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The Fourth International Symposium on image computing and Digital Medicine

作者： Junyi Yan Jinzhu Yang Wenjun Tan Peng Cao Lili Dou Dazhe Zhao School of Computer Science and Engineering Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education Northeastern University Shenyang China Neusoft Corporation Shenyang China

ISBN: (纸本)9781450389686

Brain structures segmentation in Magnetic Resonance Imaging (MRI) is a challenging task because of the large anatomical variability in both shape and size between individuals. In this work, an atlas based level set method is proposed for brain tissues segmentation. First, a skull stripping method is utilized to remove the skull region of brain. A registration algorithm is then applied to obtain the initial six brain structure labels (thalamus, hippocampus, amygdala, putamen, pallidum, caudate) with a manually labeled atlas, which can be used as the initial level set contours. The final label is achieved by minimizing the energy function which consists of three terms: an image data term, a length regularization term and an intensity constrained term. Experimental results demonstrate that our method can obtain satisfactory results compared with other state-of-art methods.

关键词： Segmentation Atlas Level set

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Source-Free Unsupervised Domain Adaptation Fundus image Segmentation via Entropy Optimization and Anatomical Priors

Source-Free Unsupervised Domain Adaptation Fundus Image Segm...

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International Conference on Biomimetic Intelligence and Robotics & medical Robotics Forum, ICBIR+MRF 2024

作者： Chen, Yijia Li, Jiapeng Yu, Haoze Qi, Lin Li, Yongchun College of Medicine and Biological Information Engineering Northeastern University Shenyang110169 China Engineering Research Center of Medical Imaging and Intelligent Analysis Ministry of Education Shenyang110169 China Key Laboratory of Medical Image Computing Ministry of Education Northeastern University Shenyang110169 China Shenyang Contain Electronic Technology Co. Ltd. Shenyang110167 China

This research focuses on fundus image segmentation within a source-free domain adaptation framework, where the availability of source images during the adaptation phase is limited due to privacy concerns. Although Source-Free Unsupervised Domain Adaptation (SFUDA) methods have seen significant innovative developments in recent years, they still face several challenges which include suboptimal performance due to substantial domain discrepancies, reliance on potentially noisy or inaccurate pseudo-labels during the adaptation process, and a lack of integration with domain-specific prior knowledge. To address these issues, this paper proposes a SFUDA framework via Entropy Optimization and Anatomical Priors (EOAPNet). To alleviate the influence of the divergence between the source and target domains, EOAPNet primarily evaluates the uncertainty (i.e., entropy) of predictions on target domain data and improves the model by focusing on high-entropy pixels or regions. Additionally, a weak-strong augmentation mean-teacher scheme is introduced in EOAPNet, which can enhance the accuracy of pseudo-labels and reduce error propagation. Thirdly, by integrating an anatomical knowledge-based class ratio prior into the overall loss function in the form of a Kullback-Leibler (KL) divergence, EOAPNet also incorporates expert domain knowledge. EOAPNet yields comparable results to several state-of-the-art adaptation techniques in experiments on two retinal image segmentation datasets involving the RIM-ONE-r3 and Drishti-GS datasets. © 2024 The Authors.

关键词： Domain Knowledge

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Hexagonal Convolutional Neural Network for Spatial Transcriptomics Classification

Hexagonal Convolutional Neural Network for Spatial Transcrip...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Gao, Jing Zhang, Fa Hu, Kai Cui, Xuefeng Xiangtan University Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan China Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Medical Technology Beijing Institute of Technology Beijing China Xiangnan University Key Laboratory of Medical Imaging and Artificial Intelligence of Hunan Province Chenzhou China Shandong University School of Computer Science and Technology Qingdao China

ISBN: (纸本)9781665468190

Recent advances in spatial transcriptomics have enabled the comprehensive measurement of transcriptional profiles while retaining the spatial contextual information. Identifying spatial domains is a critical step in the analysis of spatially resolved transcriptomics. Existing unsupervised methods perform poorly on this task owing to the large amount of noise and dropout events in the transcriptomic profiles. To address this problem, we first extend an unsupervised algorithm to a supervised learning method that can identify useful features and reduce noise hindrance. Second, inspired by the classical convolution in convolutional neural networks (CNNs), we designed a regular hexagonal convolution to compensate for the missing gene expression patterns from adjacent nodes. Compared with the graph convolution in graph neural networks (GNNs), our hexagonal convolution can preserve the relative spatial location information of different nodes in graph-structured data. Third, based on the hexagonal convolution, a novel hexagonal Convolutional Neural Network (hexCNN) is proposed for spatial transcriptomics classification. Finally, we compared the proposed hexCNN with existing methods on the DLPFC dataset. The results show that hexCNN achieves a classification accuracy of 87.2% and an average Rand index (ARI) of 78.2% (1.9% and 3.3% higher than those of GNNs). © 2022 IEEE.

