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Semi-Supervised Medical Image Segmentation Based on Frequency Domain Aware Stable Consistency Regularization

Journal of imaging informatics in medicine 2025年 2025 Jan 22页

作者： Yihao Ouyang Peipei Li Haixiang Zhang Xuegang Hu Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology Hefei 230009 Anhui China. School of Computer Science and Information Engineering Hefei University of Technology Hefei 230009 Anhui China. School of Computer Science and Information Engineering Hefei University of Technology Hefei 230009 Anhui China. peipeili@***. Center for Big Data and Population Health of IHM Anhui Medical University Hefei Anhui China. peipeili@***. Center for Big Data and Population Health of IHM Anhui Medical University Hefei Anhui China. Computer Centre The Second People's Hospital of Hefei Hefei 230011 Anhui China. Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei University of Technology Hefei 230009 Anhui China.

With the advancement of deep learning models nowadays, they have successfully applied in the semi-supervised medical image segmentation where there are few annotated medical images and a large number of unlabeled ones. A representative approach in this regard is the semi-supervised method based on consistency regularization, which improves model training by imposing consistency constraints (perturbations) on unlabeled data. However, the perturbations in this kind of methods are often artificially designed, which may introduce biases unfavorable to the model learning in the handling of medical image segmentation. On the other hand, the majority of such methods often overlook the supervision in the Encoder stage of training and primarily focus on the outcomes in the later stages, potentially leading to chaotic learning in the initial phase and subsequently impacting the learning process of the model in the later stages. At the meanwhile, they miss the intrinsic spatial-frequency information of the images. Therefore, in this study, we propose a new semi-supervised medical image segmentation approach based on frequency domain aware stable consistency regularization. Specifically, to avoid the bias introduced by artificially setting perturbations, we first utilize the inherent frequency domain information of images, including both high and low frequencies, as the consistency constraint. Secondly, we incorporate supervision in the Encoder stage of model training to ensure that the model does not fail to learn due to the disruption of the original feature space caused by strong augmentation. Finally, extensive experimentation validates the effectiveness of our semi-supervised approach.

关键词： Consistency regularization Frequency domain Medical image segmentation Semi-supervision

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DualConvNet: Enhancing CNN Inference Efficiency Through Compressed Convolutions and Reparameterization

DualConvNet: Enhancing CNN Inference Efficiency Through Comp...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Haipeng Du Muyan Jiao Jiageng Zhang Xin Lv Jie Zhang School of Computer Science and Technology Xi’an Jiaotong University Xi’an China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University Xi’an China School of Continuing Education Xi’an Jiaotong University Xi’an China Network Information Center Xi’an Jiaotong University Xi’an China School of Computer Science and Engineering Xi’an University of Technology Xi’an China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

Convolutional Neural Networks (CNNs) have significantly advanced computer vision tasks, but their increasing complexity poses challenges for efficient inference, particularly on resource-constrained devices. We present DualConvNet, a novel CNN architecture that enhances inference efficiency through two key innovations: compressed convolutions and reparameterization. Compressed convolutions reduce computational complexity by selectively processing input channel subsets during both training and inference. For inference, we introduce a reparameterization technique that merges the multi-branch structure into a single, efficient operation, significantly improving speed. Experiments on CIFAR-10, CIFAR-100, and ImageNet-1k demonstrate DualConvNet’s effectiveness, consistently outperforming state-of-the-art models in both accuracy and inference speed. On the COCO dataset, DualConvNet shows competitive accuracy in object detection and instance segmentation tasks while substantially reducing GPU latency. Ablation studies validate the impact of our dual-strategy approach, revealing significant improvements in both accuracy and computational efficiency compared to alternative designs. These results demonstrate DualConvNet’s effectiveness in improving inference efficiency while maintaining high accuracy across various tasks and datasets, making it particularly suitable for real-time applications in resource-constrained scenarios.

