Information Fusion最新文献_第5页

Fusing time- and frequency-domain information for effort-independent lung function evaluation using oscillometry 融合时间和频域信息，使用振荡法进行不依赖于努力的肺功能评估

IF 15.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Information Fusion

Pub Date : 2026-01-11 DOI: 10.1016/j.inffus.2026.104147

Sunxiaohe Li , Dongfang Zhao , Zirui Wang , Hao Zhang , Pang Wu , Zhenfeng Li , Lidong Du , Xianxiang Chen , Hongtao Niu , Xiaopan Li , Jingen Xia , Ting Yang , Peng Wang , Zhen Fang

Current methods for evaluating lung function require substantial patient cooperation and rigorous quality control. In contrast, impulse oscillometry (IOS) is a promising alternative that can measure lung mechanics with minimal patient effort and operational ease. IOS applies pressure oscillations to the airways and analyzes the resulting signals. However, previous studies on IOS have been limited to frequency-domain features derived from its response signals, while neglecting valuable time-domain information. To bridge this gap, we developed a deep learning model that fuses time- and frequency-domain IOS data for lung function evaluation. An internal dataset (2,702 cases) and an external dataset (335 cases) were retrospectively collected for model training and validation. Model performance was first evaluated through ablation studies and then tested across different demographic subgroups. Finally, Grad-CAM was employed to improve model interpretability. Results showed that our model accurately predicted lung function parameters, including FEV₁/FVC (mean absolute errors [MAEs] of 3.78 and 4.33 %), FEV₁ (MAEs of 0.235 and 0.270 L), and FVC (MAEs of 0.264 and 0.315 L), in internal and external validation sets. The model also demonstrated strong performance in respiratory disease prescreening, achieving AUCs of 0.989 and 0.980 with sensitivities of 73.97 % and 71.47 % for detecting airway obstruction, and AUCs of 0.938 and 0.925 with sensitivities of 76.41 % and 66.24 % for classifying four ventilation patterns across the two sets. By fusing time- and frequency-domain IOS data, this study offers a new strategy for pulmonary function evaluation, facilitating more efficient prescreening for pulmonary diseases.

目前评估肺功能的方法需要大量的患者配合和严格的质量控制。相比之下，脉冲振荡测量法（IOS）是一种很有前途的替代方法，它可以以最小的患者努力和操作简便来测量肺部力学。IOS对气道施加压力振荡，并分析产生的信号。然而，以往对IOS的研究仅限于从其响应信号中提取的频域特征，而忽略了宝贵的时域信息。为了弥补这一差距，我们开发了一种深度学习模型，该模型融合了用于肺功能评估的时域和频域IOS数据。回顾性收集内部数据集（2702例）和外部数据集（335例）进行模型训练和验证。首先通过消融研究评估模型的性能，然后在不同的人口亚组中进行测试。最后，采用Grad-CAM提高模型的可解释性。结果表明，该模型能够准确预测肺功能参数，包括FEV1/FVC（平均绝对误差[MAEs]分别为3.78和4.33%）、FEV1（平均绝对误差[MAEs]分别为0.235和0.270 L）和FVC（平均绝对误差[MAEs]分别为0.264和0.315 L）。该模型在呼吸道疾病的预筛查中也表现出较强的性能，对气道阻塞的检测auc分别为0.989和0.980，灵敏度分别为73.97%和71.47%；对两组四种通气方式的分类auc分别为0.938和0.925，灵敏度分别为76.41%和66.24%。通过融合时频域IOS数据，本研究为肺功能评估提供了一种新的策略，有助于更有效地进行肺部疾病的预筛查。

