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DPP-Net: Difficulty Perception-Processing Heterogeneous Network for Semi-supervised Medical Image Segmentation

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DPP-Net: Difficulty Perception-Processing Heterogeneous Netw...

IEEE International Conference on Multimedia and Expo (ICME)

作者： Qiqin Lin Weixing Xie Rongzhou Zhou Xianpeng Cao Jingze Chen Junfeng Yao Qingqi Hong Center for Digital Media Computing School of Film Xiamen University Xiamen China National Institute for Data Science in Health and Medicine Xiamen University School of Informatics Xiamen University Xiamen China Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

In semi-supervised medical image segmentation, the scarcity of labeled data makes models prone to learning bias, causing persistent errors in certain regions and eventual over-fitting, significantly impacting segmentation performance. These problematic regions, termed difficult areas, are inadequately addressed by existing methods. To address this, We propose the Difficulty Perception-Processing Heterogeneous Network (DPP-Net). It guides the model in accurately perceiving and rectifying difficult areas, overcoming learning bias. Specifically, we introduce the Global Mutual Perception (GMP) to establish a comprehensive information perception channel between sample data, enabling a more holistic and accurate perception of difficult areas. The Difficulty-Aware Rectification (DAR) structure ensures continuous monitoring of difficult areas during training, allowing for timely adjustments to errors. Additionally, the Adaptive Competitive Pseudo-Label (ACP) Augmentation strategy enhances pseudo-labels through adaptive confidence competition. Experimental results on two different medical image databases (CT and MRI) demonstrate that our approach outperforms several state-of-the-art methods.

关键词： Training Image segmentation Accuracy Image databases Magnetic resonance imaging Computed tomography Heterogeneous networks data models Monitoring Biomedical imaging

SFF-DA: Sptialtemporal Feature Fusion for Detecting Anxiety Nonintrusively

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arXiv 2022年

作者： Mo, Haimiao Li, Yuchen Yang, Shanlin Zhang, Wei Ding, Shuai The School of Management Hefei University of Technology Anhui Hefei23009 China The Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education China The National Engineering Laboratory for Big Data Distribution and Exchange Technologies Shanghai200135 China The West China Hospital of Sichuan University Sichuan Chengdu610041 China

Early detection of anxiety is crucial for reducing the suffering of individuals with mental disorders and improving treatment outcomes. Utilizing an mHealth platform for anxiety screening can be particularly practical in improving screening efficiency and reducing costs. However, the effectiveness of existing methods has been hindered by differences in mobile devices used to capture subjects' physical and mental evaluations, as well as by the variability in data quality and small sample size problems encountered in real-world settings. To address these issues, we propose a framework with spatiotemporal feature fusion for detecting anxiety nonintrusively. We use a feature extraction network based on a 3D convolutional network and long short-term memory ("3DCNN+LSTM") to fuse the spatiotemporal features of facial behavior and noncontact physiology, which reduces the impact of uneven data quality. Additionally, we design a similarity assessment strategy to address the issue of deteriorating model accuracy due to small sample sizes. Our framework is validated with a crew dataset from the real world and two public datasets: the University of Burgundy Franche-Comté Psychophysiological (UBFC-Phys) dataset and the Smart Reasoning for Well-being at Home and at Work for Knowledge Work (SWELL-KW) dataset. The experimental results indicate that our framework outperforms the comparison methods. © 2022, CC BY-NC-SA.

关键词： mHealth

A Dynamic Fault Tree Based CBTC Onboard ATP System Safety Analysis Method*

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A Dynamic Fault Tree Based CBTC Onboard ATP System Safety An...

International Conference on intelligent Transportation

作者： Pengfei Gao Chao Liu Hairong Dong Wei Zheng National Research Centre of Railway Safety Assessment and the Beijing Key Laboratory of Intelligent Traffic Data Security and Privacy Protection Technology Beijing Jiaotong University Beijing China National Research Centre of Railway Safety Assessment Beijing Jiaotong University Beijing China State Key Lab of Rail Traffic Control & Safety Beijing Jiaotong University Beijing China

ISBN: (数字)9781728141497

ISBN: (纸本)9781728141503

To obtain refined safety requirements during system architecture design stage, the traditional static fault tree analysis mothed is widely used to analyse the logical relationship between basic hazardous events leading to system hazards in railway signal system, and to identify the weak and key equipment of the system. In order to accurately characterize the dynamic behaviours of the system and improve the accuracy and credibility of analysis results, a dynamic fault tree analysis mothed of railway signal system based on failure propagation modelling was proposed. According to failure logic modelling of dynamic failure behaviours, conforming to signal system function design, component failure model was established within SimFIA platform, and fault trees of system hazards were attained by model simulation. The result of Communication-Based Train Control (CBTC) System on-board equipment case study shows that the fault tree of complex system function are generated by the dynamic fault tree analysis mothed based on Failure Propagation and Transformation Notation (FPTN) modelling, which guaranteed the safety analysis outcomes was accurate and credible.