关键词： Training Technological innovation Accuracy Image coding Computational modeling Computer architecture Transformers Computational efficiency Convolutional neural networks Computational complexity

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The K-Step H Indices in Citation Networks at the Paper, Author, and Institution Levels

SSRN

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SSRN 2023年

作者： Yang, Alex Jie Wu, Linwei Zhang, Qi Wang, Hao Deng, Sanhong School of Information Management Nanjing University 22 Hankou Rd. Nanjing210093 China Jiangsu Key Laboratory of Data Engineering and Knowledge Service Nanjing University Nanjing210093 China International Joint Informatics Laboratory Nanjing University China

From the viewpoint of high-generation citation networks, h indices can be extended to global metrics at both basic and aggregated levels. To accomplish this, three types of networks are designed: the paper-paper network, the author-paper-paper network, and the institution-paper-paper network. The k-step h index is then proposed at the level of papers, authors, and institutions, respectively. Through a comprehensive empirical study based on the APS dataset, it is found that the k-step h index extends the depth and dimensionality of the Hirsh core, allowing for more global information. The consistency and convergent validity of the k-step h indices are quantitatively assessed. The results indicate that the k-step h index demonstrates a high degree of consistency and focuses on the potential for further research, and is capable of measuring the long-term reputation rather than short-term popularity. The convergent validity of the k-step h indices is rigorously tested using benchmark examples such as Nobel laureates and their publications, and they demonstrate superior performance in identifying seminal papers and prominent scientists compared to traditional metrics at the paper and author levels. The k-step h index also excels in ranking institutions, offering a promising new approach for evaluating the impact and influence of scientific entities at both the basic and aggregate levels. This opens up exciting avenues for future research. © 2023, The Authors. All rights reserved.

关键词： Benchmarking

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Smart Contract Vulnerability Detection Based on Semantic Graph and Residual Graph Convolutional Networks with Edge Attention

SSRN

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SSRN 2022年

作者： Chen, Da Feng, Lin Fan, Yuqi Shang, Siyuan Wei, Zhenchun The Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education China School of Computer Science and Information Engineering Hefei University of Technology Anhui Hefei230009 China

It is imperative to assure the security of smart contracts via intelligent vulnerability detection tools before deploying smart contracts on blockchains. The existing deep learning-based approaches fail to effectively capture the rich syntax and semantic information embedded in smart contracts. In this paper, we detect smart contract vulnerabilities at the function level by constructing a novel semantic graph (SG) for each function and learning the SGs using a new graph convolutional network EA-RGCN. Our proposed method consists of three stages. In the first stage, we create the SG which characterizes rich data-data, instruction-instruction and instruction-data relationships in the function code. In the second stage, we propose EA-RGCN which contains three parts: node and edge representation via word2vec, content feature extraction with a residual GCN (RGCN) module, and semantic feature extraction using an edge attention (EA) module. Finally, we concatenate the code content features and the semantic features to obtain the global code feature and use a Multilayer Perceptron (MLP) to identify whether the function is vulnerable. We conduct experiments on the dataset constructed from real-world smart contracts. Experimental results demonstrate that the proposed semantic graph and the EA-RGCN model can achieve superior performance. © 2022, The Authors. All rights reserved.

关键词： Smart contract

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Deep Semantic-Enhanced Event Detection via Symmetric Graph Convolutional Network

Deep Semantic-Enhanced Event Detection via Symmetric Graph C...

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IEEE International Conference on Big knowledge (ICBK)

作者： Chenchen Sun Xingrui Zhuo Zhenya Lu Chenyang Bu Gongqing Wu Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

Event detection, an important research topic of information extraction, aims to automatically identify and classify event instances from the text. Previous studies have introduced methods combining syntactic information and graph convolutional networks into the field of event detection and verified their effectiveness. However, such methods often ignore the high-order information on the syntactic tree with noisy words, which limits their classification quality. In this paper, we propose a deep symmetric graph convolutional network to organically integrate high-order and low-order syntactic information to strengthen the semantic features of sentences. Specifically, we design a skip connection with attention gating mechanism, which selects valuable low-order syntactic information under the supervision of high-order syntactic information to strengthen the aggregation of high-order and low-order syntactic information. Then, a graph perturbation mechanism is proposed to discard noisy nodes on the syntactic graph to reduce the noisy information in the high-order syntactic information. We conducted extensive experiments on the widely used ACE 2005 benchmark, and the experimental results demonstrate that our method significantly outperforms state-of-the-art methods. Then, a graph perturbation mechanism is proposed to discard noisy nodes on the syntactic graph to reduce the noisy information in the high-order syntactic information. We conducted extensive experiments on the widely used ACE 2005 benchmark, and the experimental results demonstrate that our method significantly outperforms state-of-the-art methods. We conducted extensive experiments on the widely used ACE 2005 benchmark, and the experimental results demonstrate that our method significantly outperforms state-of-the-art methods. Then, a graph perturbation mechanism is proposed to discard noisy nodes on the syntactic graph to reduce the noisy information in the high-order syntactic information.