{"title":"Fusing time- and frequency-domain information for effort-independent lung function evaluation using oscillometry","authors":"Sunxiaohe Li , Dongfang Zhao , Zirui Wang , Hao Zhang , Pang Wu , Zhenfeng Li , Lidong Du , Xianxiang Chen , Hongtao Niu , Xiaopan Li , Jingen Xia , Ting Yang , Peng Wang , Zhen Fang","doi":"10.1016/j.inffus.2026.104147","DOIUrl":"10.1016/j.inffus.2026.104147","url":null,"abstract":"<div><div>Current methods for evaluating lung function require substantial patient cooperation and rigorous quality control. In contrast, impulse oscillometry (IOS) is a promising alternative that can measure lung mechanics with minimal patient effort and operational ease. IOS applies pressure oscillations to the airways and analyzes the resulting signals. However, previous studies on IOS have been limited to frequency-domain features derived from its response signals, while neglecting valuable time-domain information. To bridge this gap, we developed a deep learning model that fuses time- and frequency-domain IOS data for lung function evaluation. An internal dataset (2,702 cases) and an external dataset (335 cases) were retrospectively collected for model training and validation. Model performance was first evaluated through ablation studies and then tested across different demographic subgroups. Finally, Grad-CAM was employed to improve model interpretability. Results showed that our model accurately predicted lung function parameters, including FEV<sub>1</sub>/FVC (mean absolute errors [MAEs] of 3.78 and 4.33 %), FEV<sub>1</sub> (MAEs of 0.235 and 0.270 L), and FVC (MAEs of 0.264 and 0.315 L), in internal and external validation sets. The model also demonstrated strong performance in respiratory disease prescreening, achieving AUCs of 0.989 and 0.980 with sensitivities of 73.97 % and 71.47 % for detecting airway obstruction, and AUCs of 0.938 and 0.925 with sensitivities of 76.41 % and 66.24 % for classifying four ventilation patterns across the two sets. By fusing time- and frequency-domain IOS data, this study offers a new strategy for pulmonary function evaluation, facilitating more efficient prescreening for pulmonary diseases.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"130 ","pages":"Article 104147"},"PeriodicalIF":15.5,"publicationDate":"2026-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145957303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MCIVA: A Multi-View Pedestrian Detection Framework with Central Inverse Nearest Neighbor Map and View Adaptive Module 基于中心逆最近邻映射和视图自适应模块的多视图行人检测框架

IF 18.6 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Information Fusion

Pub Date : 2026-01-11 DOI: 10.1016/j.inffus.2026.104142

He Li, Taiyu Liao, Weihang Kong, Xingchen Zhang

Multi-view pedestrian detection is an important task and has many applications in areas such as surveillance and smart cities. Despite the significant performance improvements achieved in recent multi-view pedestrian detection methods, there are still three main challenges for this task. 1) In crowded areas, neighboring connected components may merge in dense regions, resulting in unclear localization of pixel peaks for each pedestrian. 2) The loss functions used in previous multi-view pedestrian detection methods have a high response to the background. 3) The camera parameters have not been fully utilized; they are only used to generate a fixed-value projection matrix. To address these challenges, we propose a novel multi-view pedestrian detection framework with Central Inverse Nearest Neighbor map and View Adaptive Module (MCIVA). A Central Inverse Nearest Neighbor (CINN) map is introduced to generate the ground-truth Probability Occupancy Map (POM) based on annotations, providing more precise location information for each pedestrian. To enhance the model’s attention to local structural information, we propose a local structural similarity loss to reduce the influence of false local maximum in background regions. Moreover, a novel plug-and-pull View Adaptive Module (VAM) is introduced to utilize the camera parameters to generate learnable weights for multi-view features fusion. We evaluate the proposed method on three benchmark datasets, and the results show that the proposed MCIVA significantly improves the quality of prediction map and achieves state-of-the-art performance.

多视角行人检测是一项重要的任务，在监控和智慧城市等领域有着广泛的应用。尽管最近的多视角行人检测方法取得了显著的性能进步，但这项任务仍然存在三个主要挑战。1)在拥挤区域，相邻的连通组件可能在密集区域合并，导致每个行人像素峰值定位不清。2)以往多视角行人检测方法中使用的损失函数对背景的响应较高。3)相机参数没有被充分利用；它们仅用于生成定值投影矩阵。为了解决这些挑战，我们提出了一种新的多视图行人检测框架，该框架具有中心逆最近邻地图和视图自适应模块（MCIVA）。引入中心逆近邻图（Central Inverse Nearest Neighbor， CINN）生成基于标注的地真概率占用图（ground-truth Probability Occupancy map， POM），为每个行人提供更精确的位置信息。为了增强模型对局部结构信息的关注，我们提出了局部结构相似度损失来减少背景区域虚假局部极大值的影响。此外，引入了一种新型的即插即用视图自适应模块（VAM），利用摄像机参数生成可学习的权重，用于多视图特征融合。我们在三个基准数据集上对所提出的方法进行了评估，结果表明所提出的MCIVA方法显著提高了预测图的质量，达到了最先进的性能。

