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arXiv 2020年

作者： Cai, Jia Lv, Kexin Huo, Junyi Huang, Xiaolin Yang, Jie School of Statistics and Mathematics Guangdong University of Finance & Economics Big Data and Educational Statistics Application Laboratory 21 Chisha Road Guangzhou Guangdong510320 China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University MOE Key Laboratory of System Control and Information Processing 800 Dongchuan Road Shanghai200240 China School of Electronics and Computer Science University of Southampton University Road SouthamptonSO17 1BJ United Kingdom

Sparse canonical correlation analysis (CCA) is a useful statistical tool to detect latent information with sparse structures. However, sparse CCA works only for two datasets, i.e., there are only two views or two distinct objects. To overcome this limitation, in this paper, we propose a sparse generalized canonical correlation analysis (GCCA), which could detect the latent relations of multiview data with sparse structures. Moreover, the introduced sparsity could be considered as Laplace prior on the canonical variates. Specifically, we convert the GCCA into a linear system of equations and impose 1 minimization penalty for sparsity pursuit. This results in a nonconvex problem on Stiefel manifold, which is difficult to solve. Motivated by Boyd’s consensus problem, an algorithm based on distributed alternating iteration approach is developed and theoretical consistency analysis is investigated elaborately under mild conditions. Experiments on several synthetic and real world datasets demonstrate the effectiveness of the proposed algorithm. Copyright © 2020, The Authors. All rights reserved.

关键词： Statistical mechanics

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Dysfunction and recovery of the cortical connectome gradient and its association with gene expression profiles in methamphetamine and heroin use disorders

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Progress in Neuro-Psychopharmacology and Biological Psychiatry 2025年 139卷 111391页

作者： Du, Zhe Yang, Wenhan Wen, Xinwen Cai, Suping Liu, Jun Yuan, Kai School of Life Science and Technology Xidian University Shaanxi Xi'an 710126 China Department of Radiology Second Xiangya Hospital Central South University Changsha 410011 China Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing School of Information Engineering Inner Mongolia University of Science and Technology Inner Mongolia Baotou 014010 China Engineering Research Center of Molecular and Neuro Imaging Ministry of Education Shaanxi Xi'an China Ganzhou City Key Laboratory of Mental Health The Third People's Hospital of Ganzhou City Jiangxi Ganzhou 341000 China Xi'an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information School of Life Science and Technology Xidian University Shaanxi Xi'an 710126 China

Background: The hierarchy and segregation of community-based brain networks can be characterized by functional connectome gradient (FCG). Whether the cortical FCG was disrupted and could even be reversed after prolonged abstinence in substance use disorders (SUDs) remained unclear. Less is known about the underlying genetic basis. Methods: We utilized three independent datasets (n = 247) to investigate cortical FCG dysfunction and recovery after prolonged abstinence in methamphetamine use disorder (MUD) (n = 131), and validated in heroin use disorder (HUD) (n = 36). Furthermore, magnetic resonance spectroscopy (MRS) was employed to test the relationship between the glutamate in the ventral tegmental area (VTA) and cortical FCG. Postmortem gene expression was utilized to assess the transcriptional profiles related to cortical FCG alterations. Support vector regression (SVR) model based on the baseline cortical FCG was built to predict craving changes after long-term abstinence. Results: MUD and HUD individuals exhibited similar cortical FCG dysfunction and recovery at global and network levels, mainly in dorsal and ventral attentions network (DAN and VAN), frontoparietal network (FPN) and default mode network (DMN). The glutamate levels in VTA were correlated with the gradient distance between FPN and limbic network (LIM). The transcriptional profiles explained variance of the altered gradient pattern, with the most related genes enriched in synaptic signaling. Baseline cortical FCG significantly predicted craving changes after long-term abstinence. Conclusions: We provided novel insights into the understanding of SUD as treatable health conditions from cortical FCG changes, which could be considered as the potential biomarkers of craving changes after prolonged abstinence for SUDs. © 2025 Elsevier Inc.

