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WDE-TransUNet: Brain Tumor Segmentation in MRI Images Utilizing Detail Enhancement and Transformers 24

WDE-TransUNet: Brain Tumor Segmentation in MRI Images Utiliz...

引用

5th International Symposium on Artificial Intelligence for Medicine Science, ISAIMS 2024

作者： Ni, Xinhu Mo, Jiaqing Xinjiang Key Laboratory of Signal Detection and Processing School of Computer Science and Technology Xinjiang Urumqi China

ISBN: (纸本)9798400717826

WDE-TransUNet is a novel neural network architecture specifically designed for robust and precise semantic segmentation in medical imaging. This method addresses the challenges posed by inconsistent image quality, diverse pathologies, and anatomical variations due to different imaging techniques by integrating Transformer and convolutional neural network (CNN) modules within an encoder-decoder framework. It utilizes prior knowledge to assign weights at the input stage, optimizing feature utilization across modalities and mitigating the impact of irrelevant features. The encoder employs detail enhanced convolution (DEConv) and root mean square (RMS)Transformer modules to enhance local feature extraction, and then conveys these features to the decoder via skip connections, enriching both spatial and channel information. Evaluated on the Brain Tumor Segmentation Challenge (BraTS)2019, 2020, and 2021 datasets, this method achieved outstanding performance, with average Dice scores of 84.74%, 86.64%, and 90.66%, respectively. Our experimental results demonstrate that WDE-TransUNet, by integrating RSM Transformer with CNN in an encoder-decoder configuration, is superior to existing algorithms for brain tumor segmentation. © 2024 Copyright held by the owner/author(s).

关键词： Semantic Segmentation

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Joint Optimization of Maximum Achievable Rate in SWIPT Systems Assisted by Active STAR-RIS 18th

Joint Optimization of Maximum Achievable Rate in SWIPT Sys...

引用

18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Yang, Junlong Qin, Xizhong Jia, Zhenhong Mao, Lamu School of Computer Science and Technology XJU Xinjiang China Xinjiang Key Laboratory of Signal Detection and Processing Wulumuqi China

ISBN: (纸本)9783031714665

This investigation focuses on a simultaneous wireless information and power transfer (SWIPT) system, significantly enhanced by an active simultaneously transmitting and reflecting reconfigurable intelligent surface (aSTAR-RIS). It extends the degrees of freedom (DoF) for the system and addresses the "double fading" challenge seen in passive RIS frameworks. Our objective is the maximization of the sum achievable rate (SAR), achieved by optimizing the transmit beamforming vector at the base station (BS) and the coefficient and amplification matrices at the aSTAR-RIS. Through an alternating optimization (AO) strategy and fractional programming (FP), a sub-optimal solution is obtained. Simulations demonstrate that our aSTAR-RIS approach results in a 16.5% increase in performance over conventional active RIS configurations and a significant 114% improvement over passive STAR-RIS schemes. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Inductive power transmission

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TBIA-DBNet: A Two-Branch Image-Adaptive DBNet for Scene Text detection in Real-World Foggy Scenes 27th

TBIA-DBNet: A Two-Branch Image-Adaptive DBNet for Scene Tex...

引用

27th International Conference on Pattern Recognition, ICPR 2024

作者： Liu, Zhaoxi Zhou, Gang He, Runlin Zhang, Mengnan Jia, Zhenhong Ma, Jing Key Laboratory of Signal Detection and Processing Department of Computer Science and Technology Xinjiang University Urumqi China

ISBN: (纸本)9783031781186

Though deep learning-based scene text detection methods have achieved promising results on conventional datasets, these methods are unable to maintain optimal performance in adverse weather conditions, such as foggy weather. To alleviate this problem, we propose a Two-Branch Image-Adaptive DBNet (TBIA-DBNet) framework. Specifically, to avoid missing discriminable features from the original image in one branch, we design an Image Enhancement Network (IENet) in another branch. Additionally, we design a Fusion Module based on Coordinate Attention (FMCA) to fully integrate original and enhanced features. Experimental results demonstrate that TBIA-DBNet significantly enhances scene text detection performance in foggy weather. Notably, it improves detection accuracy by nearly 10% in real-world foggy weather conditions compared to existing methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Deformable Multi-Scale Network for Snow Removal in Video 27th

Deformable Multi-Scale Network for Snow Removal in Video

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27th International Conference on Pattern Recognition, ICPR 2024

作者： He, Runlin Zhou, Gang Xue, Tianhao Liu, Zhaoxi Jia, Zhenhong Key Laboratory of Signal Detection and Processing Department of Computer Science and Technology Xinjiang University Urumqi China