{"title":"MCIVA: A Multi-View Pedestrian Detection Framework with Central Inverse Nearest Neighbor Map and View Adaptive Module","authors":"He Li, Taiyu Liao, Weihang Kong, Xingchen Zhang","doi":"10.1016/j.inffus.2026.104142","DOIUrl":"https://doi.org/10.1016/j.inffus.2026.104142","url":null,"abstract":"Multi-view pedestrian detection is an important task and has many applications in areas such as surveillance and smart cities. Despite the significant performance improvements achieved in recent multi-view pedestrian detection methods, there are still three main challenges for this task. 1) In crowded areas, neighboring connected components may merge in dense regions, resulting in unclear localization of pixel peaks for each pedestrian. 2) The loss functions used in previous multi-view pedestrian detection methods have a high response to the background. 3) The camera parameters have not been fully utilized; they are only used to generate a fixed-value projection matrix. To address these challenges, we propose a novel multi-view pedestrian detection framework with Central Inverse Nearest Neighbor map and View Adaptive Module (<ce:bold>MCIVA</ce:bold>). <ce:italic>A Central Inverse Nearest Neighbor (CINN) map</ce:italic> is introduced to generate the ground-truth Probability Occupancy Map (POM) based on annotations, providing more precise location information for each pedestrian. To enhance the model’s attention to local structural information, we propose <ce:italic>a local structural similarity loss</ce:italic> to reduce the influence of false local maximum in background regions. Moreover, a novel plug-and-pull <ce:italic>View Adaptive Module</ce:italic> (VAM) is introduced to utilize the camera parameters to generate learnable weights for multi-view features fusion. We evaluate the proposed method on three benchmark datasets, and the results show that the proposed MCIVA significantly improves the quality of prediction map and achieves state-of-the-art performance.","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"12 1","pages":""},"PeriodicalIF":18.6,"publicationDate":"2026-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145957305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

OMD: optimal transport-guided multimodal disentangled learning for leptomeningeal metastasis diagnosis OMD：用于小脑膜转移诊断的最佳转运引导的多模态解纠缠学习

IF 15.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Information Fusion

Pub Date : 2026-01-11 DOI: 10.1016/j.inffus.2025.104121

Shengjia Chen , Huihua Hu , Hongfu Zeng , Chenxin Li , Qing Xu , Longfeng Zhang , Haipeng Xu

Leptomeningeal metastasis (LM) diagnosis represents a significant clinical challenge. Existing diagnostic approaches are often limited by their reliance on single-modality data and the inherent difficulties in effectively integrating heterogeneous information from imaging and genomics. To address these challenges, we propose OMD, an Optimal Transport-guided Multimodal Disentangled Learning framework that integrates MRI data with genomic information for enhanced diagnostic accuracy. Our method combines optimal transport-based cross-modal attention to robustly align heterogeneous features, information bottleneck compression to mitigate noise and redundancy, and feature disentanglement to explicitly model shared and modality-specific representations, integrated with hierarchical attention for MRI processing and graph-based cross-modal reasoning. Experimental results show that OMD achieves superior diagnostic accuracy, sensitivity, and specificity on our clinical dataset, substantially outperforming current state-of-the-art methods across all evaluation metrics. The model also provides interpretable insights into the cross-modal biomarkers associated with LM. The proposed OMD framework establishes a new paradigm for multimodal medical diagnosis that effectively addresses the complementary strengths of imaging and genomic data. Beyond its immediate application to LM diagnosis, our approach offers a generalizable methodology for integrating heterogeneous medical data sources while providing clinically relevant interpretability. This work represents an important step toward personalized medicine approaches that combine multiple data modalities for improved diagnostic accuracy and treatment planning.

轻脑膜转移（LM）的诊断是一个重大的临床挑战。现有的诊断方法往往受限于它们对单模态数据的依赖，以及在有效整合来自成像和基因组学的异构信息方面的固有困难。为了应对这些挑战，我们提出了OMD，这是一个优化运输引导的多模态解纠缠学习框架，它将MRI数据与基因组信息集成在一起，以提高诊断准确性。我们的方法结合了基于最佳传输的跨模态关注来稳健地对齐异构特征，信息瓶颈压缩来减轻噪声和冗余，特征解纠缠来明确地建模共享和特定模态表示，结合了MRI处理的分层关注和基于图的跨模态推理。实验结果表明，在我们的临床数据集上，OMD实现了卓越的诊断准确性、敏感性和特异性，在所有评估指标上都大大优于当前最先进的方法。该模型还为与LM相关的跨模态生物标志物提供了可解释的见解。拟议的OMD框架为多模式医学诊断建立了一个新的范例，有效地解决了成像和基因组数据的互补优势。除了直接应用于LM诊断之外，我们的方法还提供了一种可推广的方法，用于整合异构医疗数据源，同时提供临床相关的可解释性。这项工作代表了个性化医疗方法的重要一步，将多种数据模式结合起来，以提高诊断准确性和治疗计划。

