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MEFA-Net: A mask enhanced feature aggregation network for polyp segmentation

Computers in Biology and Medicine 2025年 186卷 109601-109601页

作者： Ke, Xiao Chen, Guanhong Liu, Hao Guo, Wenzhong Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou University Fuzhou350116 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou350116 China

Accurate polyp segmentation is crucial for early diagnosis and treatment of colorectal cancer. This is a challenging task for three main reasons: (i) the problem of model overfitting and weak generalization due to the multi-center distribution of data;(ii) the problem of interclass ambiguity caused by motion blur and overexposure to endoscopic light;and (iii) the problem of intraclass inconsistency caused by the variety of morphologies and sizes of the same type of polyps. To address these challenges, we propose a new high-precision polyp segmentation framework, MEFA-Net, which consists of three modules, including the plug-and-play Mask Enhancement Module (MEG), Separable Path Attention Enhancement Module (SPAE), and Dynamic Global Attention Pool Module (DGAP). Specifically, firstly, the MEG module regionally masks the high-energy regions of the environment and polyps through a mask, which guides the model to rely on only a small amount of information to distinguish between polyps and background features, avoiding the model from overfitting the environmental information, and improving the robustness of the model. At the same time, this module can effectively counteract the "dark corner phenomenon" in the dataset and further improve the generalization performance of the model. Next, the SPAE module can effectively alleviate the inter-class fuzzy problem by strengthening the feature expression. Then, the DGAP module solves the intra-class inconsistency problem by extracting the invariance of scale, shape and position. Finally, we propose a new evaluation metric, MultiColoScore, for comprehensively evaluating the segmentation performance of the model on five datasets with different domains. We evaluated the new method quantitatively and qualitatively on five datasets using four metrics. Experimental results show that MEFA-Net significantly improves the accuracy of polyp segmentation and outperforms current state-of-the-art algorithms. Code posted on https://***/

关键词： Endoscopy

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LMD2F-Net: Layered Multi-Scale Dual-Branch Dual-Temporal Fusion Network for Medical Image Segmentation

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IEEE Access 2024年

作者： Ye, Siyuan Wei, Yan Chongqing Normal University College of Computer and Information Science Chongqing401331 China Chongqing Engineering Research Center of Educational Big Data Intelligent Perception and Application Chongqing401331 China

Image segmentation techniques play a crucial role in medical image analysis, which is directly related to the diagnosis of diseases, the development of treatment plans, and the evaluation of efficacy. Although Convolutional neural networks (CNNs) and transformer-based approaches have made significant progress in this area, the inherent complexity of medical images, including features such as low contrast, fuzzy boundaries, and noise, makes automated segmentation tasks still challenging. To address these issues, we propose LMD2F-Net, an innovative encoder-decoder architecture that cleverly fuses the multi-axis attention mechanism of MaxViT and the global context modelling capability of Swin Transformer through a two-branch structure. This combination not only enhances the model's ability to capture local features, but also improves the understanding of the global context. In the decoding stage, we propose the Multi-scale Bitemporal Fusion Module (MBFM), which further optimises the feature fusion through a multi-scale feature extraction and attention mechanism, and significantly improves the model's ability to identify and capture key features of medical images. In addition, we introduce the dual-level fusion (DLF) module, which acts as a bridge between the encoder and decoder to effectively integrate features at different levels through the cross-attention mechanism. Experimental results on several challenging medical image segmentation datasets show that LMD2F-Net outperforms existing methods in several evaluation metrics, especially in key metrics such as Dice similarity coefficient and Hausdorff distance, which demonstrate its superior performance. These findings confirm the potential of LMD2F-Net in improving the accuracy and robustness of medical image segmentation, providing a valuable reference for future research and clinical practice. © 2013 IEEE.

关键词： Convolutional neural networks

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An elevation change dataset in Greenland ice sheet from 2003 to 2020 using satellite altimetry data

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big Earth data 2024年第2期8卷 231-248页

作者： Bojin Yang Shuang Liang Huabing Huang Xinwu Li Key Laboratory of Digital Earth Science Aerospace Information Research InstituteChinese Academy of SciencesBeijingChina International Research Center of Big Data for Sustainable Development Goals BeijingChina University of Chinese Academy of Sciences BeijingChina School of Geospatial Engineering and Science Sun Yat-Sen UniversityGuangzhouChina Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai) ZhuhaiChina

