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Pancreatic cancer biomarker detection using recursive feature elimination based on Support Vector Machine and large margin distribution machine

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Pancreatic cancer biomarker detection using recursive featur...

International Conference on Systems and Informatics (ICSAI)

作者： Yidan Lv Yan Wang Yongfei Tan Wei Du Keke Liu Hao Wang Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China Basic Medicine School Jilin University Changchun China Department of Hepatopancreatobiliary Surgery and Second Affiliated Hospital Harbin Medical University Harbin China

Pancreatic cancer acts as one of the leading causes of cancer-connective deaths. Its five-year overall survival rate being reported is about 7.7% from 2006 to 2012 by the National Cancer Institute. One of the main causes for its poor prognosis is because its non-typical symptoms make early diagnosis very challenging. Therefore, a predominant strategy for early accurate detection and prognostication on pancreatic cancer is vital to the whole course of comprehensive therapy. In this research, we proposed a method which combined Recursive Feature Elimination (RFE) method based on Support Vector Machine (SVM) and Large Margin Distribution Machine (LDM) to identify potential biomarkers for pancreatic cancer. In our experiments, we have strengthened the process of RFE to achieve better performance. The dataset GSE15471 we adopted are from GEO database with 39 pairs of pancreatic ductal carcinoma and adjacent control pancreatic tissues. Through experiments, a panel of twelve genes was identified as biomarkers in pancreatic cancer with 91.28% classification accuracy. The universality of the candidate genes was examined on another dataset GSE28735 and the classification accuracy was higher than 80%. In addition, by using the SVM, LDM and BP classifiers, we compared the ordered feature sets generated by our proposed method with T-test, SVM-RFE and LDM-RFE, and the results indicated our proposed method obtained higher average classification accuracy.

关键词： Cancer Support vector machines Feature extraction Tumors Training data Testing

Joint-Motion Mutual Learning for Pose Estimation in Videos

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arXiv 2024年

作者： Wu, Sifan Chen, Haipeng Yin, Yifang Hu, Sihao Feng, Runyang Jiao, Yingying Yang, Ziqi Liu, Zhenguang College of Computer Science and Technology Jilin University Changchun China A*STAR Singapore Georgia Institute of Technology Atlanta United States School of Artificial Intelligence Jilin University Changchun China The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun China Institute of Blockchain and Data Security Hangzhou China

Human pose estimation in videos has long been a compelling yet challenging task within the realm of computer vision. Nevertheless, this task remains difficult because of the complex video scenes, such as video defocus and self-occlusion. Recent methods strive to integrate multi-frame visual features generated by a backbone network for pose estimation. However, they often ignore the useful joint information encoded in the initial heatmap, which is a byproduct of the backbone generation. Comparatively, methods that attempt to refine the initial heatmap fail to consider any spatiotemporal motion features. As a result, the performance of existing methods for pose estimation falls short due to the lack of ability to leverage both local joint (heatmap) information and global motion (feature) dynamics. To address this problem, we propose a novel joint-motion mutual learning framework for pose estimation, which effectively concentrates on both local joint dependency and global pixel-level motion dynamics. Specifically, we introduce a context-aware joint learner that adaptively leverages initial heatmaps and motion flow to retrieve robust local joint feature. Given that local joint feature and global motion flow are complementary, we further propose a progressive joint-motion mutual learning that synergistically exchanges information and interactively learns between joint feature and motion flow to improve the capability of the model. More importantly, to capture more diverse joint and motion cues, we theoretically analyze and propose an information orthogonality objective to avoid learning redundant information from multi-cues. Empirical experiments show our method outperforms prior arts on three challenging benchmarks. Copyright © 2024, The Authors. All rights reserved.