关键词： Convolution

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The parametric design based on bee dual population evolution on chaos

The parametric design based on bee dual population evolution...

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2011 International Conference on Electrical, Information Engineering and Mechatronics, EIEM 2011

作者： Chun-Hong, Cao Chuan, Tang Da-Zhe, Zhao Bin, Zhang Collge of Information Science and Engineering Northeastern University Shenyang 110819 China State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Chengdu University of Technology Chengdu 610059 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Key Laboratory of Medical Image Computing of Ministry of Education Northeastern University Shenyang Lining 110819 China

ISBN: (纸本)9781447124665

Geometric constraint problem is equivalent to the problem of solving a set of nonlinear equations substantially. We propose a new optimization algorithm-Bee Dual Population Evolution on Chaos Algorithm and apply it into the geometric constraint solving. By the use of properties of ergodicity, randomicity and regularity of chaos motion, genetic algorithm of bee dual population evolution based on chaos is proposed. On the basis of genetic algorithm based on bee dual population evolution, the algorithm adopts chaos optimization to improve species' initial quality and utilizes the annealing chaotic mutation operator to replace the standard mutation operator in order to avoid the search being trapped in local optimum. The experiment shows that Bee Dual Population Evolution on Chaos has good capability in the geometric constraint solving. © 2012 Springer-Verlag London Limited.

关键词： Genetic algorithms

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Single-cell Long Non-coding RNA Landscape of T Cells in Human Cancer Immunity

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Genomics, Proteomics & Bioinformatics 2021年第3期19卷 377-393页

作者： Haitao Luo Dechao Bu Lijuan Shao Yang Li Liang Sun Ce Wang Jing Wang Wei Yang Xiaofei Yang Jun Dong Yi Zhao Furong Li Translational Medicine Collaborative Innovation Center The Second Clinical Medical College(Shenzhen People’s Hospital)Jinan UniversityShenzhen 518020China Shenzhen Key Laboratory of Stem Cell Research and Clinical Transformation Shenzhen 518020China Integrated Chinese and Western Medicine Postdoctoral Research Station Jinan UniversityGuangzhou 510632China Bioinformatics Research Group Key Laboratory of Intelligent Information ProcessingAdvanced Computing Research CenterInstitute of Computing TechnologyChinese Academy of SciencesBeijing 100190China Department of Gastrointestinal Surgery The Second Clinical Medical College(Shenzhen People’s Hospital)Jinan UniversityShenzhen 518020China

The development of new biomarkers or therapeutic targets for cancer immunotherapies requires deep understanding of *** date,the complete landscape and systematic characterization of long noncoding RNAs(lncRNAs)in T cells in cancer immunity are ***,by systematically analyzing full-length single-cell RNA sequencing(scRNA-seq)data of more than 20,000 libraries of T cells across three cancer types,we provided the first comprehensive catalog and the functional repertoires of lncRNAs in human T ***,we developed a custom pipeline for de novo transcriptome assembly and obtained a novel lncRNA catalog containing 9433 *** increased the number of current human lncRNA catalog by 16%and nearly doubled the number of lncRNAs expressed in T *** found that a portion of expressed genes in single T cells were lncRNAs which had been overlooked by the majority of previous *** on metacell maps constructed by the MetaCell algorithm that partitions scRNA-seq datasets into disjointed and homogenous groups of cells(metacells),154 signature lncRNA genes were *** were associated with effector,exhausted,and regulatory T cell ***,84 of them were functionally annotated based on the co-expression networks,indicating that lncRNAs might broadly participate in the regulation of T cell *** findings provide a new point of view and resource for investigating the mechanisms of T cell regulation in cancer immunity as well as for novel cancer-immune biomarker development and cancer immunotherapies.

