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Semantic knowledge transfer for semi-supervised medical image segmentation

Engineering Applications of Artificial Intelligence 2025年 158卷

作者： Shiwei Zhou Haifeng Zhao Leilei Ma Dengdi Sun Key Laboratory of Intelligent Computing & Signal Processing (ICSP) Ministry of Education Anhui University Hefei 230601 China Anhui Provincial Key Laboratory of Multimodal Cognitive Computation Anhui University Hefei 230601 China School of Computer Science and Technology Anhui University Hefei 230601 China School of Artificial Intelligence Anhui University Hefei 230601 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei 230026 China

In semi-supervised medical image segmentation, due to possible differences in information content and distribution between labeled and unlabeled data, dealing with the two separately usually prevents knowledge transfer from labeled to unlabeled data. This prevents the model from effectively sharing learned information between the two types of data. To alleviate this problem, we train labeled and unlabeled data as a whole. Semantic mixing of labeled and unlabeled data is achieved by selecting and exchanging some of the region images of both through a mask to generate complementary input views. In addition, due to the limited labeled data, the unlabeled data has a weak ability to distinguish categories in the feature space. Traditional methods rely on pixel positions to generate positive and negative samples for contrastive learning to solve this problem, but relying on pixel position sampling can easily lead to semantic inconsistency, which affects the effect of feature learning; therefore, to address this problem, we propose an innovative labeled data-guided inter-class contrastive learning strategy, which extracts the category features from labeled and unlabeled data and exploits the accurate category information in the labeled data to guide contrastive learning, while introducing a similarity-based ranking weighting mechanism. Combining the two designs, we propose a new semantic knowledge transfer framework for semi-supervised medical image segmentation. Experiments demonstrate a significant improvement in our model compared to State of the Art (SOTA) on the Automatic Cardiac Diagnosis Challenge (ACDC) dataset and the Left Atrium (LA) dataset.

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A Wideband Dual-Band Magneto-Electric Dipole Antenna with Modified Feed Structure 10

A Wideband Dual-Band Magneto-Electric Dipole Antenna with Mo...

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10th International Conference on Microwave and Millimeter Wave Technology, ICMMT 2018

作者： Li, Fan Sun, Yufa Zhu, Haoran Fang, Ionzvu Anhui University Ministry of Education Key Lab of Intelligent Computing and Signal Processing Hefei230601 China

ISBN: (纸本)9781538624166

In this paper, a novel wideband dual-band magneto-electric dipole antenna with modified feed structure is proposed. Double-layer patches are utilized to design a pair of folding metal. The double-layer patches dipole can generate two resonant frequencies, which are employed to form a dual-band magneto-electric dipole antenna. A stair-shaped feeding strip with a hexagonal structure is designed to modify the impedance matching. It is found from simulated results that two operation bands with bandwidths of 60% from 1.61 to 2.99 GHz and 31.9% from 4.99 to 6.89 GHz under the return loss level of 10 dB were achieved. The gain of the antenna ranged from 7.56 to 10.56 dBi. Furthermore, low cross-polarization level and low back-radiation are also obtained with the proposed antenna. © 2018 IEEE.

关键词： Natural frequencies

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Correction: Self-orthogonal codes over a non-unital ring and combinatorial matrices

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Designs, Codes and Cryptography 2023年第2期91卷 691-691页

作者： Shi, Minjia Wang, Shukai Kim, Jon-Lark Solé, Patrick Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Mathematical Sciences Anhui University Hefei China School of Mathematical Sciences Anhui University Hefei China Department of Mathematics Sogang University Seoul South Korea Aix Marseille Univ CNRS Centrale Marseille I2M Marseilles France

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A wideband dual-polarized magneto-electric dipole antenna for 2G/3G/LTE/WiMAX base stations

A wideband dual-polarized magneto-electric dipole antenna fo...