关键词： Motion estimation

Game Theoretic Approaches in Vehicular Networks: A Survey

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arXiv 2020年

作者： Sun, Zemin Liu, Yanheng Wang, Jian Anil, Carie Cao, Dongpu College of Computer Science and Technology Key Laboratory of Symbolic Computation Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China Engineering VIT-AP AmaravatiAndhra Pradesh522237 India College of Engineering Nanjing Agricultural University Nanjing210031 China Waterloo Cognitive Autonomous Driving Lab University of Waterloo N2L 3G1 Canada

—In the era of the Internet of Things (IoT), vehicles and other intelligent components in Intelligent Transportation System (ITS) are connected, forming the Vehicular Networks (VNs) that provide efficient and secure traffic, ubiquitous access to information, and various applications. However, as the number of connected nodes keeps increasing, it is challenging to satisfy various and large amounts of service requests with different Quality of Service (QoS) and security requirements in the highly dynamic VNs. Intelligent nodes in VNs can compete or cooperate for limited network resources so that either an individual or a group objectives can be achieved. Game theory, a theoretical framework designed for strategic interactions among rational decision-makers who faced with scarce resources, can be used to model and analyze individual or group behaviors of communication entities in VNs. This paper primarily surveys the recent advantages of GT used in solving various challenges in VNs. As VNs and GT have been extensively investigate34d, this survey starts with a brief introduction of the basic concept and classification of GT used in VNs. Then, a comprehensive review of applications of GT in VNs is presented, which primarily covers the aspects of QoS and security. Moreover, with the development of fifth-generation (5G) wireless communication, recent contributions of GT to diverse emerging technologies of 5G integrated into VNs are surveyed in this paper. Finally, several key research challenges and possible solutions for applying GT in VNs are outlined. Copyright © 2020, The Authors. All rights reserved.

关键词： Game theory

BAENET: A Brain Age Estimation Network with 3D Skipping and Outlier Constraint Loss

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BAENET: A Brain Age Estimation Network with 3D Skipping and ...

IEEE International Symposium on Biomedical Imaging

作者： Taiping Qu Yangming Yue Qirui Zhang Cheng Wang Zhiqiang Zhang Guangming Lu Wei Du Xiuli Li Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education College of Computer Science and Technology Jilin University Changchun China Deepwise AI Lab Beijing China Jinling Hospital Medical School of Nanjing University Nanjing Jiangsu China

ISBN: (数字)9781538693308

ISBN: (纸本)9781538693315

The potential pattern changes in brain micro-structure can be used for the brain development assessment in children and adolescents by MRI scans. In this paper, we propose a highly accurate and efficient end-to-end brain age estimation network (BAENET) on T1-weighted MRI images. On the network, 3D skipping and outlier constraint loss are designed to accommodate deeper network and increase the robustness. Besides, we incorporate the neuroimaging domain knowledge into stratified sampling for better generalization ability for datasets with different age distributions, and gender learning for more gender-specific features during modeling. We verify the effectiveness of the proposed method on the public ABIDE2 and ADHD200 benchmark, consisting of 382 and 378 normal children scans respectively. Our BAENET achieves MAE of 1.11 and 1.16, significantly outperforming the best reported methods by 5.1 % and 9.4%.

关键词： Three-dimensional displays Brain modeling Estimation Solid modeling Feature extraction Magnetic resonance imaging Task analysis

SSBM: A Signed Stochastic Block Model for Multiple Structure Discovery in Large-Scale Exploratory Signed Networks

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arXiv 2023年

作者： Li, Yang Yang, Bo Zhao, Xuehua Yang, Zhejian Chen, Hechang Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun130012 China College of Computer Science and Technology Jilin University Changchun130012 China Aviation University of Air Force Changchun130062 China School of Digital Media Shenzhen Institute of Information Technology Shenzhen518055 China School of Artificial Intelligence Jilin University Changchun130012 China