{"title":"OMD: optimal transport-guided multimodal disentangled learning for leptomeningeal metastasis diagnosis","authors":"Shengjia Chen , Huihua Hu , Hongfu Zeng , Chenxin Li , Qing Xu , Longfeng Zhang , Haipeng Xu","doi":"10.1016/j.inffus.2025.104121","DOIUrl":"10.1016/j.inffus.2025.104121","url":null,"abstract":"<div><div>Leptomeningeal metastasis (LM) diagnosis represents a significant clinical challenge. Existing diagnostic approaches are often limited by their reliance on single-modality data and the inherent difficulties in effectively integrating heterogeneous information from imaging and genomics. To address these challenges, we propose OMD, an <u>O</u>ptimal Transport-guided <u>M</u>ultimodal <u>D</u>isentangled Learning framework that integrates MRI data with genomic information for enhanced diagnostic accuracy. Our method combines optimal transport-based cross-modal attention to robustly align heterogeneous features, information bottleneck compression to mitigate noise and redundancy, and feature disentanglement to explicitly model shared and modality-specific representations, integrated with hierarchical attention for MRI processing and graph-based cross-modal reasoning. Experimental results show that OMD achieves superior diagnostic accuracy, sensitivity, and specificity on our clinical dataset, substantially outperforming current state-of-the-art methods across all evaluation metrics. The model also provides interpretable insights into the cross-modal biomarkers associated with LM. The proposed OMD framework establishes a new paradigm for multimodal medical diagnosis that effectively addresses the complementary strengths of imaging and genomic data. Beyond its immediate application to LM diagnosis, our approach offers a generalizable methodology for integrating heterogeneous medical data sources while providing clinically relevant interpretability. This work represents an important step toward personalized medicine approaches that combine multiple data modalities for improved diagnostic accuracy and treatment planning.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"130 ","pages":"Article 104121"},"PeriodicalIF":15.5,"publicationDate":"2026-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145957306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Secure Tobit filtering for multi-rate nonlinear systems under multi-node random access protocol: A Paillier encryption-decryption mechanism 多节点随机访问协议下多速率非线性系统的安全Tobit滤波：一种Paillier加解密机制

IF 15.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Information Fusion

Pub Date : 2026-01-10 DOI: 10.1016/j.inffus.2026.104146

Shuo Yang , Raquel Caballero-Águila , Jun Hu , Antonia Oya-Lechuga

In this paper, the secure Tobit filtering (TF) problem is investigated for nonlinear systems subject to measurement censoring under a multi-node random access protocol (MNRAP). A multi-rate sampling framework is considered, which allows the system states and measurement outputs to operate with distinct sampling periods, thus reflecting practical engineering constraints. Furthermore, to mitigate data collisions and improve resource utilization, the MNRAP is adopted to regulate the transmission order of measurement signals over communication networks. In addition, to safeguard the communication confidentiality between the sensor node and the filter, the Paillier encryption-decryption mechanism is incorporated. This protects the transmitted information from being intercepted by unauthorized third parties. This paper concentrates on developing an innovative secure TF scheme that guarantees the existence of an upper bound (UB) on the filtering error second moment. Subsequently, the minimization of the obtained UB is carried out in the trace sense by designing a proper filter gain. Additionally, the uniform boundedness of the filtering error is verified in the mean-square sense by establishing a sufficient criterion. Finally, the efficacy and advantages of the proposed secure TF approach are demonstrated through a simulation example.

研究了在多节点随机访问协议（MNRAP）下，受测量滤波约束的非线性系统的安全Tobit滤波问题。考虑了一个多速率采样框架，它允许系统状态和测量输出在不同的采样周期下运行，从而反映了实际的工程约束。此外，为了减少数据冲突，提高资源利用率，采用MNRAP规范通信网络中测量信号的传输顺序。此外，为了保证传感器节点与滤波器之间通信的机密性，还引入了Paillier加解密机制。这样可以防止传输的信息被未经授权的第三方截获。本文研究了一种保证滤波误差秒矩存在上界的安全TF方案。随后，通过设计适当的滤波器增益，在迹线意义上实现了所得UB的最小化。此外，通过建立一个充分的判据，在均方意义上验证了滤波误差的均匀有界性。最后，通过仿真实例验证了该方法的有效性和优越性。