关键词： Cortical functional connectome gradient Gene expression Glutamate Heroin use disorder Methamphetamine use disorder Support vector regression

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Learning data-adaptive non-parametric kernels

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2020年第1期21卷 8590-8628页

作者： Fanghui Liu Xiaolin Huang Chen Gong Jie Yang Li Li Department of Electrical Engineering ESAT-STADIUS KU Leuven Belgium Institute of Image Processing and Pattern Recognition Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology China and Department of Computing Hong Kong Polytechnic University Hong Kong SAR China Department of Automation BNRist Tsinghua University China

In this paper, we propose a data-adaptive non-parametric kernel learning framework in margin based kernel methods. In model formulation, given an initial kernel matrix, a data-adaptive matrix with two constraints is imposed in an entry-wise scheme. Learning this data-adaptive matrix in a formulation-free strategy enlarges the margin between classes and thus improves the model flexibility. The introduced two constraints are imposed either exactly (on small data sets) or approximately (on large data sets) in our model, which provides a controllable trade-off between model flexibility and complexity with theoretical demonstration. In algorithm optimization, the objective function of our learning framework is proven to be gradient-Lipschitz continuous. Thereby, kernel and classifier/regressor learning can be efficiently optimized in a unified framework via Nesterov's acceleration. For the scalability issue, we study a decomposition-based approach to our model in the large sample case. The effectiveness of this approximation is illustrated by both empirical studies and theoretical guarantees. Experimental results on various classification and regression benchmark data sets demonstrate that our non-parametric kernel learning framework achieves good performance when compared with other representative kernel learning based algorithms.

关键词： support vector machines non-parametric kernel learning gradient-Lipschitz continuous

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Current Progress and Challenges in Large-scale 3D Mitochondria Instance Segmentation

TechRxiv

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TechRxiv 2023年

作者： Franco-Barranco, Daniel Lin, Zudi Jang, Won-Dong Wang, Xueying Shen, Qijia Yin, Wenjie Fan, Yutian Li, Mingxing Chen, Chang Xiong, Zhiwei Xin, Rui Liu, Hao Chen, Huai Li, Zhili Zhao, Jie Chen, Xuejin Pape, Constantin Conrad, Ryan De Folter, Jozefus Nightingale, Luke Jones, Martin L. Liu, Yanling Ziaei, Dorsa Huschauer, Stephan Arganda-Carreras, Ignacio Pfister, Hanspeter Wei, Donglai The Department of Computer Science and Artificial Intelligence University of the Basque Country Donostia-San Sebastian Spain San Sebastian Spain Ikerbasque Basque Foundation for Science Bilbao Spain Biofisika Institute CSIC UPV/EHU Bilbao Spain Harvard University All-ston MA United States The Department of Molecular and Cellular Biology Harvard University CambridgeMA United States The Wellcome Centre for Integrative Neuroimaging FMRIB Nuffield Department of Clinical Neurosciences University of Oxford Oxford United Kingdom University of Science and Technology of China Anhui China The Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University Shanghai China The National Engineering Laboratory for Brain-inspired Intelligence Technology and Application University of Science and Technology of China Anhui China The Georg-August University Goettingen Germany The Center for Molecular Microscopy Center for Cancer Research National Cancer Institute National Institutes of Health Bethesda United States The Cancer Research Technology Program Frederick National Laboratory for Cancer Research Frederick United States The Francis Crick Institute London United Kingdom The Advanced Biomedical Computational Science Group Frederick National Laboratory for Cancer Research FrederickMD United States The Computer Science Department Boston College Chestnut Hill MA United States