Signed network structure discovery has received extensive attention and has become a research focus in the field of network science. However, most of the existing studies are focused on the networks with a single structure, e.g., community or bipartite, while ignoring multiple structures, e.g., the coexistence of community and bipartite structures. Furthermore, existing studies were faced with challenge regarding large-scale signed networks due to their high time complexity, especially when determining the number of clusters in the observed network without any prior knowledge. In view of this, we propose a mathematically principled method for signed network multiple structure discovery named the Signed Stochastic Block Model (SSBM). The SSBM can capture the multiple structures contained in signed networks, e.g., community, bipartite, and coexistence of them, by adopting a probabilistic model. Moreover, by integrating the minimum message length (MML) criterion and component-wise EM (CEM) algorithm, a scalable learning algorithm that has the ability of model selection is proposed to handle large-scale signed networks. By comparing state-of-the-art methods on synthetic and realworld signed networks, extensive experimental results demonstrate the effectiveness and efficiency of SSBM in discovering large-scale exploratory signed networks with multiple structures. Copyright © 2023, The Authors. All rights reserved.

关键词： Stochastic models

M3oE: Multi-Domain Multi-Task Mixture-of-Experts Recommendation Framework

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arXiv 2024年

作者： Zhang, Zijian Cai, Qingpeng Liu, Ziru Hu, Lantao Liu, Shuchang Zhao, Xiangyu Liu, Qidong Jiang, Peng Yu, Jiaao Zhang, Chunxu Zhao, Hongwei Gai, Kun Jilin University City University of Hong Kong Jilin China Kuaishou Technology Beijing China City University of Hong Kong Hong Kong Xi’an Jiaotong University City University of Hong Kong Xi’an China Jilin University Jilin China Beijing China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China

Multi-domain recommendation and multi-task recommendation have demonstrated their effectiveness in leveraging common information from different domains and objectives for comprehensive user modeling. Nonetheless, the practical recommendation usually faces multiple domains and tasks simultaneously, which cannot be well-addressed by current methods. To this end, we introduce M3oE, an adaptive Multi-domain Multi-task Mixture-of-Experts recommendation framework. M3oE integrates multi-domain information, maps knowledge across domains and tasks, and optimizes multiple objectives. We leverage three mixture-of-experts modules to learn common, domain-aspect, and task-aspect user preferences respectively to address the complex dependencies among multiple domains and tasks in a disentangled manner. Additionally, we design a two-level fusion mechanism for precise control over feature extraction and fusion across diverse domains and tasks. The framework’s adaptability is further enhanced by applying AutoML technique, which allows dynamic structure optimization. To the best of the authors’ knowledge, our M3oE is the first effort to solve multi-domain multi-task recommendation self-adaptively. Extensive experiments on two benchmark datasets against diverse baselines demonstrate M3oE’s superior performance. The implementation code is available to ensure reproducibility1 Copyright © 2024, The Authors. All rights reserved.

关键词： Recommender systems

GPFedRec: Graph-Guided Personalization for Federated Recommendation

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arXiv 2023年

作者： Zhang, Chunxu Long, Guodong Zhou, Tianyi Zhang, Zijian Yan, Peng Yang, Bo College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China Australian Artificial Intelligence Institute FEIT University of Technology Sydney Sydney Australia UMIACS University of Maryland Maryland United States College of Computer Science and Technology Jilin University City University of Hong Kong China

The federated recommendation system is an emerging AI service architecture that provides recommendation services in a privacy-preserving manner. Using user-relation graphs to enhance federated recommendations is a promising topic. However, it is still an open challenge to construct the user-relation graph while preserving data locality-based privacy protection in federated settings. Inspired by a simple motivation, similar users share a similar vision (embeddings) to the same item set, this paper proposes a novel Graph-guided Personalization for Federated Recommendation (GPFedRec). The proposed method constructs a user-relation graph from user-specific personalized item embeddings at the server without accessing the users' interaction records. The personalized item embedding is locally fine-tuned on each device, and then a user-relation graph will be constructed by measuring the similarity among client-specific item embeddings. Without accessing users' historical interactions, we embody the data locality-based privacy protection of vanilla federated learning. Furthermore, a graph-guided aggregation mechanism is designed to leverage the user-relation graph and federated optimization framework simultaneously. Extensive experiments on five benchmark datasets demonstrate GPFedRec's superior performance. The in-depth study validates that GPFedRec can generally improve existing federated recommendation methods as a plugin while keeping user privacy safe. Code is available to ease reproducibility. Copyright © 2023, The Authors. All rights reserved.