关键词： Long non-coding RNA Transcriptome assembly Metacell Immune regulation Functional annotation

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Set pair three-way overlapping community discovery algorithm for weighted social internet of things

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Digital Communications and Networks 2023年第1期9卷 3-13页

作者： Chunying Zhang Jing Ren Lu Liu Shouyue Liu Xiaoqi Li Liya Wang College of Science North China University of Science and TechnologyTangshanHebeiChina Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes North China University of Science and TechnologyTangshanHebeiChina Hebei Key Laboratory of Data Science and Application North China University of Science and TechnologyTangshanHebeiChina The Key Laboratory of Engineering Computing in Tangshan City North China University of Science and TechnologyTangshanHebeiChina Tangshan Intelligent Industry and Image Processing Technology Innovation Center North China University of Science and TechnologyTangshanHebeiChina

There are many problems in Social Internet of Things(IoTs),such as complex topology information,different degree of association between nodes and overlapping *** idea of set pair information grain computing and clustering is introduced to solve the above problems so as to accurately describe the similarity between nodes and fully explore the multi-community structure.A Set Pair Three-Way Overlapping Community Discovery Algorithm for Weighted Social Internet of Things(WSIoT-SPTOCD)is *** the local network structure,which fully considers the topological information between nodes,the set pair connection degree is used to analyze the identity,difference and reverse of neighbor *** similarity degree of different neighbor nodes is defined from network edge weight and node degree,and the similarity measurement method of set pair between nodes based on the local information structure is *** to the number of nodes'neighbors and the connection degree of adjacent edges,the clustering intensity of nodes is defined,and an improved algorithm for initial value selection of k-means is *** nodes are allocated according to the set pair similarity between nodes and different ***-way community structures composed of a positive domain,boundary domain and negative domain are generated ***,the overlapping node set is generated according to the calculation results of community node ***,experiments are carried out on artificial networks and real *** results show that WSIoT-SPTOCD performs well in terms of standardized mutual information,overlapping community modularity and F1.

关键词： Social internet of things Set pair analysis K-means Local information structure Overlapping community

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A Single-Loop Accelerated Extra-Gradient Difference Algorithm with Improved Complexity Bounds for Constrained Minimax Optimization 37

A Single-Loop Accelerated Extra-Gradient Difference Algorith...

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37th Conference on Neural Information Processing Systems, NeurIPS 2023

作者： Liu, Yuanyuan Shang, Fanhua An, Weixin Liu, Junhao Liu, Hongying Lin, Zhouchen Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial Intelligence Xidian University China School of Computer Science and Technology College of Intelligence and Computing Tianjin University China Medical College Tianjin University China National Key Lab of General AI School of Intelligence Science and Technology Peking University China Institute for Artificial Intelligence Peking University China Peng Cheng Laboratory China

ISBN: (纸本)9781713899921

In this paper, we propose a novel extra-gradient difference acceleration algorithm for solving constrained nonconvex-nonconcave (NC-NC) minimax problems. In particular, we design a new extra-gradient difference step to obtain an important quasi-cocoercivity property, which plays a key role to significantly improve the convergence rate in the constrained NC-NC setting without additional structural assumption. Then momentum acceleration is also introduced into our dual accelerating update step. Moreover, we prove that, to find an Ε-stationary point of the function f, the proposed algorithm attains the complexity O(Ε-2) in the constrained NC-NC setting, while the best-known complexity bound is Oe(Ε-4), where Oe(·) hides logarithmic factors compared to O(·). As the special cases of the constrained NC-NC setting, the proposed algorithm can also obtain the same complexity O(Ε-2) for both the nonconvex-concave (NC-C) and convex-nonconcave (C-NC) cases, while the best-known complexity bounds are Oe(Ε-2.5) for the NC-C case and Oe(Ε-4) for the C-NC case. For fair comparison with existing algorithms, we also analyze the complexity bound to find Ε-stationary point of the primal function φ for the constrained NC-C problem, which shows that our algorithm can improve the complexity bound from Oe(Ε-3) to O(Ε-2). To the best of our knowledge, this is the first time that the proposed algorithm improves the best-known complexity bounds from Oe(Ε-4) and Oe(Ε-3) to O(Ε-2) in both the NC-NC and NC-C settings. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Constrained optimization

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Answering probabilistic top-k queries over P2P networks

