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TransMatch: Employing Bridging Strategy to Overcome Large Deformation for Feature Matching in Gastroscopy Scenario

IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Zhu, Guosong Qin, Zhen Yu, Linfang Ding, Yi Qin, Zhiguang University of Electronic Science and Technology of China Network and Data Security Key Laboratory of Sichuan Province Chengdu611731 China Industrial Research Institute Chengdu610000 China

Feature matching is widely applied in the image processing field. However, both traditional feature matching methods and previous deep learning-based methods struggle to accurately match the features with severe deformations and large displacements, particularly in gastroscopy scenario. To fill this gap, an effective feature matching framework named TransMatch is proposed, which addresses the largely displacements issue by matching features with global information leveraged via Transformer structure. To address the severe deformation of features, an effective bridging strategy with a novel bidirectional quadratic interpolation network is employed. This bridging strategy decomposes and simplifies the matching of features undergoing severe deformations. A deblurring module for gastroscopy scenario is specifically designed to address the potential blurriness. Experiments have illustrated that proposed method achieves state-of-the-art performance of feature matching and frame interpolation in gastroscopy scenario. Moreover, a large-scale gastroscopy dataset is also constructed for multiple tasks. © 1982-2012 IEEE.

关键词： Feature extraction

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Multi-scale Graph Convolution with Corrective Contrastive Learning for Skeleton-based Action Recognition

Multi-scale Graph Convolution with Corrective Contrastive Le...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Tianming Zhuang Erqiang Zhou Hanwen Zhang Yi Ding Ji Geng Zhen Qin Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

For pursuing accurate skeleton-based action recognition, many existing graph-based approaches deploy the higher-order polynomials of the skeletal adjacency matrix to model the node correlations of distant neighbours. To further capture robust graphical patterns, a novel multi-scale graph convolution operator is proposed, which enables to aggregate multi-scale dependencies and capture long-range joint relationships on human skeleton graph. Additionally, a novel corrective contrastive learning strategy is proposed, which aims to distinguish the representative clues and calibrate the confused action clips in the feature space. Comprehensive experiments validate the effectiveness and superiority of our proposed method over state-of-the-art approaches on three large-scale datasets: NTU RGB+D 60, NTU RGB+D 120, and Kinetics Skeleton 400.

关键词： Correlation Convolution Contrastive learning Speech recognition Benchmark testing Skeleton Polynomials Kinetic theory Speech processing Kernel

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CPSNet: Comprehensive Enhancement Representation for Polyp Segmentation Task

CPSNet: Comprehensive Enhancement Representation for Polyp S...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Cai, Jiati Liu, Xiaogang Yang, Hongjie Ding, Yi Zhong, Ting Qin, Zhen Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Chengdu China Department of Gastroenterology and Hepatology University of Electronic Science and Technology of China Chengdu China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350368741

Accurately segmenting polyp regions in colonoscopy images is crucial for the diagnosis and intervention of colorectal cancer. However, the task of polyp segmentation remains challenging due to the diverse size and shape variations among polyps, their extreme similarity to the background, and frequent rotation of the lens, which further increases the diversity in polyp presentation. To address these challenges effectively, we propose a comprehensive polyp segmentation network (CPSNet). Specifically, we introduce a Comprehensive Spatial Feature Extraction Module (CFEM) that progressively and densely integrates features while forming receptive windows with various shapes. This enables enhanced perception of polyps at manifold sizes and shapes. Additionally, we propose a Fine-grained Region Strengthen Module (FGSM) to supplement uncertain areas around polyps by mitigating background noise interference. In terms of training strategy, we further introduce a Rotation-augmented Constrained Loss (RC Loss), which reinforces consistency constraints on polyp images under multiple rotation angles. Qualitative and quantitative experiments conducted on five public datasets demonstrate both the plug-and-play capability of CFEM as well as the effectiveness and excellence achieved by CPSNet. © 2025 IEEE.

关键词： colonoscopy computer vision polyp segmentation representation enhancement

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Ultrasound-Guided Registration Pseudo-labels for Semi-Supervised Brachial Plexus Segmentation

Ultrasound-Guided Registration Pseudo-Labels for Semi-Superv...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Luo, Jia Zhang, Ze Ding, Yi Wang, Yiqian Zhang, Jian Network and Data Security Key Laboratory of Sichuan Province School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Yibin GREAT Technology Co. Ltd. Yibin644000 China Sichuan Provincial Hospital for Women and Children/Affilated Women and Children's Hospital of Chengdu Medical College Chengdu610054 China

ISBN: (纸本)9798350368741

In semi-supervised medical image segmentation, two main challenges arise. First, the quality of pseudo-labels generated by segmentation networks in data-limited scenarios is often poor, reducing segmentation accuracy. Second, many methods fail to effectively utilize the temporal context in video data. Moreover, the intricate relationships among multiple targets make the conventional teacher-student network consistency loss unsuitable for multi-target tasks, leading to inaccurate feature capture and degraded performance. To address these issues, we propose a registration-based pseudo-label generation strategy that produces pseudo-labels more closely aligned with actual labels. Additionally, we introduce multiple guidance mechanisms into the teacher-student network to ensure correct optimization. Experimental results show that our approach significantly outperforms leading semi-supervised segmentation methods. Specifically, at a 50% label rate, our method improves the Dice coefficient by 6.9% over existing methods. © 2025 IEEE.