关键词： Recommender systems

Improved cross-media relevance model for automatic image annotation

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ICIC Express Letters, Part B: Applications 2012年第5期3卷 1231-1237页

作者： Song, Haiyu Li, Xiongfei Wang, Pengjie Bao, Cuizhu Liu, Yongkui Xu, Lu Institute of Computer Graphics and Image Processing Dalian Nationalities University No. 18 Liaohe Road Economic and Technological Development Zone-Dalian 116600 China Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education Jilin University No. 2699 Qianjin Avenue Changchun 130012 China

Automatic image annotation has been an active research topic in the last decade due to its potentially large impact on image retrieval, object recognition and image understanding. Many approaches have been proposed for image annotation in recent years. The most influential annotation model is relevance model, whose representative algorithm is Cross-Media Relevance Model (CMRM). In this paper, to overcome the shortcomings of the original CMRM, we propose improved CMRM. In the improved CMRM, the annotation word distribution is estimated using binary probability, the significance of blob is discriminately depicted using TF-IDF (term frequency-inverse document frequency) schemes, and the similarity measurement of blob is based on blob and continuousvalued feature vector. The experimental results on the standard Corel5K dataset show that our proposed approach outperforms the original CMRM by about 111% and 73% in terms of F1 and N+ measures respectively. The improved CMRM can get better performance in specific domains such as cultural heritage and special necessities digitization for ethnic minorities. © 2012 ICIC International.

关键词： Image retrieval

Automatic Detection of Lumbar Spondylolisthesis and Instability Using Supine Magnetic Resonance Images

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SSRN

SSRN 2023年

作者： Liu, Zewen Zhang, Han Zhao, Hang Liu, Yuwu Zhang, Chi Shi, Xiaohu College of Software Jilin University Changchun130021 China Guangyuan Central Hospital China College of Computer Science and Technology Jilin University Changchun130021 China Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education Changchun130021 China

Background and Objective: Lumbar spondylolisthesis is a condition in which one vertebra is displaced relative to the vertebra below it. Lumbar instability can be divided into static and dynamic spondylolisthesis depending on whether the degree of slippage changes significantly in images taken in different postures. Usually, the degree of slippage of lumbar instability is not obvious on magnetic resonance (MR) images taken in the supine position;therefore, it is easy to miss the detection of spondylolisthesis with lumbar instability based on slippage. This feature of lumbar instability also poses a challenge for the automatic detection of dynamic spondylolisthesis, particularly in supine MR images. This study aimed to develop a two-stage model for the automatic detection of spondylolisthesis and lumbar instability using supine MR ***: Specifically, we used You Only Look Once (YOLO) to detect and crop consecutive images near the intervertebral discs (IVDs) on both sagittal and transverse axis magnetic resonance images, and then classified the cropped images using weight-sharing convolutional neural networks (CNNs). Consistency regularization and a new contrast loss function ware also developed to improve the robustness. The MR data we used includes 108 mixed samples of lumbar instability and spondylolisthesis (39 cases of lumbar instability and 69 cases of spondylolisthesis) and 182 normal ***: We found that YOLO could detect the complete image sequence between L1 and S1 with 97.24% accuracy (ACC). Compared with the ground truth labels, the mean of intersection over union (mIOU) is 84.58%. In addition, the ACC and mIoU for transverse axis image are 98.62% and 85.71% respectively. In the classification stage, CNN achieved a maximum ACC of 97.9% with transverse axis image as inputs and using consistency ***: While MR images offer a wider range of applications in disease detection compared to X-rays, the identification of con

关键词： Stability