ABSTRACT A decade-long pronounced increase in temperatures in the Arctic resulted in a global warming hotspot over the Greenland ice sheet(GrIS).Associated changes in the cryosphere were the consequence and led to a demand for monitoring glacier changes,which are one of the major parameters to analyze the responses of the GrIS to climate ***-term altimetry data(***,CryoSat-2,and ICESat-2)can provide elevation changes over different periods,and many methods have been developed for altimetry alone to obtain elevation *** this work,we provided the long-term elevation change rate data of the GrIS in three different periods using ICESat data(from February 2003 to October 2009),Cryosat-2 data(from August 2010 to October 2018)and ICESat-2 data(from October 2018 to December 2020).Optimal methods were applied to the datasets collected by three different altimeters:crossover analysis for ICESat/ICESat-2 and the surface fit method for *** data revealed that the elevation change rates of the GrIS were-12.19±3.81 cm/yr,-19.70±3.61 cm/yr and-23.39±3.06 cm/yr in the three different periods,corresponding to volume change rates of-210.20±25.34 km^(3)/yr,-339.11±24.01 km^(3)/yr and-363.33±20.37 km^(3)/yr,*** general,the obtained results agree with the trends discovered by other studies that were also derived from satellite altimetry *** dataset provides the basic data for research into the impact of climate change over the *** dataset is available at https://***/10.57760/sciencedb.j00076.00121.

关键词： Greenland ice sheet elevation change volume change satellite altimetry

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An accurate cell segmentation method for high-resolution complex brain image

An accurate cell segmentation method for high-resolution com...

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International Conference on Image, Vision and Computing (ICIVC)

作者： Xiaofeng Xu Shuai Li Wei Zheng Henan Engineering Research Center of Intelligent Processing for Big Data of Digital Image School of Computer Science and Technology Nanyang Normal University Nanyang China

ISBN: (数字)9798350385991

ISBN: (纸本)9798350386004

Cell segmentation is a key step in medical image analysis and cell biology research, and is important in areas such as understanding cell behaviour, cancer detection and cell cycle analysis. This task is challenging due to the presence of overlap between cells and image noise. With the development of imaging techniques, the amount and quality of acquired cellular image data have increased significantly and even contain diverse information such as neuronal structures and blood vessels, which puts higher demands on segmentation techniques. Real rat brain cell images obtained using micro-optical slice tomography (MOST) contain complex and diverse information such as neuronal structure, cell morphology, and the distribution of blood vessels, demonstrating the characteristics of real biological samples, for which segmentation of the target region is more difficult. In order to solve these challenges, this paper proposes a new cell image segmentation method that combines watershed order differential feature extraction and fuzzy entropy spatial theory. Due to the complexity of image information, we first use traditional image enhancement techniques. Then feature extraction is performed and fuzzy entropy processing is added to achieve accurate identification of cell boundaries. Then distance transform and curvature analysis are added to further segment the cell clusters into individual cells while avoiding over-segmentation and missed detection. Experiments demonstrate that the method not only shows high performance on our dataset, but also validates our method on several publicly available cellular datasets respectively, proving that the segmentation algorithm shows great potential for segmentation on different types of cells. Future research will further optimise the model to improve segmentation performance on complex and irregularly shaped cells and explore the application of these methods to other types of medical image segmentation tasks.

关键词： Image segmentation Accuracy Morphology Clustering algorithms Transforms Feature extraction Entropy Complexity theory data mining Biomedical imaging

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Multi-level Distance Metric Learning for Few-shot Spoken term classification 7

Multi-level Distance Metric Learning for Few-shot Spoken ter...

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2022 7th International Conference on Electronic Technology and Information science, ICETIS 2022

作者： Wu, Yiqi Ma, Zhongchen Mao, Qirong School of Computer Science and Communication Engineering Jiangsu University Jiangsu Zhenjiang212000 China Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications Jiangsu Zhenjiang212000 China

ISBN: (纸本)9783800757794

Spoken term classification has attracted great attention of researchers because of its wide application in real life. Traditional methods use a deep neural network trained with tremendous samples to classify a particular keyword which may not be applicable in case of insufficient samples and restrict the system from recognizing new or user-defined keywords. Few-shot learning algorithm is then applied to this speech task since it can use only a few examples to adapt to classifying new classes. Metric learning for few-shot learning aims to learn a metric in episode learning such that the distance between the positive pair of each episode is closed and the distance between the negative pair of each episode is far away. However, due to various pronunciation, speed or intonation, some spoken samples may be extremely dissimilar to its positive sample, but very similar to its negative sample, leading these samples to be ignored or overly influenced in metric learning. Therefore, to cope with this problem we propose a multi-level distance regularized metric learning method for few-shot spoken term classification. It explicitly disturbs a learning procedure by regularizing pairwise distances between embedding vectors into multiple levels. In this way, the degree of similarity between a pair can be fully utilized and the importance of each sample in metric learning is considered in a balanced manner. Multi-level Distance Regularization with simple Prototypical Network achieves the state-of-the-art performance in benchmark dataset: Google Speech Commands dataset. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Learning algorithms