In this paper, we present the results of the MitoEM challenge on mitochondria 3D instance segmentation from electron microscopy images, organized in conjunction with the IEEE-ISBI 2021 conference. Our benchmark dataset consists of two large-scale 3D volumes, one from human and one from rat cortex tissue, which are 3,600 times larger than previously used datasets. At the time of paper submission, 257 participants had registered for the challenge, 14 teams had submitted their results, and six teams participated in the challenge workshop. Here, we present eight top-performing approaches from the challenge participants, along with our own baseline strategies. Posterior to the challenge, annotation errors in the ground truth were corrected without altering the final ranking. Additionally, we present a retrospective evaluation of the scoring system which revealed that (1) the challenge metric was permissive with the false positive predictions and (2) the size-based grouping of instances did not correctly categorize mitochondria of interest. Thus, we propose a new scoring system that better reflects the correctness of the segmentation results. Although several of the top methods are compared favorably to our own baselines, substantial errors remain unsolved for mitochondria with challenging morphologies. Thus, the challenge remains open for submission and automatic evaluation, with all volumes available for download. © 2023, CC BY.

关键词： Electron microscopy

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Prediction of σ54 promoters in prokaryotes based on SVM–Adaboost

Prediction of σ54 promoters in prokaryotes based on SVM–Ad...

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Chinese Automation Congress (CAC)

作者： Yongxian Fan Qingqi Zhu Chengwei Lv Xianyong Pan School of Computer and Information Security Guilin University of Electronic Technology Guilin Guangxi Key Laboratory of System Control and Information Processing Ministry of Education of China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China

σ 54 promoters are responsible for transcriptional carbon and nitrogen in prokaryotes. However, it is costly and difficult by experimental identification of them, especially in the postgenomic era with avalanche of sequencing data. Thus, it is imperative to develop efficiently and rapidly computational algorithms to identify the σ 54 promoters. In this study, a novel predictor named SVM-Adaboost was developed to predict σ 54 promoters from sequences alone, it used the Adaboost algorithm as the core, and support vector machine (SVM) as weak base predictors. SVM-Adaboost integrated SVM predictors to construct a more powerful and robust ensemble predictor. In SVM-Adaboost, we used pseudo k-tuple nucleotide composition method to encode DNA sequences, and then a feature selection method was used to further select the discriminate features for subsequent classification. We strictly evaluate the SVM-Adaboost on a constructed gold-standard σ 54 promoter dataset using ten-fold cross validation 100 times, and achieved an average accuracy of 96.06%.

关键词： DNA Support vector machines Feature extraction Training Prediction algorithms Benchmark testing Computational modeling

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Automatic Monitoring of Driver's Physiological Parameters Based on Microarray Camera

Automatic Monitoring of Driver's Physiological Parameters Ba...

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IEEE Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS)

作者： Jiancheng Zou Zhengzheng Li Peizhou Yan Institute of Image Processing and Pattern Recognition North China University of Technology Beijing Key Laboratory of Urban Rod Traffic Intelligent Control Technology North China University of Technology Shijingshan District Beijing China

Driver's physical and mental states are very important factors affecting the driving states. Traffic accidents are occurred by accompanying abnormal physiological parameters. So how to monitor automatically driver's physiological parameters is an important problem. A real-time driver's physiological parameters monitoring method based on microarray camera is proposed in this paper. Firstly, we use a microarray camera to acquire videos in real time without contact. Secondly, the videos are registered and fused into a high quality video. Thirdly, deep learning framework is used to capture driver's facial data quickly. Then based on the principle of imaging photoplethysmography (IPPG) algorithm, physiological parameters such as heart rate (HR) and oxygen saturation (SpO 2 ) are monitored in real time. Results of expression analysis can further support the physiological parameters monitoring results. The experimental results show that the method proposed in this paper can monitor the physiological parameters of drivers in real time and accurately, and it is hopeful to play an active role in improving traffic safety.

关键词： Biomedical monitoring Monitoring Cameras Videos Heart rate Real-time systems

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A Hierarchical Model with Pseudoinverse Learning Algorithm Optimazation for Pulsar Candidate Selection

A Hierarchical Model with Pseudoinverse Learning Algorithm O...