{"title":"Secure Tobit filtering for multi-rate nonlinear systems under multi-node random access protocol: A Paillier encryption-decryption mechanism","authors":"Shuo Yang , Raquel Caballero-Águila , Jun Hu , Antonia Oya-Lechuga","doi":"10.1016/j.inffus.2026.104146","DOIUrl":"10.1016/j.inffus.2026.104146","url":null,"abstract":"<div><div>In this paper, the secure Tobit filtering (TF) problem is investigated for nonlinear systems subject to measurement censoring under a multi-node random access protocol (MNRAP). A multi-rate sampling framework is considered, which allows the system states and measurement outputs to operate with distinct sampling periods, thus reflecting practical engineering constraints. Furthermore, to mitigate data collisions and improve resource utilization, the MNRAP is adopted to regulate the transmission order of measurement signals over communication networks. In addition, to safeguard the communication confidentiality between the sensor node and the filter, the Paillier encryption-decryption mechanism is incorporated. This protects the transmitted information from being intercepted by unauthorized third parties. This paper concentrates on developing an innovative secure TF scheme that guarantees the existence of an upper bound (UB) on the filtering error second moment. Subsequently, the minimization of the obtained UB is carried out in the trace sense by designing a proper filter gain. Additionally, the uniform boundedness of the filtering error is verified in the mean-square sense by establishing a sufficient criterion. Finally, the efficacy and advantages of the proposed secure TF approach are demonstrated through a simulation example.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"130 ","pages":"Article 104146"},"PeriodicalIF":15.5,"publicationDate":"2026-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145957307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Team collaboration-oriented multi-agent pathfinding and probabilistic verification 面向团队协作的多智能体寻路与概率验证

IF 15.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Information Fusion

Pub Date : 2026-01-10 DOI: 10.1016/j.inffus.2026.104125

Xia Wang , Jun Liu , Chris D. Nugent , Shaobing Xu , Guanfeng Wu

Multi-agent pathfinding and its reliable execution in stochastic environments represent a critical challenge for real-world applications, demanding both the planning of efficient paths and the formal assurance of safe, conflict-free operation. This paper introduces a novel methodology framework to address this dual requirement. To maximize operational efficiency, we introduce a strategy for optimal goal allocation for team collaboration, integrating it with the conflict-based search algorithm to minimize the total move counts required for mission completion. The second component is an integrated verification process grounded in probabilistic model checking. We model the multi-agent path execution process under stochastic uncertainties using a Markov decision process. By leveraging the probabilistic model checker and probabilistic computation tree logic, the framework formally verifies critical safety properties, ensuring conflict-free and deadlock-free path execution. Furthermore, it evaluates the effectiveness of proposed behavioral constraints designed to mitigate stochastic delays, thereby verifying the overall system safety. By fusing multi-agent planning, probabilistic reasoning, and formal logic-based verification, the proposed framework establishes a foundation amenable to natural extension for addressing multi-agent decision-making and uncertainty estimation. Case study results demonstrate that our methodology effectively selects the pathfinding solution with the minimum move count while significantly enhancing overall system safety through these formally verified behavioral constraints.

多智能体寻路及其在随机环境中的可靠执行对现实世界的应用来说是一个关键的挑战，它既要求规划有效的路径，又要求正式保证安全、无冲突的操作。本文介绍了一种新的方法框架来解决这一双重要求。为了使操作效率最大化，我们引入了一种团队协作的最佳目标分配策略，将其与基于冲突的搜索算法相结合，以最小化完成任务所需的总移动次数。第二个组成部分是基于概率模型检查的集成验证过程。利用马尔可夫决策过程对随机不确定性下的多智能体路径执行过程进行建模。通过利用概率模型检查器和概率计算树逻辑，框架正式验证关键安全属性，确保无冲突和无死锁的路径执行。此外，它还评估了旨在减轻随机延迟的行为约束的有效性，从而验证了整个系统的安全性。通过融合多智能体规划、概率推理和基于形式逻辑的验证，该框架为解决多智能体决策和不确定性估计问题建立了一个可自然扩展的基础。案例研究结果表明，我们的方法有效地选择了移动次数最少的寻路解决方案，同时通过这些正式验证的行为约束显著提高了整体系统的安全性。

{"title":"Team collaboration-oriented multi-agent pathfinding and probabilistic verification","authors":"Xia Wang , Jun Liu , Chris D. Nugent , Shaobing Xu , Guanfeng Wu","doi":"10.1016/j.inffus.2026.104125","DOIUrl":"10.1016/j.inffus.2026.104125","url":null,"abstract":"<div><div>Multi-agent pathfinding and its reliable execution in stochastic environments represent a critical challenge for real-world applications, demanding both the planning of efficient paths and the formal assurance of safe, conflict-free operation. This paper introduces a novel methodology framework to address this dual requirement. To maximize operational efficiency, we introduce a strategy for optimal goal allocation for team collaboration, integrating it with the conflict-based search algorithm to minimize the total move counts required for mission completion. The second component is an integrated verification process grounded in probabilistic model checking. We model the multi-agent path execution process under stochastic uncertainties using a Markov decision process. By leveraging the probabilistic model checker and probabilistic computation tree logic, the framework formally verifies critical safety properties, ensuring conflict-free and deadlock-free path execution. Furthermore, it evaluates the effectiveness of proposed behavioral constraints designed to mitigate stochastic delays, thereby verifying the overall system safety. By fusing multi-agent planning, probabilistic reasoning, and formal logic-based verification, the proposed framework establishes a foundation amenable to natural extension for addressing multi-agent decision-making and uncertainty estimation. Case study results demonstrate that our methodology effectively selects the pathfinding solution with the minimum move count while significantly enhancing overall system safety through these formally verified behavioral constraints.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"130 ","pages":"Article 104125"},"PeriodicalIF":15.5,"publicationDate":"2026-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145957309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SHIFT: Enhancing federated learning robustness through client-side backdoor detection SHIFT：通过客户端后门检测增强联邦学习的健壮性

