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Content- and semantic-based progressive multimodal image retrieval

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ICIC Express Letters 2011年第1期5卷 201-206页

作者： Song, Haiyu Li, Xiongfei Wang, Pengjie Zhang, Xiaoli Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Jilin University Qianjin Avenue Changchun 130012 China Institute of Computer Graphics and Image Processing Dalian Nationalities University Economic and Technological Development Zone Liaohe Road Dalian 116600 China

Image retrieval has been an active research topic due to its great importance in the field of image management, digital library and web searching. However, the performances of the existing approaches, including text-based or content-based image retrieval, are often unsatisfactory. Inspired by text-based image retrieval, we propose semantic-based approach, whose similarity computation is semantic relevance rather than word matching. In this paper, a novel approach of progressive image retrieval based on content and semantic is proposed. The proposed retrieval model overcomes the drawbacks of pure content-based and text-based image retrieval, which exploits the strengths of both semantic- and content-based methods, minimizing their weaknesses. Experiments demonstrate the effectiveness and efficiency of the proposed method for image retrieval. © 2011 ISSN.

关键词： Semantics

Distantly Supervised Relation Extraction via Recursive Hierarchy-Interactive Attention and Entity-Order Perception

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arXiv 2021年

作者： Han, Ridong Peng, Tao Han, Jiayu Cui, Hai Liu, Lu College of Computer Science and Technology Jilin University Jilin Changchun130012 China College of Software Jilin University Jilin Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Jilin Changchun130012 China Department of Linguistics University of Washington SeattleWA98195 United States

Wrong-labeling problem and long-tail relations severely affect the performance of distantly supervised relation extraction task. Many studies mitigate the effect of wrong-labeling through selective attention mechanism and handle long-tail relations by introducing relation hierarchies to share knowledge. However, almost all existing studies ignore the fact that, in a sentence, the appearance order of two entities contributes to the understanding of its semantics. Furthermore, they only utilize each relation level of relation hierarchies separately, but do not exploit the heuristic effect between relation levels, i.e., higher-level relations can give useful information to the lower ones. Based on the above, in this paper, we design a novel Recursive Hierarchy-Interactive Attention network (RHIA) to further handle long-tail relations, which models the heuristic effect between relation levels. From the top down, it passes relation-related information layer by layer, which is the most significant difference from existing models, and generates relation-augmented sentence representations for each relation level in a recursive structure. Besides, we introduce a newfangled training objective, called Entity-Order Perception (EOP), to make the sentence encoder retain more entity appearance information. Substantial experiments on the popular New York Times (NYT) dataset are conducted. Compared to prior baselines, our RHIA-EOP achieves state-of-the-art performance in terms of precision-recall (P-R) curves, AUC, Top-N precision and other evaluation metrics. Insightful analysis also demonstrates the necessity and effectiveness of each component of RHIA-EOP. © 2021, CC BY.

关键词： Semantics

Document-level Relation Extraction with Relation Correlations

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arXiv 2022年

作者： Han, Ridong Peng, Tao Wang, Benyou Liu, Lu Wan, Xiang College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education China College of Software Jilin University China Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China School of Data Science The Chinese University of Hong Kong Shenzhen China

Document-level relation extraction faces two overlooked challenges: long-tail problem and multi-label problem. Previous work focuses mainly on obtaining better contextual representations for entity pairs, hardly address the above challenges. In this paper, we analyze the co-occurrence correlation of relations, and introduce it into DocRE task for the first time. We argue that the correlations can not only transfer knowledge between data-rich relations and data-scarce ones to assist in the training of tailed relations, but also reflect semantic distance guiding the classifier to identify semantically close relations for multi-label entity pairs. Specifically, we use relation embedding as a medium, and propose two co-occurrence prediction sub-tasks from both coarse- and fine-grained perspectives to capture relation correlations. Finally, the learned correlation-aware embeddings are used to guide the extraction of relational facts. Substantial experiments on two popular DocRE datasets are conducted, and our method achieves superior results compared to baselines. Insightful analysis also demonstrates the potential of relation correlations to address the above challenges. Copyright © 2022, The Authors. All rights reserved.

关键词： Extraction

Method for Preoperative Prediction of Microvascular Invasion of Hepatocellular Carcinoma

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Method for Preoperative Prediction of Microvascular Invasion...