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Congress on Evolutionary Computation

作者： Shijia Li Sibo Feng Ping Guo Qian Yin Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China

Pulsars search has always been one of the most concerned problem in the field of astronomy. Nowadays, with the development of astronomical instruments and observation technology, the amount of data is getting bigger and bigger. Radio pulsar surveys have generated and will generate vast amounts of data. To handle big data, developing new technologies and frameworks to efficiently and accurately analyze these data become increasing urgent. The number of positive and negative samples in pulsar candidate data set is very unbalanced, if we only use these a few positive samples to train a deep neural network (DNN), the trained DNN is prone because of the problem of overfitting and will affect the generalization ability. Motivated by the mixtures of experts network architecture, we proposed a hierarchical model for pulsar candidate selection which assembles a set of trained base classifiers. Moreover, training a neural network always takes a lot of time because of using gradient descent (GD) based algorithm. In this work, we utilize the pseudoinverse learning algorithm instead of GD based algorithm to train proposed model. With the designed network architecture and adopted training algorithm, our model has the advantages not only with high steady-state precision but also good generalization performance.

关键词： Matrix decomposition Training Big Data Neural networks Task analysis Decoding Machine learning

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Learning tubule-sensitive CNNs for pulmonary airway and artery-vein segmentation in CT

arXiv

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arXiv 2020年

作者： Qin, Yulei Zheng, Hao Gu, Yun Huang, Xiaolin Yang, Jie Wang, Lihui Yao, Feng Zhu, Yue-Min Yang, Guang-Zhong Institute of Image Processing and Pattern Recognition Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province School of Computer Science and Technology Guizhou University Guiyang China Department of Thoracic Surgery Shanghai Chest Hospital Shanghai Jiao Tong University Shanghai China Université de Lyon INSA Lyon CREATIS CNRS INSERM UMR 5220 VilleurbanneU1206 France Institute of Medical Robotics School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China

Training convolutional neural networks (CNNs) for segmentation of pulmonary airway, artery, and vein is challenging due to sparse supervisory signals caused by the severe class imbalance between tubular targets and background. We present a CNNs-based method for accurate airway and artery-vein segmentation in non-contrast computed tomography. It enjoys superior sensitivity to tenuous peripheral bronchioles, arterioles, and venules. The method first uses a feature recalibration module to make the best use of features learned from the neural networks. Spatial information of features is properly integrated to retain relative priority of activated regions, which benefits the subsequent channel-wise recalibration. Then, attention distillation module is introduced to reinforce representation learning of tubular objects. Fine-grained details in high-resolution attention maps are passing down from one layer to its previous layer recursively to enrich context. Anatomy prior of lung context map and distance transform map is designed and incorporated for better artery-vein differentiation capacity. Extensive experiments demonstrated considerable performance gains brought by these components. Compared with state-of-the-art methods, our method extracted much more branches while maintaining competitive overall segmentation performance. Codes and models are available at http://***/News/56. © 2020, CC BY.

关键词： Computerized tomography

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AIM 2020 Challenge on image Extreme Inpainting 16th

AIM 2020 Challenge on Image Extreme Inpainting

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Workshops held at the 16th European Conference on Computer Vision, ECCV 2020