关键词： Multi-Object Registration Semi-Supervised Medical Image Segmentation

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EVPIR: Efficient and Verifiable Privacy-Preserving Image Retrieval in Cloud-assisted Internet of Things

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IEEE Internet of Things Journal 2025年

作者： Li, Mingyue Li, Yuntao Du, Ruizhong Jia, Chunfu Shao, Wei Hebei University School of Cyber Security and Computer Baoding071000 China Key Lab on High Trusted Information Sys tem in Hebei Province Hebei China College of Cyber Sciences Nankai university Tianjin300350 China Tianjin Key Laboratory of Network and Data Security Technology Tianjin300350 China Jinan250014 China

With the proliferation of mobile devices and the advancement of cloud computing capabilities, cloud-assisted Internet of Things (IoT) attracts increased attention based on its computational and storage advantages. Upon these conveniences, there also raises privacy concerns that numerous solutions have been proposed to solve it. However, existing methods suffer from challenges such as low retrieval accuracy, inefficiency in large-scale image retrieval, and lack of efficient result verification. In this paper, we propose an Efficient Verifiable Privacy-preserving Image Retrieval scheme (EVPIR). Specifically, we design a hierarchical graph index to significantly enhance retrieval efficiency, which organizes image feature vectors into a multi-level structure, establishing connections between neighboring nodes within each layer and creating a highly structured and efficient retrieval framework. During the retrieval process, we employ greedy search algorithm to navigate these connections and identify the closest neighbors across different levels, which make the proposed multi-level approach reduce the search space at each level, achieving faster and more accurate retrieval. Furthermore, we design an efficient dynamic verifiable framework leveraging Chameleon hash functions and BLS signatures where we utilize Chameleon hash nodes based on Merkle Hash Trees (MHT) to enable dynamic updates of the verification tree and employ BLS signatures to construct multiple verification nodes for effectively shortening the verification path. Finally, security analysis shows that EVPIR can defend various threat models and extensive experiments further demonstrates that EVPIR can improve retrieval and verification efficiency. (Figure presented) © 2014 IEEE.

关键词： Differential privacy

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Bidirectionally Guided Large Language Models for Consumer-Centric Personalized Recommendation

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IEEE Transactions on Consumer Electronics 2025年

作者： Yu, Linfang Xiao, Peng Xu, Liqun Kumari, Saru Alenazi, Mohammed J.F. Industrial Research Institute Chengdu610213 China University of Electronic Science and Technology of China Network and Data Security Key Laboratory of Sichuan Province Chengdu610054 China Chengdu University of Technology School of Computer and Network Security Chengdu China Industrial Research Institute Chengdu610213 China Chaudhary Charan Singh University Department of Mathematics Uttar Pradesh Meerut250004 India Department of Computer Engineering Riyadh11451 Saudi Arabia

With the increasing abundance of consumer electronics, consumers are facing the challenge of information overload, making personalized recommendation systems (RSs) crucial for enhancing consumer experience and promoting sales. Recently, LLM-driven RSs have attracted widespread attention. Nevertheless, the application of LLMs in recommendation still encounters challenges: how to effectively integrate user/item content (CT) and collaborative filtering (CF) information;how to effectively process heterogeneous token sequences;and how to efficiently generate recommendations. Therefore, we propose BGLLM4Rec, a bidirectionally guided LLM for recommendation. Specifically, a bidirectionally guided pre-training strategy is proposed to extend user/item ID tokens into the vocabulary of the pre-trained LLM, and leverage both CF LLM and CT LLM to learn their embedding representations. Meanwhile, a heterogeneous-homogeneous (Het-Hom) prompting strategy is proposed to decompose the input sequence into heterogeneous prompt and homogeneous main text parts, performing language modeling solely on the latter to enhance modeling effectiveness and stability. Finally, a recommendation-specific masked prompting fine-tuning strategy is employed to further refine the pre-trained CF LLM, enabling the efficient generation of multiple recommended items without hallucinations. Extensive experiments conducted on three public datasets have demonstrated that BGLLM4Rec exhibits exceptional performance in recommendation tasks. © 1975-2011 IEEE.

