborhood sampling algorithm and take advantage of the flexible propagation mechanism of GCNs to learn the representations of each node at a specific time. Furthermore, multiple scoring functions are proposed to exploit user-region periodic pattern and user-POI periodic pattern, respectively. We also develop a time smoothing strategy to alleviate the data sparsity problem. Extensive experiments are conducted on two real-world datasets, and the experimental results demonstrate the effectiveness of our method.

关键词： Learning systems Heating systems Smoothing methods Image color analysis Fuses Data mining Task analysis

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Identification of Cirrhosis Subtypes Through Heterogeneous Medical Information Network

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Gastroenterology & Hepatology Research 2022年第3期4卷 11-22页

作者： Jun Li Xin Dong Ya-Na Zhou Hui-Kun Wu Xiao-Dong Li Xue-Zhong Zhou Clinical College of Traditional Chinese Medicine Hubei University of Chinese MedicineHubei 430061China Hubei Key Laboratory of Liver and Kidney Research and Application of Traditional Chinese Medicine Hubei Provincial Hospital of Traditional Chinese MedicineHubei 430061China Affiliated Hospital of Hubei University of Traditional Chinese Medicine Hubei 430061China Hubei Academy of Traditional Chinese Medicine Hubei 430074China Institute of Medical Intelligence School of Computer and Information TechnologyBeijing Jiaotong UniversityBeijing 100063China

Background:Liver cirrhosis is a complex and heterogeneous disease,with a mortality rate of up to 57%,resulting in 1.03 million deaths per *** prevalence of liver cirrhosis is on the *** with liver cirrhosis have a variety of clinical phenotypes and are prone to various complications related to liver ***,there is an urgent need to improve the early prevention and clinical management of cirrhosis and its ***:We use a precise medical approach to analyze and characterize the complex manifestations of cirrhotic patient populations,and we propose a Heterogeneous Medical Record Network(HEMnet)that includes electronic medical records,molecular interaction networks,and domain *** train the network embedding vector on HEMnet to obtain the low-dimensional vector representation of each *** these vectors,we enriched the original medical record and identified six subtypes of ***:Subtype 1 is characterized by heart disease,and subtype 2 has the strongest association with metabolic-related *** 3 was characterized by Chronic gastritis *** 4 was characterized by Liver cirrhosis-related complications-serous *** 5 had the strongest association with hepatitis-cirrhosis-related complications diseases and gallbladder *** 6 was most strongly associated with Liver cirrhosis-related complications and hepatic *** assessing the human disease-gene association of each subtype,the rich phenotype and biological functions of each subtype at the gene level were matched to the disease comorbidities and clinical differences we identified through ***:Our approach demonstrates the utility of applying a precision medicine paradigm to cirrhosis and the prospect of extending this approach to other complexes,multifactorial diseases.

关键词： heterogeneous medical record network liver cirrhosis subtype characteristics

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Corrigendum to “Multi-classification of fundus diseases based on DSRA-CNN” [Biomed. Signal Process. Control 77 (2022) 103763]

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Biomedical Signal Processing and Control 2022年 77卷

作者： Xue-lian Yang San-li Yi School of Information Engineering and Automation Kunming University of Science and Technology Kunming 650500 Yunnan China Key Laboratory of Computer Technology Application of Yunnan Province Kunming 650500 China

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Missed prediction of the neutron halo in 37Mg

arXiv

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arXiv 2023年

作者： Zhang, K.Y. Yang, S.Q. An, J.L. Zhang, S.S. Papakonstantinou, P. Mun, M.-H. Kim, Y. Yan, H. Institute of Nuclear Physics and Chemistry CAEP Sichuan Mianyang621900 China State Key Laboratory of Nuclear Physics and Technology School of Physics Peking University Beijing100871 China Institute of Computer Application CAEP Sichuan Mianyang621900 China School of Physics Beihang University Beijing100191 China Rare Isotope Science Project Institute for Basic Science Daejeon34000 Korea Republic of Department of Physics Origin of Matter and Evolution of Galaxy Institute Soongsil University Seoul06978 Korea Republic of Center for Exotic Nuclear Studies Institute for Basic Science Daejeon34126 Korea Republic of Key Laboratory of Neutron Physics Institute of Nuclear Physics and Chemistry CAEP Sichuan Mianyang621900 China

Halo phenomena have long been an important frontier in both experimental and theoretical nuclear physics. 37Mg was identified as a halo nucleus in 2014 and remains the heaviest nuclear halo system to date. While the halo phenomenon in 37Mg was not predicted before the discovery, its description has been still challenging afterwards. In this Letter, we report a microscopic and self-consistent description of the neutron halo in 37Mg using the deformed relativistic Hartree-Bogoliubov theory in continuum (DRHBc) that was developed in 2010. The experimental neutron separation energies and empirical matter radii of neutron-rich magnesium isotopes as well as the deformed p-wave halo characteristics of 37Mg are well reproduced without any free parameters. In particular, the orbital occupied by the halo neutron in 37Mg, exhibiting p-wave components comparable to those suggested in experiments, remains consistent across various employed density functionals including PC-F1, PC-PK1, NL3*, and PK1. The DRHBc theory investigated only even-even magnesium isotopes in previous works and for that reason missed predicting 37Mg as a halo nucleus before 2014. Although the core and the halo of 37Mg are both prolate, higher-order shape decoupling on the hexadecapole and hexacontatetrapole levels is predicted. Copyright © 2023, The Authors. All rights reserved.