IF 15.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Information Fusion

Pub Date : 2026-01-10 DOI: 10.1016/j.inffus.2026.104144

Kang Wang , Liangliang Wang , Zhiquan Liu , Yiyuan Luo , Kai Zhang , Weiwei Li

Federated Learning (FL) is vulnerable to backdoor attacks, where hidden triggers in model updates can induce malicious behavior on specific inputs, ultimately compromising the reliability of FL. However, existing backdoor detection methods require decryption of locally uploaded encrypted models on the server before further detection can be performed. In this paper, we propose SHIFT containing three parts: transferring the backdoor detection task to the client side to significantly reduce the computational burden on the server; employing client-side code obfuscation to prevent malicious clients from analyzing or bypassing the detection mechanism; and utilizing a dynamic risk level mapping mechanism to adaptively adjust the results of the backdoor detection output. SHIFT can directly detect unencrypted data on the client side. We evaluated the time overhead of SHIFT compared with various backdoor detection schemes based on different encryption methods. Additionally, we assessed its performance in handwritten digit recognition and image classification tasks under single-client and multi-client backdoor attacks, specifically in non-independent and identically distributed (non-IID) scenarios. Experimental results indicate that SHIFT improves backdoor detection efficiency by a factor ranging from 1.28 to 36.65 over existing schemes, while also demonstrating robust performance in detecting and defending against various backdoor attacks, particularly in large-scale, multi-client distributed federated learning systems.

联邦学习（FL）容易受到后门攻击，其中模型更新中的隐藏触发器可能会导致特定输入的恶意行为，最终损害FL的可靠性。然而，现有的后门检测方法需要在服务器上对本地上传的加密模型进行解密，然后才能执行进一步的检测。在本文中，我们提出SHIFT包含三个部分：将后门检测任务转移到客户端，以显着减少服务器的计算负担；采用客户端代码混淆，防止恶意客户端分析或绕过检测机制；并利用动态风险等级映射机制自适应调整后门检测输出结果。SHIFT可以直接检测客户端上未加密的数据。我们评估了SHIFT与基于不同加密方法的各种后门检测方案的时间开销。此外，我们评估了其在单客户端和多客户端后门攻击下手写数字识别和图像分类任务中的性能，特别是在非独立和同分布（non-IID）场景下。实验结果表明，与现有方案相比，SHIFT将后门检测效率提高了1.28到36.65倍，同时在检测和防御各种后门攻击方面也表现出了强大的性能，特别是在大规模、多客户端分布式联邦学习系统中。

{"title":"SHIFT: Enhancing federated learning robustness through client-side backdoor detection","authors":"Kang Wang , Liangliang Wang , Zhiquan Liu , Yiyuan Luo , Kai Zhang , Weiwei Li","doi":"10.1016/j.inffus.2026.104144","DOIUrl":"10.1016/j.inffus.2026.104144","url":null,"abstract":"<div><div>Federated Learning (FL) is vulnerable to backdoor attacks, where hidden triggers in model updates can induce malicious behavior on specific inputs, ultimately compromising the reliability of FL. However, existing backdoor detection methods require decryption of locally uploaded encrypted models on the server before further detection can be performed. In this paper, we propose SHIFT containing three parts: transferring the backdoor detection task to the client side to significantly reduce the computational burden on the server; employing client-side code obfuscation to prevent malicious clients from analyzing or bypassing the detection mechanism; and utilizing a dynamic risk level mapping mechanism to adaptively adjust the results of the backdoor detection output. SHIFT can directly detect unencrypted data on the client side. We evaluated the time overhead of SHIFT compared with various backdoor detection schemes based on different encryption methods. Additionally, we assessed its performance in handwritten digit recognition and image classification tasks under single-client and multi-client backdoor attacks, specifically in non-independent and identically distributed (non-IID) scenarios. Experimental results indicate that SHIFT improves backdoor detection efficiency by a factor ranging from 1.28 to 36.65 over existing schemes, while also demonstrating robust performance in detecting and defending against various backdoor attacks, particularly in large-scale, multi-client distributed federated learning systems.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"130 ","pages":"Article 104144"},"PeriodicalIF":15.5,"publicationDate":"2026-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145957308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Region-based deep metric learning for tackling class overlap in online semi-supervised data stream classification 基于区域的深度度量学习处理在线半监督数据流分类中的类重叠