ISBN: (纸本)9783031781247

Snowfall severely degrades outdoor video visibility while reducing the performance of subsequent vision tasks. Although video recovery methods based on deep learning have achieved amazing accomplishments, video snow removal still faces problems such as varying scales and intricate trajectories of snowflakes, which makes it difficult to remove snowflakes and easy to create artifacts on moving objects. To address these issues, we propose a deformable multi-scale video desnowing network. Specifically, we design a multi-scale pseudo-3D residual block(MSRB-P3D) that can effectively remove snowflakes of different scales. Furthermore, a deformable large kernel attention 3Dblock(D-LKA 3Dblock) is introduced to capture the inter-frame dynamic information and reduce the artifacts. Due to the scarcity of dataset, we proposed a new dataset named Synthetic and Real Snowy Video Dataset(SRSVD). Extensive experiments have proven that our proposed method not only outperforms other state-of-the-art methods on both synthetic and real snowy videos, but also effectively improves performance on subsequent vision task. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Snow

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Shared Vision Transformer Helps Scene Text Retrieval 4th

Shared Vision Transformer Helps Scene Text Retrieval

引用

4th International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2024

作者： Luo, Hailong Ibrayim, Mayire Hamdulla, Askar Deng, Qilin School of Computer Science and Technology Xinjiang University Urumqi830017 China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi830017 China

ISBN: (纸本)9789819787043

The scene text retrieval system can search all the images containing the query text in the gallery based on the input query text and locate the position of the query text at the same time. The current state-of-the-art approach is to match query images and text instances based on visual information, which somewhat mitigates the heterogeneous differences between the two modalities and can support the zero-shot training approach. But there is no consideration of two branches of features interacting on information as a way to enhance the features. In this paper, we improve this method. First, we design a structure that enhances features using a shared-visual embedding transformer (S-VET). Text instance features and query input features are brought closer visually by a visual transformer with shared parameters and then spliced and fused with the original features to enrich the visual information of the two branch features. We then design a Multi-Scale Residual Channel Attention (MRCA) module to obtain features at different scales by using convolution kernels at multiple scales and then use channel attention to mine beneficial information from features at different scales. Facilitated by the above scheme, our method achieved a more advanced performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Structured Query Language

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E-RNS: Enhancing Negative Sample Quality from Gradient Perspective for Graph Recommendation

E-RNS: Enhancing Negative Sample Quality from Gradient Persp...

引用

2025 IEEE International Conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Feng, Qiangsheng Qin, Jiwei Ma, Jie School of Computer Science and Technology Xinjiang University China Key Laboratory of Signal Detection and Processing China

ISBN: (纸本)9798350368741

Bayesian Personalized Ranking (BPR) is a widely used optimization function in GNN-based recommender systems, and negative samples are usually obtained through the Random Negative Sampling (RNS) method during BPR training. However, from the gradient perspective, RNS tends to select low-quality samples with minimal information, resulting in small gradients. These small gradients contribute little to BPR optimization, limiting the model's ability to effectively distinguish between positive and negative samples. To alleviate this issue, we propose a general negative sample information enhancement method: Enhancing Random Negative Sampling (E-RNS), which constructs hard negative samples by enhancing the information in randomly selected negative samples. Specifically, in the Noise Injection step, it generates initial noise and injects a certain amount of noise in the same direction into the vector dimensions of positive samples to create enriched information. Then, in the Information Fusion step, this enriched information is mixed with the negative samples to synthesize new hard negative samples. Extensive experiments demonstrate that applying E-RNS to GNN-based recommender models significantly improves performance. © 2025 IEEE.

关键词： Collaborative Filtering Graph Neural Networks Negative Sampling Recommender Systems

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MFDPonzi: Detecting Ethereum Ponzi Schemes Using Static Features from Novel Opcode Sequences

MFDPonzi: Detecting Ethereum Ponzi Schemes Using Static Feat...

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2025 IEEE International Conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Cao, Longwei Qin, Jiwei Zhang, Xuzi College of Computer Science and Technology Xinjiang University Urumqi China Xinjiang Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi China

ISBN: (纸本)9798350368741

Ethereum, the first blockchain platform to support smart contracts, has become a target for various cybercrimes, particularly financial frauds like Ponzi schemes. Ponzi schemes on Ethereum are known as Smart Ponzi Schemes (or Ponzi Contracts) and have caused huge financial losses. Current Ponzi contract detection models face three main challenges: simple opcode sequence processing does not effectively distinguish Ponzi from non-Ponzi contracts, single-feature-based models lack accuracy, and reliance on transaction records hinders early detection. To address these issues, this paper proposes a Multi-Feature Ponzi Scheme detection Model (MFDPonzi). MFDPonzi tracks the changes in stack, memory, and storage parameters during the execution of smart contracts, reconstructing opcode sequences and extracting diverse features, including semantic and developer features. Finally, a multi-feature fusion algorithm is used to enhance model stability. Additionally, MFDPonzi can identify Ponzi contracts at the early stage of smart contract creation without relying on transaction data. Experimental results show that MFDPonzi achieves an 85.9% recall and an 88.7% F-score on Ethereum smart contracts, outperforming baselines in both performance and robustness. © 2025 IEEE.