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Information and Sensing Beamforming Optimization for Multi-User Multi-Target MIMO ISAC Systems 48

Information and Sensing Beamforming Optimization for Multi-U...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Zhu, Minghe Li, Lei Xia, Shuqiang Chang, Tsung-Hui The Chinese University of Hong Kong School of Science & Engineering Shenzhen China Shenzhen Research Institute of Big Data China ZTE Corporation China State Key Laboratory of Mobile Network and Mobile Multimedia Technology China

ISBN: (纸本)9781728163277

In this paper, we consider the joint beamforming design for simultaneous sensing and communication in a wireless multi-user system. Different from the existing works that mostly are for single target, we consider sensing the channel parameters of multiple targets while communicating with multiple users. The design goal is to minimize a weighted sum of the Cramer-Rao bounds (CRB) of target parameters subject to the communication sum rate and transmission power constraints. While the classical weighted minimum mean square error (WMMSE) and semidefinite relaxation (SDR) can be used to handle the problem, we propose to reformulate the problem into a max-min form, by leveraging the tightness of SDR, and solve it by a low-complex first-order method. Numerical results not only demonstrate the computation efficiency of the proposed algorithm but also its effectiveness in enhancing the sensing performance in practice. © 2023 IEEE.

关键词： Mean square error

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Prediction of Ionospheric TEC Based on BLS-LSTM-GRU Hybrid Model 14th

Prediction of Ionospheric TEC Based on BLS-LSTM-GRU Hybrid M...

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14th China Satellite Navigation Conference, CSNC 2024

作者： Li, Dengao Zhang, Xinfang Zhao, Jumin Wu, Fanming Feng, Ran Zhao, Jinhua Shi, Danyang College of Data Science Taiyuan University of Technology Taiyuan030024 China College of Information and Computer Taiyuan University of Technology Taiyuan030024 China Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province Taiyuan030024 China Intelligent Perception Engineering Technology Center of Shanxi Taiyuan030024 China Shanxi Province Engineering Technology Research Center of Spatial Information Network Taiyuan030024 China

ISBN: (纸本)9789819969272

In satellite communications, global navigation satellite systems(GNSS) and other important space activities, the value of total electron content (TEC) in the ionosphere directly affects the size of ionospheric delay. Achieving accurate prediction of ionospheric total electron content through modeling can effectively improve the reliability and accuracy of GNSS positioning. Considering the high prediction accuracy of long short-term memory neural network (LSTM) and the fast training speed of gated recurrent unit network (GRU), but it is easy to fall into the local optimum, while the width learning system(BLS) can effectively avoid this drawback. Therefore, this paper proposes a model for predicting the total electron content of the ionosphere, which consists of a broad learning system, an LSTM network and a GRU networks. Six parameters of TEC, geomagnetic index Dst, Kp, Ap, solar activity index F10.7, and hour (HD) are selected as input features from the observed data to predict the variation of the total ionospheric electron content. The results generate prediction maps of the total ionospheric electron content and are compared with empirical models and conventional neural network models at different times and solar activity conditions. The experimental results show that the root mean square error (RMSE) of the prediction results of this BLS-LSTM-GRU model is decreased by 23.07%, 17.48% and 9.46% compared with the IRI-2016, NeQuick and CNN-LSTM prediction models, respectively, with higher prediction accuracy. © 2024, Aerospace Information research institute.

关键词： Long short-term memory

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Heavy Rainfall Prediction Model Using Sample Entropy Derived from GNSS-PWV and PSO-SVM 14th

Heavy Rainfall Prediction Model Using Sample Entropy Derived...

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14th China Satellite Navigation Conference, CSNC 2024

作者： Wu, Fanming Li, Dengao Zhao, Jinhua Feng, Ran Shi, Danyang Zhang, Xinfang Zhao, Jumin College of Data Science Taiyuan University of Technology Taiyuan030024 China College of Information and Computer Taiyuan University of Technology Taiyuan030024 China Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province Taiyuan030024 China Intelligent Perception Engineering Technology Center of Shanxi Taiyuan030024 China Shanxi Province Engineering Technology Research Center of Spatial Information Network Taiyuan030024 China

ISBN: (纸本)9789819969272

There is a growing interest to use Global Navigation Satellite System (GNSS) inversed PWV for heavy rainfall prediction. When heavy rainfall occurs, it requires the atmosphere to contain sufficient water vapour and undergo strong upward motion. However, existing models using GNSS-PWV for heavy rainfall prediction have not accounted for the effect of the complex motion of water vapour. In this paper, an hourly heavy rainfall prediction model using sample entropy derived from GNSS-PWV and PSO-SVM is proposed. The sample entropy of GNSS-PWV is used to measure the complexity of the water vapour movement process before heavy rainfall occurs. Meanwhile, combining GNSS-PWV time domain features and co-located meteorological data, the occurrence of hourly heavy rainfall events is predicted by support vector machine optimized with particle swarm. To verify the validity and feasibility of the proposed algorithm, Hong Kong's HKSC station in Sham Shui Po is used for the model to train and test. The result shows that probability of detection, false alarm rate and critical success index of the proposed algorithm in this paper have significantly improved over other heavy rainfall prediction models. © 2024, Aerospace Information research institute.