关键词： knowledge engineering Event detection Perturbation methods Semantics Syntactics Benchmark testing Information retrieval

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Evaluation of China's Digital Economy: A Case Study using Entropy and TOPSIS 11

Evaluation of China's Digital Economy: A Case Study using En...

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11th International Conference on Information Technology and Quantitative Management, ITQM 2024

作者： Liu, Junjie Wang, Boya Xue, Jiayu Qu, Yi Shi, Yong Sino-Danish College University of Chinese Academy of Sciences Beijing100049 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China Faculty of Science and Engineering University of Nottingham Ningbo China Zhejiang Ningbo315100 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China College of Information Science and Technology University of Nebraska at Omaha OmahaNE68182 United States

Recently, digital economy in social science has become increasingly significant. A well-constructed evaluation structure could generally and promptly describe and reveal its overall social digitalization development. Building on existent researches, this paper improves the evaluation system, which comprises four first-level indicators and thirty-one second-level indicators. Notably, this paper innovatively incorporates higher education into digital innovation of the evaluation. The Entropy method is utilized to assign weights to various indicators in an objective way, with employing the TOPSIS approach to prioritize the development of digital economy in places based on the closeness to the ideal solution. By analysing related data related from 31 provinces in China between 2017 to 2021, this paper greatly finds: Firstly, the development of digital economy vary significantly across provinces, with most provinces at a relatively poor level, indicating considerable potential for overall growth;Secondly, Guangdong, Jiangsu, Zhejiang, and Shandong consistently ranked in the top four from 2017 to 2021, reflecting their specific development attributes and their emphasis on digital economy;Thirdly, significant disparities exist both between regions and among provinces. Consequently, this paper suggests establishing a comprehensive national digital economy development plan, formulating region-specific digital economy policies tailored to local conditions, and encouraging leading provinces to actively share experiences with other provinces. © 2024 The Authors.

关键词： Digital economy Evaluation Multi-source information TOPSIS

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Label Enhanced Event Detection with Collective knowledge and Heterogeneous Graph

Label Enhanced Event Detection with Collective Knowledge and...

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IEEE International Conference on Big knowledge (ICBK)

作者： Gongqing Wu Zhenya Lu Zhuochun Miao Xingrui Zhuo Zan Zhang Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

Event Detection (ED) aims to recognize instances of specified types of event triggers in text. Existing graph neural network-based models have achieved promising progress to alleviate this problem by capturing different orders of syntactic information, but they are limited by two issues. First, the long-range syntactic information between words is not fully exploited. Second, they ignore the semantic information provided by dependency labels which provide linguistic knowledge that is useful to ED. As a result, we proposed a label-enhanced dense graph convolutional network that employs dense connectivity and Graph Transformer Networks (GTN) to learn a flexible selection of edge types and composite relations between the words. Each layer can make use of the collective knowledge of dense blocks in order to model syntactic dependencies over long distances through dense connectivity. The proposed model achieves state-of-the-art performance for ED on common datasets after extensive experiments are conducted to show its advantages.

关键词： knowledge engineering Event detection Text recognition Semantics Syntactics Linguistics Transformers

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Adaptive Loose Optimization for Robust Question Answering

arXiv

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arXiv 2023年

作者： Ma, Jie Wang, Pinghui Wang, Zewei Kong, Dechen Hu, Min Han, Ting Liu, Jun The Ministry of Education of Key Laboratory for Intelligent Networks and Network Security School of Cyber Science and Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China The Ministry of Education of Key Laboratory for Intelligent Networks and Network Security School of Automation Science and Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China The China Mobile Research Institute China The Shannxi Provincial Key Laboratory of Big Data Knowledge Engineering School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Xi’an710049 China

Question answering methods are well-known for leveraging data bias, such as the language prior in visual question answering and the position bias in machine reading comprehension (extractive question answering). Current debiasing methods often come at the cost of significant in-distribution performance to achieve favorable out-of-distribution generalization ability, while non-debiasing methods sacrifice a considerable amount of out-of-distribution performance in order to obtain high in-distribution performance. Therefore, it is challenging for them to deal with the complicated changing real-world situations. In this paper, we propose a simple yet effective novel loss function with adaptive loose optimization, which seeks to make the best of both worlds for question answering. Our main technical contribution is to reduce the loss adaptively according to the ratio between the previous and the current optimization state on mini-batch training data. This loose optimization can be used to prevent non-debiasing methods from overlearning data bias while enabling debiasing methods to maintain slight bias learning. Experiments on the visual question answering datasets, including VQA v2, VQA-CP v1, VQA-CP v2, GQA-OOD, and the extractive question answering dataset SQuAD demonstrate that our approach enables QA methods to obtain state-of-the-art in- and out-of-distribution performance in most cases. The source code has been released publicly in https://***/reml-group/ALO. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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A Novel Local Search Algorithm Based on Connectivity Method and Vertex Penalty Mechanism for Minimum Weighted Connected Dominating Set Problem