IF 15.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Information Fusion

Pub Date : 2026-01-09 DOI: 10.1016/j.inffus.2026.104126

Zhonglin Wu , Hongliang Wang , Tongze Zhang , Hongyuan Liu , Jinxia Guo , Qinli Yang , Junming Shao

Class overlap in data streams presents a significant challenge for real-time classification, particularly when confronted with the high dimensionality and evolving distributions inherent in such streams. Traditional classification methods, typically designed for static datasets, struggle to adapt to the dynamic nature of data streams, where both high-dimensional feature spaces and class imbalance exacerbate the complexity of classifying overlapping regions. In this paper, we propose a novel deep metric learning framework specifically tailored to address the challenges of class overlap in high-dimensional data streams. Our approach introduces two key innovations. First, we develop a multi-anchor sample mining mechanism based on neighborhood rough set theory, which partitions the data into non-overlapping and overlapping regions. By utilizing region-specific triplet-margin losses and hinge embedding loss, we construct a more refined discriminative metric space that significantly enhances the separation of overlapping classes. Furthermore, we introduce a dynamic, density-aware real-time label propagation mechanism with class-imbalance compensation. This component integrates real-time distribution estimation with a nonlinear adaptive threshold controller, enabling dual adaptivity: (1) dynamically re-weighting density contributions via inverse-frequency scaling to mitigate the dominance of majority classes and (2) adjusting threshold boundaries for frequent classes while relaxing propagation criteria for rare classes through nonlinear adjustments. Empirical evaluations on both synthetic and real-world data streams demonstrate that our method not only improves balanced accuracy but also enhances robustness in the presence of class overlap and class imbalance, outperforming state-of-the-art techniques.

数据流中的类重叠对实时分类提出了重大挑战，特别是当面对此类流中固有的高维和不断发展的分布时。传统的分类方法通常是针对静态数据集设计的，难以适应数据流的动态特性，高维特征空间和类不平衡加剧了重叠区域分类的复杂性。在本文中，我们提出了一种新的深度度量学习框架，专门用于解决高维数据流中类重叠的挑战。我们的方法引入了两个关键的创新。首先，提出了一种基于邻域粗糙集理论的多锚点样本挖掘机制，将数据划分为非重叠和重叠区域；通过利用特定区域的三重边缘损失和铰链嵌入损失，我们构建了一个更精细的判别度量空间，显著增强了重叠类的分离。此外，我们还引入了一种具有类不平衡补偿的动态、密度感知的实时标签传播机制。该组件将实时分布估计与非线性自适应阈值控制器集成在一起，实现了双重自适应：(1)通过反频率缩放动态地重新加权密度贡献，以减轻多数类的优势地位；(2)通过非线性调整调整频繁类的阈值边界，同时放宽罕见类的传播标准。对合成数据流和真实数据流的实证评估表明，我们的方法不仅提高了平衡的准确性，而且在类重叠和类不平衡的情况下增强了鲁棒性，优于最先进的技术。

{"title":"Region-based deep metric learning for tackling class overlap in online semi-supervised data stream classification","authors":"Zhonglin Wu , Hongliang Wang , Tongze Zhang , Hongyuan Liu , Jinxia Guo , Qinli Yang , Junming Shao","doi":"10.1016/j.inffus.2026.104126","DOIUrl":"10.1016/j.inffus.2026.104126","url":null,"abstract":"<div><div>Class overlap in data streams presents a significant challenge for real-time classification, particularly when confronted with the high dimensionality and evolving distributions inherent in such streams. Traditional classification methods, typically designed for static datasets, struggle to adapt to the dynamic nature of data streams, where both high-dimensional feature spaces and class imbalance exacerbate the complexity of classifying overlapping regions. In this paper, we propose a novel deep metric learning framework specifically tailored to address the challenges of class overlap in high-dimensional data streams. Our approach introduces two key innovations. First, we develop a multi-anchor sample mining mechanism based on neighborhood rough set theory, which partitions the data into non-overlapping and overlapping regions. By utilizing region-specific triplet-margin losses and hinge embedding loss, we construct a more refined discriminative metric space that significantly enhances the separation of overlapping classes. Furthermore, we introduce a dynamic, density-aware real-time label propagation mechanism with class-imbalance compensation. This component integrates real-time distribution estimation with a nonlinear adaptive threshold controller, enabling dual adaptivity: (1) dynamically re-weighting density contributions via inverse-frequency scaling to mitigate the dominance of majority classes and (2) adjusting threshold boundaries for frequent classes while relaxing propagation criteria for rare classes through nonlinear adjustments. Empirical evaluations on both synthetic and real-world data streams demonstrate that our method not only improves balanced accuracy but also enhances robustness in the presence of class overlap and class imbalance, outperforming state-of-the-art techniques.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"130 ","pages":"Article 104126"},"PeriodicalIF":15.5,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145957310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Tokenized EEG signals with large language models for epilepsy detection via multimodal information fusion 基于多模态信息融合的大语言模型脑电信号标记化检测