关键词： Support vector machines

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research on Zenith Tropospheric Delay Model Based on TCN Improving HGPT2 Model 14th

Research on Zenith Tropospheric Delay Model Based on TCN Imp...

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14th China Satellite Navigation Conference, CSNC 2024

作者： Li, Dengao Shi, Danyang Zhao, Jumin Wu, Fanming Yan, Liangquan Feng, Ran Zhang, Xinfang Zhao, Jinhua College of Data Science Taiyuan University of Technology Taiyuan030024 China College of Information and Computer Taiyuan University of Technology Taiyuan030024 China Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province Taiyuan030024 China Intelligent Perception Engineering Technology Center of Shanxi Taiyuan030024 China Shanxi Province Engineering Technology Research Center of Spatial Information Network Taiyuan030024 China

ISBN: (纸本)9789819969272

The zenith tropospheric delay (ZTD) obtained by global navigation satellite system (GNSS) atmospheric sounding is a pivotal data source for water vapor monitoring. Meteorological changes in Antarctica play an important role in analysis of the global climate, but factors such as complex climatic conditions can limit the collection of meteorological data needed for ZTD retrieval. Therefore, it is necessary to establish high-precision ZTD models that do not rely on measured meteorological data. The existing global pressure and temperature 3 (GPT3) model has limited ability to capture complex weather variations and cannot obtain high-precision GPT3_ZTD. To address the above issues, this research proposes a high-precision ZTD model through using temporal convolutional network (TCN) for improving the hourly global pressure and temperature 2 (HGPT2) model. The HGTP2 model based on Fourier analysis and the time-segmentation concept can consider the linear trend of climate variations, simultaneously the TCN is introduced to simulate the long-term temporal dependence of HGPT2_ZTD, so as to achieve the goal of obtaining high-precision ZTD without relying on measured meteorological data. The experimental results show that the precision of TCN_ZTD obtained from the proposed model is higher than GPT3_ZTD and HGPT2_ZTD when using the GNSS_ZTD observations obtained from 35 stations in Antarctica as a reference, and the improvement of the model is obvious. © 2024, Aerospace Information research institute.

关键词： Fourier analysis

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Large Language Models as Evolutionary Optimizers 13

Large Language Models as Evolutionary Optimizers

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Liu, Shengcai Chen, Caishun Qu, Xinghua Tang, Ke Ong, Yew-Soon Southern University of Science and Technology Centre for Frontier Ai Research A∗star Department of Computer Science and Engineering China Centre for Frontier Ai Research A∗star Singapore Tianqiao & Chrissy Chen Institute China Southern University of Science and Technology Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering China Nanyang Technological University Centre for Frontier Ai Research A∗star College of Computing and Data Science Singapore

ISBN: (纸本)9798350308365

Evolutionary algorithms (EAs) have achieved remarkable success in tackling complex combinatorial optimization problems. However, EAs often demand carefully-designed operators with the aid of domain expertise to achieve satisfactory performance. In this work, we present the first study on large language models (LLMs) as evolutionary combinatorial optimizers. The main advantage is that it requires minimal domain knowledge and human efforts, as well as no additional training of the model. This approach is referred to as LLM-driven EA (LMEA). Specifically, in each generation of the evolutionary search, LMEA instructs the LLM to select parent solutions from current population, and perform crossover and mutation to generate offspring solutions. Then, LMEA evaluates these new solutions and include them into the population for the next generation. LMEA is equipped with a self-adaptation mechanism that controls the temperature of the LLM. This enables it to balance between exploration and exploitation and prevents the search from getting stuck in local optima. We investigate the power of LMEA on the classical traveling salesman problems (TSPs) widely used in combinatorial optimization research. Notably, the results show that LMEA performs competitively to traditional heuristics in finding high-quality solutions on TSP instances with up to 20 nodes. Additionally, we also study the effectiveness of LLM-driven crossover/mutation and the self- adaptation mechanism in evolutionary search. In summary, our results reveal the great potentials of LLMs as evolutionary optimizers for solving combinatorial problems. We hope our research shall inspire future explorations on LLM-driven EAs for complex optimization challenges. © 2024 IEEE.

关键词： Traveling salesman problem

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