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2018 IEEE MTT-S International Wireless Symposium, IWS 2018

作者： Lu, Zongzheng Sun, Yufa Zhu, Haoran Zhang, Hongwei Huang, Fei Key Lab of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230601 China

ISBN: (纸本)9781538663462

A wideband dual-polarized magneto-electric (ME) dipole antenna is proposed for 2G/3G/LTE/WiMAX applications. It also applies to 5G (3.3-3.6 GHz). The proposed antenna has Γ-shaped feeding strips to impart a wide impedance bandwidth for it special structure, and a rectangular box-shaped reflector to enhance its stability in radiation patterns and high gain over the operating frequencies. The simulated results show a common impedance bandwidth is 81.2% with standing-wave ratio (SWR) ≤ 1.5 from 1.58 to 3.74 GHz, port-to-port isolation is large than 25dB within the bandwidth. The simulated antenna gains vary from 8 to 10.7 dBi and from 8.2 to 10.6 dBi for port 1 and port 2, respectively. The antenna has nearly symmetrical radiation patterns with low back lobe radiation both in horizontal and vertical planes, and broadside radiation patterns with narrow beam can be also obtained. © 2018 IEEE.

关键词： Base stations

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Seismic Event Detection via Deep Multi-Task Learning

Seismic Event Detection via Deep Multi-Task Learning

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International Joint Conference on Neural Networks (IJCNN)

作者： Yang Yu Lei Zhang Jiakai Shen Qingcai Wang Guiquan Liu School of Computer Science and Technology University of Science and Technology of China Hefei China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei China Department of Computer Science and Technology Nanjing University Nanjing China

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

It is fundamental to detect seismic events reliably and efficiently when processing continuous waveform data recorded by seismic stations. Recently, convolutional neural network (CNN) based detecting methods have been proposed for seismic events detection and obtained great success in this area, where the learning of seismic event detecting network of all seismic stations is considered as one learning task and numerous labeled data need to be collected for training the detecting network. However, they tend to ignore the differences between seismic stations caused by geographic position. Moreover, due to a few seismic activities and high cost of manual data labeling, in some areas, the labeled data for seismic event detecting tasks is insufficient. Under this condition, these methods always encounter over-fitting problem leading to bad detection performance. In this paper, we propose a multi-task based framework based on convolutional neural network for accurate seismic event detection under the condition of insufficient labeled data. Specifically, we first cluster the seismic stations into several station clusters and treat the learning of seismic event detecting network of every station cluster as a learning task, and then we propose a deep multi-task network named detectMTIA among multiple tasks. Experimental results on a real-world seismic dataset with nine stations demonstrate the effectiveness of the proposed framework, especially when the labeled data is insufficient.

关键词： Task analysis Clustering algorithms

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A Novel Solution of Underdetermined Equations for DGTD based on Prior knowledge

A Novel Solution of Underdetermined Equations for DGTD based...

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International Conference on Microwave and Millimeter Wave Technology Proceedings

作者： Qi Qi Xinyuan Cao Mingsheng Chen Zhixiang Huang Yi Liu Xianliang Wu Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei Anhui Province Key Laboratory of Simulation and Design for Electronic Information System Hefei Normal University Hefei China School of Electronic Engineering Huainan Normal University Huainan China

ISBN: (数字)9781728157337

ISBN: (纸本)9781728157344

To accelerate solving transient electromagnetic problems by discontinuous Galerkin time domain (DGTD) method, a solution of underdetermined equations is established based on prior knowledge. In the proposed method, nodal basis function and a leapfrog scheme are used to Maxwell curl equations for spatial and time discretization. Electromagnetic fields in each element of computation domain are updated individually by applying conventional DGTD in initial time. When the electromagnetic wave covers the domain, the fields of all elements are updated as a whole. The underdetermined equations are established by randomly extracting rows from global mass matrix. Taking the results of a few previous time steps as the prior knowledge, a sparse transform is constructed. Then the underdetermined system of equations can be solved by recovery algorithms. Meanwhile,in order to maintain the advantage of low computational complexity, a restart mechanism is presented. The validity of the proposed method is verified by numerical experiments.