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Chen Zhao Tian Bai Tongjia Chu Feng Wei Fa Zhang College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun China Hepatobiliary and Pancreas Department General Surgery Center First Hospital of Jilin University Changchun China School of Medical Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is of great guiding significance for the formulating treatment strategies and accessing the prognosis before the surgery. However, in traditional medicine, the gold standard for the diagnosis of MVI is obtained by examining pathological images which can only be obtained by sampling and sectioning tumors after surgery. At this time, MVI results have lost the timeliness of guiding tumor resection surgery. In order to solve this problem, existing studies began to use deep learning-based methods for preoperative prediction of MVI using non-invasive imaging. Most of these methods adopt the fusion methods of multi-sequence images to predict MVI, but fail to make full use of the characteristics of multiply sequences as prior knowledge to combine into the model, resulting in no further improvement of prediction performance. So we propose a multi-sequence image difference and correlation deep learning model. The model can extract the difference and correlation information between sequences from different scales and combine them into the model. To validate proposed model, we collected a data set consists of 120 HCC patients, including 50 MVI-positive patients. Compared with existing studies, our method has greatly improved in all evaluation metrics.

关键词： Deep learning Pathology Correlation Biological system modeling Surgery Predictive models Data mining

Multimodal Medical Image Feature Representation and Fusion for AD Early Diagnosis

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Multimodal Medical Image Feature Representation and Fusion f...

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jing Qu Ying Qu Xueliang Bai Lanxin Xu Han Wang Guixia Liu College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China College of Science North China University of Science and Technology Tangshan China School of Information Science and Technology Institute of Computational Biology Northeast Normal University Changchun China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Alzheimer's disease (AD) is progressive and gets worse with time. Early detection is an effective treatment for the disease, which can timely implement effective interventions. The multi-modal medical image contains more comprehensive and abundant information about the disease, which can reflect the different aspects of AD and reduce the potential bias in single-modal diagnosis. However, an effective and reliable solution is still lacking, which can integrate multimodal neuroimaging effectively for the precision diagnosis and treatment of AD. In this work, we proposed a feature representation and fusion model of MRI and PET scans based on a multiscale convolution network and cross-attention for AD early diagnosis, named camAD. Compared with the existing models, camAD performs better with fewer pre-processing steps. Using the Alzheimer’s disease neuroimaging initiative (ADNI) datasets, we demonstrated that integrating multi-modality data outperforms single-modality models in accuracy, specificity, sensitivity, AUC, and F1 scores. Our models exhibited significantly good accuracy and generality in four classification tasks, which provided a promising way to understand the underlying mechanisms of the disorder's progression.

关键词： Neuroimaging Accuracy Sensitivity Convolution Magnetic resonance imaging Feature extraction Data models Reliability Alzheimer's disease Medical diagnostic imaging

Hyperbolic node embedding for signed networks

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arXiv 2019年

作者： Song, Wenzhuo Chen, Hongxu Liu, Xueyan Jiang, Hongzhe Wang, Shengsheng College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China Advanced Analytics Institute School of Computer Science Faculty of Engineering and IT University of Technology Sydney NSW2007 Australia College of Mechanical and Electronic Engineering Nanjing Forestry University Nanjing210037 China

Signed network embedding methods aim to learn vector representations of nodes in signed networks. However, existing algorithms only managed to embed networks into low-dimensional Euclidean spaces whereas many intrinsic features of signed networks are reported more suitable for non-Euclidean spaces. For instance, previous works did not consider the hierarchical structures of networks, which is widely witnessed in real-world networks. In this work, we answer an open question that whether the hyperbolic space is a better choice to accommodate signed networks and learn embeddings that can preserve the corresponding special characteristics. We also propose a non-Euclidean signed network embedding method based on structural balance theory and Riemannian optimization, which embeds signed networks into a Poincar ball in a hyperbolic space. This space enables our approach to capture underlying hierarchy of nodes in signed networks because it can be seen as a continuous tree. We empirically compare our method against six Euclidean-based baselines in three tasks on seven real-world datasets, and the results show the effectiveness of our method. Copyright © 2019, The Authors. All rights reserved.

关键词： Geometry

Pathological brain detection based on online sequential extreme learning machine

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Pathological brain detection based on online sequential extr...