关键词： Blockchain Ethereum Ponzi scheme Smart contract

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Arbitrary-Shape Text Spotting Based on Global, Pixel and Sequence Semantics 4th

Arbitrary-Shape Text Spotting Based on Global, Pixel and S...

引用

4th International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2024

作者： Zhang, Chunhu Ibrayim, Mayire Hamdulla, Askar Deng, Qilin Xinjiang Key Laboratory of Signal Detection and Processing Urumqi China Xinjiang University School of Information Science and Engineering Urumqi China Xinjiang University College of Future Technology Urumqi China

ISBN: (纸本)9789819787012

The field of end-to-end text spotting has garnered significant interest in recent years, propelled by the revealed intrinsic synergies between scene text detection and recognition. While advancements have been made, the challenge of arbitrarily shaped scene text spotting persists. This paper introduces an innovative feature augmentation module that addresses the issues of limited receptive fields and weak representation typical of lightweight backbone networks, while also enhancing multi-scale information more effectively and reducing information loss during feature aggregation. Furthermore, to extract a richer set of backbone features, we propose a dual information attention mechanism that enables the backbone network to neuronally focus on salient information. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Semantics

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CAU-Net: U-Shaped medical image segmentation network based on compound attention

CAU-Net: U-Shaped medical image segmentation network based o...

引用

2024 International Conference on Computer Vision and Image processing, CVIP 2024

作者： Liu, Jiaqi Mo, Jiaqing Xinjiang University School of Computer Science and Technology Key Laboratory of Signal Detection and Processing No.777 Huarui Street Shuimogou District Xinjiang Urumqi830017 China

ISBN: (纸本)9781510688384

In the context of the ongoing advancement of deep learning technologies, traditional image segmentation approaches are gradually become difficult for adapted to the requirements of medical imaging. The importance of medical image segmentation models is increasing, and they play a key role in accurately delineating anatomical structures and lesion regions. This is crucial for accurate diagnosis of diseases, formulation of treatment plans, and monitoring of disease course. In this paper, we propose a compound attention image segmentation model, CAU-Net, which aims to improve the segmentation accuracy of skin lesion images by introducing the receptive field module (RFB), the double squeezing-excitation (DSE) module, and our innovatively proposed compound attention (CA) module lesion image segmentation accuracy. The design of CAU-Net focuses on enhancing the feature extraction capability and contextual information capturing ability, and improving the edge segmentation capability and play a strong role in the processing of medical images with complex edges and chaotic backgrounds. The model reached an mDice to 0.9408 and a recall to 0.9418 on the ISIC 2018 dataset. Future work can further optimize these modules, extend to more medical image tasks. © 2025 SPIE.

关键词： Image segmentation

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Difference Bonds Consistency and Complementarity to Enhance Multimodal Representation Learning

Difference Bonds Consistency and Complementarity to Enhance ...

引用

2025 IEEE International Conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： He, Congbing Song, Sensen Jia, Zhenhong Zhao, Hui Xinjiang Uygur Autonomous Region Signal Detection and Processing Key Laboratory School of Computer Science and Technology Xinjiang University Urumqi830046 China

ISBN: (纸本)9798350368741

In the field of multimodal representation learning, existing research has primarily focused on exploring modal consistency and modal complementarity, while overlooking the positive role of modal difference. Moreover, modal difference establishes a bonding relationship between modal consistency and modal complementarity. However, existing algorithms lack the study of this relationship, resulting in an accuracy that still needs to be improved on multimodal classification tasks. To tackle the above issues, We propose a novel multimodal representation learning framework. It enhances multimodal representation learning by modal difference constructing connecting bonds of modal consistency and modal complementarity. We conducted experiments on two widely used multimodal emotion recognition datasets, IEMOCAP and MELD. The results demonstrate that our method outperforms existing multimodal representation learning approaches in terms of accuracy on the multimodal emotion recognition task. © 2025 IEEE.

关键词： Multimodal Difference Multimodal Emotion Recognition Multimodal Representation Learning

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