关键词： Microwave technology Millimeter wave technology Transforms Sparse matrices Electromagnetic fields Transient analysis Time-domain analysis

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LCD codes from tridiagonal Toeplitz matrice

arXiv

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arXiv 2021年

作者： Shi, Minjia Özbudak, Ferruh Xu, Li Solé, Patrick Key Laboratory of Intelligent Computing Signal Processing Ministry of Education School of Mathematical Sciences Anhui University HefeiAnhui230601 China Department of Mathematics Institute of Applied Mathematics Middle East Technical University Ankara Turkey School of Mathematical Sciences Anhui University HefeiAnhui230601 China I2M Aix Marseille Univ. Centrale Marseille CNRS Marseille France

Double Toeplitz (DT) codes are codes with a generator matrix of the form (I, T) with T a Toeplitz matrix, that is to say constant on the diagonals parallel to the main. When T is tridiagonal and symmetric we determine its spectrum explicitly by using Dickson polynomials, and deduce from there conditions for the code to be LCD. Using a special concatenation process, we construct optimal or quasi-optimal examples of binary and ternary LCD codes from DT codes over extension *** Codes 94B05, 15B05, 12E10 Copyright © 2021, The Authors. All rights reserved.

关键词： Matrix algebra

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Reversible data hiding in encrypted images based on pixel prediction and bit-plane compression

arXiv

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arXiv 2019年

作者： Yin, Zhaoxia Peng, Yinyin Xiang, Youzhi school of Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230601 China

Reversible data hiding in encrypted images (RDHEI) receives growing attention because it protects the content of the original image while the embedded data can be accurately extracted and the original image can be reconstructed lossless. To make full use of the correlation of the adjacent pixels, this paper proposes an RDHEI scheme based on pixel prediction and bitplane compression. Firstly, to vacate room for data embedding, the prediction error of the original image is calculated and used for bit-plane rearrangement and compression. Then, the image after vacating room is encrypted by a stream cipher. Finally, the additional data is embedded in the vacated room by multi-LSB substitution. Experimental results show that the embedding capacity of the proposed method outperforms the state-of-the-art methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Steganography

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Author Correction: Full reconstruction of simplicial complexes from binary contagion and Ising data

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Nature communications 2022年第1期13卷 3692页

作者： Huan Wang Chuang Ma Han-Shuang Chen Ying-Cheng Lai Hai-Feng Zhang The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Mathematical Science Anhui University Hefei 230601 China. School of Internet Anhui University Hefei 230601 China. School of Physics and Material Science Anhui University Hefei 230601 China. School of Electrical Computer and Energy Engineering Arizona State University Tempe AZ 85287 USA. The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Mathematical Science Anhui University Hefei 230601 China. haifengzhang1978@***.

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Multiple reconstruction compression framework based on PNG image

arXiv

引用

arXiv 2019年

作者： Lu, Zhiqing Yin, Zhaoxia Luo, Bin Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei230601 China

It is shown that neural networks (NNs) achieve excellent performances in image compression and reconstruction. However, there are still many shortcomings in the practical application, which eventually lead to the loss of neural network image processing ability. Based on this, this paper proposes a joint framework based on neural network and zoom compression. The framework first encodes the incoming PNG or JPEG image information, and then the image is converted into binary input decoder to reconstruct the intermediate state image, next we import the intermediate state image into the zooming compressor and re-pressurize it, and reconstruct the final image. From the experimental results, this method can better process the digital image and suppress the reverse expansion problem, and the compression effect can be improved by 4 to 10 times as much as that of using RNN alone, showing better ability in application. In this paper, the method is transmitted over a digital image, the effect is far better than the existing compression method alone, the Human visual system can not feel the change of the effect. Copyright © 2019, The Authors. All rights reserved.

关键词： Image compression

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