关键词： Safety Analytical models System analysis and design Fault trees Rail transportation Discrete Fourier transforms Systems architecture

Factors correlation mining on railway accidents using association rule learning algorithm

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Factors correlation mining on railway accidents using associ...

International Conference on intelligent Transportation

作者： Yakun Wang Wei Zheng Hairong Dong Pengfei Gao National Research Center of Railway Safety Assessment Beijing Jiaotong University Beijing China National Research Centre of Railway Safety Assessment and the Beijing Key Laboratory of Intelligent Traffic Data Security and Privacy Protection Technology Beijing Jiaotong University Beijing China State Key Lab of Rail Traffic Control & Safety Beijing Jiaotong University Beijing China

ISBN: (数字)9781728141497

ISBN: (纸本)9781728141503

Although much research work for the operation safety has been taken in the railway domain, some accidents still occur because past experiences of accident analysis were not fully accumulated for safety improvement. This study aims to identify potential causal relationships among the many factors playing a role in railway accidents. A new interestingness measure, Confidence_interestingness ("C_" Inter) and corresponding improved algorithm, Positive and Negative Association Rules Algorithm based on "C_" Inter (PNARA_CI) were put forward in our study. Compared with traditional association rule mining algorithms, the PNARA_CI does not generate candidate association rules by means of frequent itemsets, but by the combination between every two accident factors, which can mine the positive and negative association rules with practical value to the maximum. And they were applied to railway accidents data to explore the association rules of the causal factors in the case study. The effectiveness of the algorithm was verified.

关键词： Railway accidents data mining Rail transportation Itemsets Safety databases Task analysis

Stream Concept-cognitive Computing System for Streaming data Learning

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TechRxiv

TechRxiv 2023年

作者： Mi, Yunlong Liu, Wenqi Shi, Yong The Big Data and Intelligent Decision Making Research Center Central South University Changsha410083 China The Data Science Research Center Kunming University of Science and Technology Kunming650500 China The Faculty of Science Kunming University of Science and Technology Kunming650500 China The School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China The Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China

People can often acquire knowledge dynamically and rapidly from different types of data, yet existing incremental learning algorithms are still computationally time consuming and most of stream learning methods are mainly designed for streaming data while ignoring other types of data. Hence, this paper proposes a novel dynamic concept learning (CL) algorithm by imitating human cognitive learning processes from the perspective of brain logical cognition, which is named stream concept-cognitive computing system (streamC3S). For streamC3S, it mainly consists of three aspects: the concept space, CL process, and model update process. Moreover, considering the concept drift frequently occurs in the streaming data over time, an extended version of streamC3S (namely, streamC3SE) is also proposed in this work. Specifically, we first show the related theories for streamC3S and streamC3SE on the basis of the concept space. Then an overall framework and its corresponding algorithm are shown. Finally, experimental results on various types of datasets, including the standard machine learning datasets, streaming datasets, image datasets, and two traffic data streams, validate the effectiveness of our streamC3S and streamC3SE compared to the state-of-the-art incremental learning and stream learning algorithms. © 2023, CC BY.

关键词： Learning algorithms

Global context-aware progressive aggregation network for salient object detection

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arXiv 2020年

作者： Chen, Zuyao Xu, Qianqian Cong, Runmin Huang, Qingming University of Chinese Academy of Sciences Beijing China Key Lab. of Intelligent Information Processing ICT CAS Beijing China Institute of Information Science Beijing Jiaotong University Beijing China Key Lab. of Big Data Mining and Knowledge Management CAS Beijing China Peng Cheng Laboratory Shenzhen Guangdong China

Deep convolutional neural networks have achieved competitive performance in salient object detection, in which how to learn effective and comprehensive features plays a critical role. Most of the previous works mainly adopted multiplelevel feature integration yet ignored the gap between different features. Besides, there also exists a dilution process of high-level features as they passed on the top-down pathway. To remedy these issues, we propose a novel network named GCPANet to effectively integrate low-level appearance features, high-level semantic features, and global context features through some progressive context-aware Feature Interweaved Aggregation (FIA) modules and generate the saliency map in a supervised way. Moreover, a Head Attention (HA) module is used to reduce information redundancy and enhance the top layers features by leveraging the spatial and channel-wise attention, and the Self Refinement (SR) module is utilized to further refine and heighten the input features. Furthermore, we design the Global Context Flow (GCF) module to generate the global context information at different stages, which aims to learn the relationship among different salient regions and alleviate the dilution effect of high-level features. Experimental results on six benchmark datasets demonstrate that the proposed approach outperforms the state-of-the-art methods both quantitatively and qualitatively. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

Privacy-preserving medical treatment system through nondeterministic finite automata