作者： Ntavelis, Evangelos Romero, Andrés Bigdeli, Siavash Timofte, Radu Hui, Zheng Wang, Xiumei Gao, Xinbo Shin, Chajin Kim, Taeoh Son, Hanbin Lee, Sangyoun Li, Chao Li, Fu He, Dongliang Wen, Shilei Ding, Errui Bai, Mengmeng Li, Shuchen Zeng, Yu Lin, Zhe Yang, Jimei Zhang, Jianming Shechtman, Eli Lu, Huchuan Zeng, Weijian Ni, Haopeng Cai, Yiyang Li, Chenghua Xu, Dejia Wu, Haoning Han, Yu Nadim, Uddin S. M. Jang, Hae Woong Ahmed, Soikat Hasan Yoon, Jungmin Jung, Yong Ju Li, Chu-Tak Liu, Zhi-Song Wang, Li-Wen Siu, Wan-Chi Lun, Daniel P. K. Suin, Maitreya Purohit, Kuldeep Rajagopalan, A.N. Narang, Pratik Mandal, Murari Chauhan, Pranjal Singh Computer Vision Lab ETH Zürich Zürich Switzerland CSEM Neuchâtel Switzerland School of Electronic Engineering Xidian University Xi’an China Image and Video Pattern Recognition Laboratory School of Electrical and Electronic Engineering Yonsei University Seoul Korea Republic of Baidu Inc. Beijing China Beijing China Dalian University of Technology Dalian China Adobe San Jose United States Rensselaer Polytechnic Institute Troy United States Peking University Beijing China Lab Gachon University Seongnam Korea Republic of Centre for Multimedia Signal Processing Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hong Kong China Indian Institute of Technology Madras Chennai India BITS Pilani Pilani India MNIT Jaipur Jaipur India

ISBN: (纸本)9783030670696

This paper reviews the AIM 2020 challenge on extreme image inpainting. This report focuses on proposed solutions and results for two different tracks on extreme image inpainting: classical image inpainting and semantically guided image inpainting. The goal of track 1 is to inpaint large part of the image with no supervision. Similarly, the goal of track 2 is to inpaint the image by having access to the entire semantic segmentation map of the input. The challenge had 88 and 74 participants, respectively. 11 and 6 teams competed in the final phase of the challenge, respectively. This report gauges current solutions and set a benchmark for future extreme image inpainting methods. © 2020, Springer Nature Switzerland AG.

关键词： Semantic Segmentation

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Pulsar candidate selection using ensemble networks for FAST drift-scan survey

arXiv

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arXiv 2019年

作者： Wang, Hongfeng Zhu, Weiwei Guo, Ping Li, Di Feng, Sibo Yin, Qian Miao, Chenchen Tao, Zhenzhao Pan, Zhichen Wang, Pei Zheng, Xin Deng, Xiaodan Liu, Zhijie Xie, Xiaoyao Yu, Xuhong You, Shanping Zhang, Hui Image Processing and Pattern Recognition Laboratory College of Information Science and Technology Beijing Normal University Beijing100875 China CAS Key Laboratory of FAST Chinese Academy of Science Beijing100101 China School of Information Management Dezhou University Dezhou253023 China Image Processing and Pattern Recognition Laboratory School of Systems Science Beijing Normal University Beijing100875 China University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Information and Computing Science Guizhou Province Guizhou Normal University Guiyang550001 China School of Physics and Electronic Science Guizhou Normal University Guiyang550001 China

The Commensal Radio Astronomy Five-hundred-meter Aperture Spherical radio Telescope (FAST) Survey (CRAFTS) utilizes the novel drift-scan commensal survey mode of FAST and can generate billions of pulsar candidate signals. The human experts are not likely to thoroughly examine these signals, and various machine sorting methods are used to aid the classification of the FAST candidates. In this study, we propose a new ensemble classification system for pulsar candidates. This system denotes the further development of the pulsar image-based classification system (PICS), which was used in the Arecibo Telescope pulsar survey, and has been retrained and customized for the FAST drift-scan survey. In this study, we designed a residual network model comprising 15 layers to replace the convolutional neural networks (CNNs) in PICS. The results of this study demonstrate that the new model can sort >96% of real pulsars to belong the top 1% of all candidates and classify > 1.6 million candidates per day using a dual-GPU and 24-core computer. This increased speed and efficiency can help to facilitate real-time or quasi-real-time processing of the pulsar-search data stream obtained from CRAFTS. In addition, we have published the labeled FAST data used in this study online, which can aid in the development of new deep learning techniques for performing pulsar searches. Copyright © 2019, The Authors. All rights reserved.

关键词： Pulsars

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