关键词： Collaborative filtering

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Ultrasound-Guided Registration Pseudo-labels for Semi-Supervised Brachial Plexus Segmentation

Ultrasound-Guided Registration Pseudo-Labels for Semi-Superv...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jia Luo Ze Zhang Yi Ding Yiqian Wang Jian Zhang Network and Data Security Key Laboratory of Sichuan Province School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Yibin GREAT Technology Co. Ltd Yibin China Sichuan Provincial Hospital for Women and Children/Affilated Women and Children’s Hospital of Chengdu Medical College Chengdu China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

关键词： Deformable models Image segmentation Accuracy Ultrasonic imaging Deformation Signal processing Brachytherapy Speech processing Optimization Biomedical imaging

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A Lightweight Collaborative Target Recognition Method for Unmanned Aerial Vehicle Cluster

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IEEE Internet of Things Journal 2025年

作者： Cao, Mingsheng Yin, Yiyang Zhang, Li Li, Weizhuang Liu, Ziqiang Zhu, Ruizheng Zhao, Yang University of Electronic Science and Technology of China Network and Data Security Key Laboratory of Sichuan Province School of Information and Software Engineering Chengdu611731 China University of Electronic Science and Technology of China ChengDu Research Institute Chengdu610054 China Nanjing University of Aeronautics and Astronautics School of Aerospace Engineering Nanjing210001 China

This paper aims to explore a reliable target recognition technique for unmanned aerial vehicle (UAV) clusters, and proposes a lightweight collaborative target recognition methods based on multiple viewpoints. In the proposed method, the UAVs are divided into the leaf node UAVs and the head node UAVs. Leaf node UAVs are used for multi-view image acquisition and image feature extraction by utilizing a lightweight feature extraction model. The head node UAVs realize efficient feature fusion from the collected image features by using graph convolutional network and graph coarsening techniques. Based on the above lightweight technologies, the proposed method can realize the efficient and accurate target recognition and reduce the demand for limited computing resources and communication resources in UAV clusters. Experimental results show that, compared with existing multi-view object recognition method, the proposed method has less computing cost and communication overhead while ensuring reliable recognition accuracy. © 2014 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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CPSNet: Comprehensive Enhancement Representation for Polyp Segmentation Task

CPSNet: Comprehensive Enhancement Representation for Polyp S...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jiati Cai Xiaogang Liu Hongjie Yang Yi Ding Ting Zhong Zhen Qin Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Chengdu China Department of Gastroenterology and Hepatology University of Electronic Science and Technology of China Chengdu China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

关键词： Training Manifolds Image segmentation Shape Video sequences Morphology Speech enhancement Signal processing Feature extraction Lenses

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ATEDU-NET: An Attention-Embedded Deep Unet for multi-disease diagnosis in chest X-ray images, breast ultrasound, and retina fundus

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Computers in Biology and Medicine 2025年 186卷 109708-109708页

作者： Ejiyi, Chukwuebuka Joseph Qin, Zhen Agbesi, Victor K. Ejiyi, Makuachukwu Bennedith Chikwendu, Ijeoma A. Bamisile, Oluwatoyosi F. Onyekwere, Favour Ezinne Bamisile, Olusola O. College of Nuclear Technology and Automation Engineering Chengdu University of Technology Sichuan 610059 China Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Sichuan Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Pharmacy Department University of Nigeria Nsukka Enugu State Nsukka Nigeria School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

In image segmentation for medical image analysis, effective upsampling is crucial for recovering spatial information lost during downsampling. This challenge becomes more pronounced when dealing with diverse medical image modalities, which can significantly impact model performance. Plain and standard skip connections, widely used in most models, often fall short of maintaining high segmentation accuracy across different modalities, because essential spatial information transferred from the encoder to the decoder is lost. Inspired by these limitations, the Attention-Embedded Deep UNet (ATEDU-Net) is presented here, an innovative framework designed and tested on diverse medical image modalities, including X-ray, breast ultrasound, and retinal fundus images. ATEDU-Net features a unique architecture that combines progressive context refinement modules (PCRM) and global context modules (GCM) within a U-shaped network structure with Convgroup blocks which facilitate the integration of convolutional operations. This allows ATEDU-Net to autonomously capture rich spatial details and crucial contextual information from input images. The GCM captures essential contextual information for informed decision-making, while the PCRM enhances feature attention, resulting in precise and robust segmentation. The experimentation and analysis demonstrate that ATEDU-Net promises to be a powerful tool for medical professionals, aiding in the early detection of chest-related diseases, accurate localization of breast tumors, and early identification of eye diseases. This, in turn, contributes significantly to the formulation of optimal therapeutic strategies, enhancing patient care and outcomes. The versatility of ATEDU-Net is further highlighted by its ability to analyze medical images across various modalities, making it well-suited for the complex task of medical image analysis in diverse clinical scenarios. © 2025 Elsevier Ltd

关键词： Image segmentation

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