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arXiv 2020年

作者： Yang, Yang Deng, Robert H. Liu, Ximeng Wu, Yongdong Weng, Jian Zheng, Xianghan Rong, Chunming School of Information Systems Singapore Management University Singapore College of Mathematics and Computer Science Fuzhou University Fujian China State Key Laboratory of Integrated Services Networks Xidian University Guangdong Provincial Key Laboratory of Data Security and Privacy Protection Guangzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China School of Information Systems Singapore Management University Singapore Singapore Department of Computer Science Jinan University Guangdong China College of Mathematics and Computer Science Fuzhou University Fujian China Department of Electronic Engineering and Computer Science University of Stavanger Norway MingByte Technology Qingdao China

In this paper, we propose a privacy-preserving medical treatment system using nondeterministic finite automata (NFA), hereafter referred to as P-Med, designed for the remote medical environment. P-Med makes use of the nondeterministic transition characteristic of NFA to flexibly represent the medical model, which includes illness states, treatment methods and state transitions caused by exerting different treatment methods. A medical model is encrypted and outsourced to the cloud to deliver telemedicine services. Using P-Med, patient-centric diagnosis and treatment can be made on-the-fly while protecting the confidentiality of a patient's illness states and treatment recommendation results. Moreover, a new privacy-preserving NFA evaluation method is given in P-Med to get a confidential match result for the evaluation of an encrypted NFA and an encrypted data set, which avoids the cumbersome inner state transition determination. We demonstrate that P-Med realizes treatment procedure recommendation without privacy leakage to unauthorized parties. We conduct extensive experiments and analyses to evaluate efficiency. Copyright © 2020, The Authors. All rights reserved.

关键词： Diseases

Towards Unified Token Learning for Vision-Language Tracking

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arXiv 2023年

作者： Zheng, Yaozong Zhong, Bineng Liang, Qihua Li, Guorong Ji, Rongrong Li, Xianxian The Key Laboratory of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin541004 China The Guangxi Key Laboratory of Multi-Source Information Mining and Security Guangxi Normal University Guilin541004 China The School of Computer Science and Technology Key Laboratory of Big Data Mining and Knowledge Management University of Chinese Academy of Sciences Beijing100049 China Media Analytics and Computing Lab Department of Artificial Intelligence School of Informatics Xiamen University 361005 China

In this paper, we present a simple, flexible and effective vision-language (VL) tracking pipeline, termed MMTrack, which casts VL tracking as a token generation task. Traditional paradigms address VL tracking task indirectly with sophisticated prior designs, making them over-specialize on the features of specific architectures or mechanisms. In contrast, our proposed framework serializes language description and bounding box into a sequence of discrete tokens. In this new design paradigm, all token queries are required to perceive the desired target and directly predict spatial coordinates of the target in an auto-regressive manner. The design without other prior modules avoids multiple sub-tasks learning and hand-designed loss functions, significantly reducing the complexity of VL tracking modeling and allowing our tracker to use a simple cross-entropy loss as unified optimization objective for VL tracking task. Extensive experiments on TNL2K, LaSOT, LaSOText and OTB99-Lang benchmarks show that our approach achieves promising results, compared to other state-of-the-arts. Copyright © 2023, The Authors. All rights reserved.

关键词： Modeling languages

iSplit LBI: Individualized partial ranking with ties via split LBI

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arXiv 2019年

作者： Xu, Qianqian Sun, Xinwei Yang, Zhiyong Cao, Xiaochun Huang, Qingming Yao, Yuan Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS Microsoft Research Asia State Key Laboratory of Information Security Institute of Information Engineering CAS School of Cyber Security University of Chinese Academy of Sciences School of Computer Science and Tech. University of Chinese Academy of Sciences Key Laboratory of Big Data Mining and Knowledge Management CAS Peng Cheng Laboratory Department of Mathematics Hong Kong University of Science and Technology Hong Kong

Due to the inherent uncertainty of data, the problem of predicting partial ranking from pairwise comparison data with ties has attracted increasing interest in recent years. However, in real-world scenarios, different individuals often hold distinct preferences. It might be misleading to merely look at a global partial ranking while ignoring personal diversity. In this paper, instead of learning a global ranking which is agreed with the consensus, we pursue the tie-aware partial ranking from an individualized perspective. Particularly, we formulate a unified framework which not only can be used for individualized partial ranking prediction, but also be helpful for abnormal user selection. This is realized by a variable splitting-based algorithm called iSplitLBI. Specifically, our algorithm generates a sequence of estimations with a regularization path, where both the hyperparameters and model parameters are updated. At each step of the path, the parameters can be decomposed into three orthogonal parts, namely, abnormal signals, personalized signals and random noise. The abnormal signals can serve the purpose of abnormal user selection, while the abnormal signals and personalized signals together are mainly responsible for individual partial ranking prediction. Extensive experiments on simulated and real-world datasets demonstrate that our new approach significantly outperforms state-of-the-art alternatives. The code is now availiable at https://***/qianqianxu010/ NeurIPS2019-iSplitLBI. Copyright © 2019, The Authors. All rights reserved.

关键词： Forecasting