SSRN

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SSRN 2024年

作者： Li, Ruizhi He, Jintao Yin, Minghao Hu, Shuli Ding, Nan Jilin University of Finance and Economics Changchun130117 China Northeast Normal University Changchun130117 China Business Big Data Research Center of Jilin Province Changchun130117 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China

The minimum weighted connected dominating set problem is a classical NP-hard problem with wide applications in practice. In this paper, an efficient local search algorithm is proposed to solve this problem. In this algorithm, firstly, an initial solution construction method based on greedy randomized adaptive strategy is adopted, which can balance the quality and the diversity of the initial solutions. Second, two new scoring functions, Ascore and Cscore, are proposed to increase the diversity of deleted vertices. Third, a new connectivity method is proposed so that the algorithm no longer relies on the shortest path between two connected components. Fourth, a vertex penalty mechanism is introduced, which helps the algorithm to jump out of the local optimum effectively. Fifth, two scoring functions, Ascore and Cscore, are combined with the vertex penalty mechanism to propose an effective vertex selection method, which helps the algorithm to select suitable vertices to be added to or removed from the current candidate solution. Finally, the propsoed local search algorithm is compared with seven state-of-the-art algorithms on three types of benchmark instances. The experimental results show that the algorithm exhibits better performance on these three types of classical benchmark instances. © 2024, The Authors. All rights reserved.

关键词： Learning algorithms

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Once Read is Enough: Domain-specific Pretraining-free Language Models with Cluster-guided Sparse Experts for Long-tail Domain knowledge 38

Once Read is Enough: Domain-specific Pretraining-free Langua...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Dong, Fang Chen, Mengyi Zhou, Jixian Shi, Yubin Chen, Yixuan Dong, Mingzhi Wang, Yujiang Li, Dongsheng Yang, Xiaochen Zhu, Rui Dick, Robert Lv, Qin Yang, Fan Lu, Tun Gu, Ning Shang, Li China and Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China Oxford Suzhou Centre for Advanced Research Suzhou China University of Bath United Kingdom Microsoft Research Asia Shanghai China School of Mathematics & Statistics University of Glasgow United Kingdom Bayes Business School City St George's University of London United Kingdom Department of Electrical Engineering and Computer Science University of Michigan United States Department of Computer Science University of Colorado Boulder United States School of Microelectronics Fudan University China

Language models (LMs) only pretrained on a general and massive corpus usually cannot attain satisfying performance on domain-specific downstream tasks, and hence, applying domain-specific pretraining to LMs is a common and indispensable practice. However, domain-specific pretraining can be costly and time-consuming, hindering LMs' deployment in real-world applications. In this work, we consider the incapability to memorize domain-specific knowledge embedded in the general corpus with rare occurrences and "long-tail" distributions as the leading cause for pretrained LMs' inferior downstream performance. Analysis of Neural Tangent Kernels (NTKs) reveals that those long-tail data are commonly overlooked in the model's gradient updates and, consequently, are not effectively memorized, leading to poor domain-specific downstream performance. Based on the intuition that data with similar semantic meaning are closer in the embedding space, we devise a Cluster-guided Sparse Expert (CSE) layer to actively learn long-tail domain knowledge typically neglected in previous pretrained LMs. During pretraining, a CSE layer efficiently clusters domain knowledge together and assigns long-tail knowledge to designate extra experts. CSE is also a lightweight structure that only needs to be incorporated in several deep layers. With our training strategy, we found that during pretraining, data of long-tail knowledge gradually formulate isolated, "outlier" clusters in an LM's representation spaces, especially in deeper layers. Our experimental results show that only pretraining CSE-based LMs is enough to achieve superior performance than regularly pretrained-finetuned LMs on various downstream tasks, implying the prospects of domain-specific-pretraining-free language models. © 2024 Neural information processing systems foundation. All rights reserved.

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