关键词： Neutrons

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Multi-Modal Transformer for Accelerated MR Imaging

arXiv

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arXiv 2021年

作者： Feng, Chun-Mei Yan, Yunlu Chen, Geng Xu, Yong Hu, Ying Shao, Ling Fu, Huazhu Shenzhen Key Laboratory of Visual Object Detection and Recognition Harbin Institute of Technology Shenzhen518055 China National Engineering Laboratory for Integrated AeroSpace-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an710072 China Terminus Group China Institute of High Performance Computing A*STAR Singapore138632 Singapore

Accelerated multi-modal magnetic resonance (MR) imaging is a new and effective solution for fast MR imaging, providing superior performance in restoring the target modality from its undersampled counterpart with guidance from an auxiliary modality. However, existing works simply combine the auxiliary modality as prior information, lacking in-depth investigations on the potential mechanisms for fusing different modalities. Further, they usually rely on the convolutional neural networks (CNNs), which is limited by the intrinsic locality in capturing the long-distance dependency. To this end, we propose a multi-modal transformer (MTrans), which is capable of transferring multi-scale features from the target modality to the auxiliary modality, for accelerated MR imaging. To capture deep multi-modal information, our MTrans utilizes an improved multi-head attention mechanism, named cross attention module, which absorbs features from the auxiliary modality that contribute to the target modality. Our framework provides three appealing benefits: (i) Our MTrans use an improved transformers for multi-modal MR imaging, affording more global information compared with existing CNN-based methods. (ii) A new cross attention module is proposed to exploit the useful information in each modality at different scales. The small patch in the target modality aims to keep more fine details, the large patch in the auxiliary modality aims to obtain high-level context features from the larger region and supplement the target modality effectively. (iii) We evaluate MTrans with various accelerated multi-modal MR imaging tasks, e.g., MR image reconstruction and super-resolution, where MTrans outperforms state-of-the-art methods on fastMRI and real-world clinical datasets. © 2021, CC BY.

关键词： Magnetic resonance imaging

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A convolutional neural network-based diagnostic method using resting-state electroencephalograph signals for major depressive and bipolar disorders

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Biomedical Signal Processing and Control 2022年 72卷

作者： Lei, Yu Belkacem, Abdelkader Nasreddine Wang, Xiaotian Sha, Sha Wang, Changming Chen, Chao Beijing Institute of Basic Medical Sciences Beijing China Department of Computer and Network Engineering College of Information Technology United Arab Emirates University Al Ain United Arab Emirates School of Artificial Intelligence Xidian University Xian China National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders Beijing Anding Hospital Capital Medical University Beijing China Brain-inspired Intelligence and Clinical Translational Research Center/Department of Neurosurgery Xuanwu Hospital Capitap Medical University Beijing China Key Laboratory of Complex System Control Theory and Application Tianjin University of Technology China Academy of Medical Engineering and Translational Medicine Tianjin University Tianjin China

Background: Early and accurate diagnosis of bipolar and major depressive disorders is important in clinical practice. However, no diagnostic biomarkers can discriminate bipolar from major depressive disorder with high accuracy at present. Methods: We propose a novel convolutional neural network architecture using multichannel raw resting-state electroencephalograph signals to differentiate bipolar disorder from major depressive disorder. This method has great potential in diagnosing mental disorders. In total, 101 patients with major depressive disorder, 82 patients with bipolar disorder, and 81 healthy controls were assessed. Clinical diagnosis was performed by psychiatrists based on the Diagnostic and Statistical Manual of Mental Disorders, fifth edition. Participants were instructed to fix their eyes on a cross on the monitor during the collection of resting-state electroencephalograph signals. Results: A classification mean accuracy of 96.88% was achieved using the resting-state electroencephalograph signals with the 10-fold cross-validation method. The results indicate that deep neural networks could learn efficient feature patterns automatically without manual feature selections. Both statistical analysis and feature visualization on different brain regions showed that the classification performance and the learned features of the proposed model were consistent with that obtained in neurobiological studies on bipolar and major depressive disorders. Conclusions: The combined use of deep neural networks and electroencephalograph signals is an effective approach for the computer-aided diagnosis of bipolar and major depressive disorders. © 2021 Elsevier Ltd

关键词： Deep neural networks

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Ultrasonic backscatter communication for implantable medical devices

arXiv

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arXiv 2022年

作者： Wang, Qianqian Guan, Quansheng Cheng, Julian Tang, Yuankun School of Electronic and Information Engineering South China University of Technology Guangzhou510640 China School of Engineering The University of British Columbia KelownaBCV1V 1V7 Canada Key Laboratory of Marine Environmental Survey Technology and Application Ministry of Natural Resources Guangzhou510300 China Southampton Wireless Group School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom

This paper proposes an ultrasonic backscatter communication (UsBC) system for passive implantable medical devices (IMDs) that can operate without batteries, enabling versatile revolutionary applications for future healthcare. The proposed UsBC system consists of a reader and a tag. The reader sends interrogation pulses to the tag. The tag backscatters the pulses based on the piezoelectric effect of a piezo transducer. We present several basic modulation schemes for UsBC by impedance matching of the piezo transducer. To mitigate the interference of other scatters in the human body, the tag transmits information bits by codeword mapping, and the reader performs codeword matching before energy detection in the reader. We further derive the theoretical bit-error rate (BER) expression. Monte Carlo simulations verify the theoretical analysis and show that passive UsBC can achieve low BER and low complexity, which is desirable for size-and energy-constrained IMDs. Copyright © 2022, The Authors. All rights reserved.

关键词： Piezoelectricity

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Motif Graph Neural Network

arXiv

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arXiv 2021年

作者： Chen, Xuexin Cai, Ruichu Fang, Yuan Wu, Min Li, Zijian Hao, Zhifeng The School of Computer Science Guangdong University of Technology Guangzhou510006 China The School of Computer Science Guangdong University of Technology Guangdong Provincial Key Laboratory of Public Finance and Taxation with Big Data Application Guangzhou China Peng Cheng Laboratory Shenzhen518066 China The School of Computing and Information Systems Singapore Management University 178902 Singapore A*STAR 138632 Singapore The College of Science Shantou University Shantou515063 China

Graphs can model complicated interactions between entities, which naturally emerge in many important applications. These applications can often be cast into standard graph learning tasks, in which a crucial step is to learn low-dimensional graph representations. Graph neural networks (GNNs) are currently the most popular model in graph embedding approaches. However, standard GNNs in the neighborhood aggregation paradigm suffer from limited discriminative power in distinguishing high-order graph structures as opposed to low-order structures. To capture high-order structures, researchers have resorted to motifs and developed motif-based GNNs. However, existing motif-based GNNs still often suffer from less discriminative power on high-order structures. To overcome the above limitations, we propose Motif Graph Neural Network (MGNN), a novel framework to better capture high-order structures, hinging on our proposed motif redundancy minimization operator and injective motif combination. First, MGNN produces a set of node representations w.r.t. each motif. The next phase is our proposed redundancy minimization among motifs which compares the motifs with each other and distills the features unique to each motif. Finally, MGNN performs the updating of node representations by combining multiple representations from different motifs. In particular, to enhance the discriminative power, MGNN utilizes an injective function to combine the representations w.r.t. different motifs. We further show that our proposed architecture increases the expressive power of GNNs with a theoretical analysis. We demonstrate that MGNN outperforms state-of-the-art methods on seven public benchmarks on both node classification and graph classification tasks. Copyright © 2021, The Authors. All rights reserved.

关键词： Graph neural networks

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Quality Meets Diversity: A Model-Agnostic Framework for computerized Adaptive Testing

Quality Meets Diversity: A Model-Agnostic Framework for Comp...

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IEEE International Conference on Data Mining (ICDM)

作者： Haoyang Bi Haiping Ma Zhenya Huang Yu Yin Qi Liu Enhong Chen Yu Su Shijin Wang Anhui Province Key Laboratory of Big Data Analysis and ApplicationSchool of Computer Science and Technology University of Science and Technology of China CN Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China Anhui University CN IFLYTEK Research CN

ISBN: (数字)9781728183169

ISBN: (纸本)9781728183176

computerized Adaptive Testing (CAT) is emerging as a promising testing application in many scenarios, such as education, game and recruitment, which targets at diagnosing the knowledge mastery levels of examinees on required concepts. It shows the advantage of tailoring a personalized testing procedure for each examinee, which selects questions step by step, depending on her performance. While there are many efforts on developing CAT systems, existing solutions generally follow an inflexible model-specific fashion. That is, they need to observe a specific cognitive model which can estimate examinee's knowledge levels and design the selection strategy according to the model estimation. In this paper, we study a novel model-agnostic CAT problem, where we aim to propose a flexible framework that can adapt to different cognitive models. Meanwhile, this work also figures out CAT solution with addressing the problem of how to generate both high-quality and diverse questions simultaneously, which can give a comprehensive knowledge diagnosis for each examinee. Inspired by Active Learning, we propose a novel framework, namely Model-Agnostic Adaptive Testing (MAAT) for CAT solution, where we design three sophisticated modules including Quality Module, Diversity Module and Importance Module. Specifically, at one CAT selection step, Quality Module first quantifies the informativeness of questions and generates candidate subset with the highest quality. Then, Diversity Module selects one question at each step that maximizes the concept coverage. Additionally, we propose Importance Module to estimate the importance of concepts that optimizes the CAT selection. Under MAAT, we prove that the goal of maximizing both quality and diversity is NP-hard, but we provide efficient algorithms by exploiting the inherent submodular property. Extensive experimental results on two real-world datasets clearly demonstrate that our MAAT can support CAT with guaranteeing both quality and diversity

关键词： Adaptation models Cats Computational modeling Estimation Games Testing Recruitment

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