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Jisuanji Xuebao/Chinese Journal of Computers 2011年第11期34卷 2155-2164页

作者： Sun, Yong-Jiao Yuan, Ye Wang, Guo-Ren Key Laboratory of Medical Image Computing of Ministry of Education Northeastern University Shenyang 110819 China College of Information Science and Engineering Northeastern University Shenyang 110819 China

Top-k queries in distributed databases have been studied widely in recent years. There exists an inherent uncertainty on the data objects due to imprecise measurements and network delays. In this paper, based on horizontally distributed data among peers, we propose an efficient approach of processing uncertain top-k queries in P2P networks. Firstly, we construct a distributed index using Quad-tree, and based on the index, propose a spatial pruning algorithm. Secondly, we propose the upper bound of top-k probabilistic according to the relationship between local top-k probabilities and global top-k probabilities. We also propose the lower bound of top-k probabilities according to the relationship between skyline probabilities and top-k probabilities. Using the two probabilistic pruning algorithms, we can further reduce computation costs and network overhead of top-k queries, and further reduce the number of candidate sets. Finally, we develop a sampling algorithm to estimate top-k probabilities of candidates. Extensive experiments are conducted to verify the effectiveness and efficiency of the proposed methods.

关键词： Uncertainty analysis

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Uncertainty-based active learning with instability estimation for text classification

ACM Transactions on Speech and Language Processing

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ACM Transactions on Speech and Language Processing 2012年第4期8卷 5-5页

作者： Zhu, Jingbo Ma, Matthew Key Laboratory of Medical Image Computing Ministry of Education Northeastern University China Scientific Works Princeton NJ United States

This article deals with pool-based active learning with uncertainty sampling. While existing uncertainty sampling methods emphasize selection of instances near the decision boundary to increase the likelihood of selecting informative examples, our position is that this heuristic is a surrogate for selecting examples for which the current learning algorithm iteration is likely to misclassify. To more directly model this intuition, this article augments such uncertainty sampling methods and proposes a simple instability-based selective sampling approach to improving uncertainty-based active learning, in which the instability degree of each unlabeled example is estimated during the learning process. Experiments on seven evaluation datasets show that instability-based sampling methods can achieve significant improvements over the traditional uncertainty sampling method. In terms of the average percentage of actively selected examples required for the learner to achieve 99% of its performance when training on the entire dataset, instability sampling and sampling by instability and density methods achieve better effectiveness in annotation cost reduction than random sampling and traditional entropy-based uncertainty sampling. Our experimental results have also shown that instability-based methods yield no significant improvement for active learning with SVMs when a popular sigmoidal function is used to transform SVM outputs to posterior probabilities. © 2012 ACM.

关键词： Stability

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Variational Clustering and Denoising of Spatial Transcriptomics

Variational Clustering and Denoising of Spatial Transcriptom...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Li, Cuiyuan Zhang, Fa Hu, Kai Cui, Xuefeng Shandong University School of Computer Science and Technology Qingdao China Beijing Institute of Technology School of Medical Technology Beijing China Xiangtan University Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan China Xiangnan University Key Laboratory of Medical Imaging and Artificial Intelligence of Hunan Province Chenzhou China

ISBN: (纸本)9798350337488

Spatial transcriptomics data provides a unique opportunity to investigate both gene expression and spatial structure in tissues at the same time. However, incorporating spatial information to accurately identify spatial domains is difficult due to factors such as high-dimensionality, sparsity, noise, and dropout events. To address these issues, we introduce vGraphST, a novel graph-based deep learning approach tailored for spatial transcriptomics data. Our method combines auto-encoder and contrastive learning techniques to process high-dimensional data and generate meaningful low-dimensional embeddings. Additionally, we use continuous distributions instead of discrete values in both the latent space and the denoised gene expression space. Specifically, Gaussian distributions are used to model the latent space, while zero-inflated Poisson distributions are used to model the denoised gene expression space. Experimental results demonstrate the effectiveness of vGraphST in accurately representing and analyzing spatial transcriptomics data. When compared to other methods using the DLPFC dataset, vGraphST achieves an average Adjusted Rand Index (ARI) of 0.58, demonstrating its superiority in segmenting spatial domains and recognizing biologically relevant spatiotemporal patterns. © 2023 IEEE.

关键词： clustering contrastive learning denoising spatial transcriptomics variational auto-encoder

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