IEEE International Conference on Progress in Informatics and Computing (PIC)

作者： Siyuan Lu Hainan Wang Xueyan Wu Shuihua Wang School of Computer Science and Technology Nanjing Normal University Nanjing Jiangsu China State Key Lab of CAD & CG Zhejiang University Hangzhou Zhejiang China Key laboratory of symbolic computation and knowledge engineering of ministry of education Jilin University Changchun Jilin China Key Laboratory of Statistical information technology and data mining State Statistics Bureau Chengdu Sichuan China Department of Electrical Engineering CUNY New York NY USA

ISBN: (纸本)9781509034857

Magnetic resonance imaging (MRI) is a kind of imaging modality, which offers clearer images of soft tissues than computed tomography (CT). It is especially suitable for brain disease detection. It is beneficial to detect diseases automatically and accurately. We proposed a pathological brain detection method based on brain MR images and online sequential extreme learning machine. First, seven wavelet entropies (WE) were extracted from each brain MR image to form the feature vector. Then, an online sequential extreme learning machine (OS-ELM) was trained to differentiate pathological brains from the healthy controls. The experiment results over 132 brain MRIs showed that the proposed approach achieved a sensitivity of 93.51%, a specificity of 92.22%, and an overall accuracy of 93.33%, which suggested that our method is effective.

关键词： Entropy Discrete wavelet transforms Feature extraction Pathology Training Magnetic resonance imaging Diseases

AMPCliff: quantitative definition and benchmarking of activity cliffs in antimicrobial peptides

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arXiv 2024年

作者： Li, Kewei Wu, Yuqian Li, Yinheng Guo, Yutong Wang, Yan Liang, Yiyang Fan, Yusi Huang, Lan Zhang, Ruochi Zhou, Fengfeng College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Jilin Changchun130012 China School of Software Jilin University Jilin Changchun130012 China School of Life Sciences Jilin University Jilin Changchun130012 China Department of Computer Science Columbia University 116th and Broadway New York CityNY10027 United States School of Artificial Intelligence Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Jilin Changchun130012 China School of Biology and Engineering Guizhou Medical University Guizhou Guiyang550025 China School of Computer Engineering Changchun University of Engineering Jilin Changchun130103 China Changchun Wenli High School Jilin Changchun130062 China

Since the mechanism of action of drug molecules in the human body is difficult to reproduce in the in vitro environment, it becomes difficult to reveal the causes of the activity cliff phenomenon of drug molecules. We found out the AC of small molecules has been extensively investigated but limited knowledge is accumulated about the AC phenomenon in peptides with canonical amino acids. Understanding the mechanism of AC in canonical amino acids might help understand the one in drug molecules. This study introduces a quantitative definition and benchmarking framework AMPCliff for the AC phenomenon in antimicrobial peptides (AMPs) composed by canonical amino acids. A comprehensive analysis of the existing AMP dataset reveals a significant prevalence of AC within AMPs. AMPCliff quantifies the activities of AMPs by the MIC, and defines 0.9 as the minimum threshold for the normalized BLOSUM62 similarity score between a pair of aligned peptides with at least two-fold MIC changes. This study establishes a benchmark dataset of paired AMPs in Staphylococcus aureus from the publicly available AMP dataset GRAMPA, and conducts a rigorous procedure to evaluate various AMP AC prediction models, including nine machine learning, four deep learning algorithms, four masked language models, and four generative language models. Our analysis reveals that these models are capable of detecting AMP AC events and the pre-trained protein language model ESM2 demonstrates superior performance across the evaluations. The predictive performance of AMP activity cliffs remains to be further improved, considering that ESM2 with 33 layers only achieves the Spearman correlation coefficient 0.4669 for the regression task of the MIC values on the benchmark dataset. Source code and additional resources are available at https://***/supp/or https://***/Kewei2023/AMPCliff-generation. © 2024, CC BY-NC-SA.

关键词： Deep learning

Combining Immune with Ant Colony Algorithm for Geometric Constraint Solving

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Combining Immune with Ant Colony Algorithm for Geometric Con...

International Workshop on knowledge Discovery and Data Mining (WKDD)

作者： Hua Yuan Yi Li Wenhui Li Kong Zhao Duo Wang Rongqin Yi Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education College of Computer Science and Technology Jilin University Changchun China School of Computer Science & Engineering Changchun University of Technology China Suzhou Top Institute of Information Technology Suzhou China

Geometric constraint problem can be transformed to an optimization problem which the objective function and constraints are non-convex functions. In this paper an evolutionary algorithm based on ant colony optimization algorithm and the immune system model is proposed to provide solution to the geometric constraints problem. In the new algorithm, affinity calculation process and pheromone trail lying is embedded to maintain diversity and carry out the global search and the local search in many directions rather than one direction around the same individual simultaneously. This new algorithm different with current optimization methods in that it gets the good solution by excluding bad solutions. The experimental results reported here will shed more light into how affects the hybrid algorithm's search power in solving geometric constraint problem.

关键词： Immune system Ant colony optimization Nonlinear equations knowledge engineering Computer science educational technology Evolutionary computation Data mining Laboratories Computer science education