IF 15.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Information Fusion

Pub Date : 2026-01-07 DOI: 10.1016/j.inffus.2026.104128

XingchiChen , Fushen Xie , Fa Zhu , Shuanglong Zhang , Xiaoyang Lu , Qing Li , Rong Chen , Dazhou Li , David Camacho

The detection of epileptic seizures using multi-sensor EEG signals is a challenging task due to the inherent complexity of the signals, the variability in sensor configurations, and the difficulty in distinguishing the weak inter-class difference. To address these challenges, we propose a novel multimodal information fusion framework that integrates a large language model (LLM) and a multimodal EEG feature tokenization method for enhanced epilepsy detection. This paper adopts a multimodal feature extraction (MFE) method to effectively generate multimodal feature representations from EEG signals and extract different feature representations of EEG signals from different signal domains. In addition, we design a multimodal EEG feature tokenization method to tokenize EEG signal features and fuse the semantic information, solving the problem of fusing epileptic EEG features with semantic information in prompt words. We use the powerful reasoning and pattern recognition capabilities of pre-trained LLMs to accurately and robustly detect epileptic events. The proposed method is evaluated on a public dataset. Extensive experimental results show that the proposed method outperforms the current comparative methods in multiple performance indicators.

由于信号固有的复杂性、传感器配置的可变性以及难以区分微弱的类间差异，利用多传感器脑电图信号检测癫痫发作是一项具有挑战性的任务。为了解决这些挑战，我们提出了一种新的多模态信息融合框架，该框架集成了大语言模型（LLM）和多模态EEG特征标记化方法，以增强癫痫检测。本文采用多模态特征提取（multimodal feature extraction， MFE）方法，有效地从脑电信号中生成多模态特征表示，并从不同的信号域中提取脑电信号的不同特征表示。此外，我们设计了一种多模态脑电信号特征标记方法，对脑电信号特征进行标记并融合语义信息，解决了癫痫脑电信号特征与提示词语义信息的融合问题。我们使用预训练llm的强大推理和模式识别能力来准确和稳健地检测癫痫事件。在一个公共数据集上对该方法进行了评估。大量的实验结果表明，该方法在多个性能指标上优于现有的比较方法。

{"title":"Tokenized EEG signals with large language models for epilepsy detection via multimodal information fusion","authors":"XingchiChen , Fushen Xie , Fa Zhu , Shuanglong Zhang , Xiaoyang Lu , Qing Li , Rong Chen , Dazhou Li , David Camacho","doi":"10.1016/j.inffus.2026.104128","DOIUrl":"10.1016/j.inffus.2026.104128","url":null,"abstract":"<div><div>The detection of epileptic seizures using multi-sensor EEG signals is a challenging task due to the inherent complexity of the signals, the variability in sensor configurations, and the difficulty in distinguishing the weak inter-class difference. To address these challenges, we propose a novel multimodal information fusion framework that integrates a large language model (LLM) and a multimodal EEG feature tokenization method for enhanced epilepsy detection. This paper adopts a multimodal feature extraction (MFE) method to effectively generate multimodal feature representations from EEG signals and extract different feature representations of EEG signals from different signal domains. In addition, we design a multimodal EEG feature tokenization method to tokenize EEG signal features and fuse the semantic information, solving the problem of fusing epileptic EEG features with semantic information in prompt words. We use the powerful reasoning and pattern recognition capabilities of pre-trained LLMs to accurately and robustly detect epileptic events. The proposed method is evaluated on a public dataset. Extensive experimental results show that the proposed method outperforms the current comparative methods in multiple performance indicators.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"131 ","pages":"Article 104128"},"PeriodicalIF":15.5,"publicationDate":"2026-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146006527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SG-DGLF: A similarity-guided dual-graph learning framework SG-DGLF：一个相似引导的双图学习框架

IF 15.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Information Fusion

Pub Date : 2026-01-07 DOI: 10.1016/j.inffus.2026.104127

Menglin Yu , Shuxia Lu , Jiacheng Cong

Graph neural networks (GNNs) perform exceptionally well in node classification, but graph neural networks face severe challenges when dealing with imbalanced node classification. On the one hand, the model is prone to overfitting due to the small number of minority class samples. GNN’s message passing mechanism amplifies this problem, causing the model to overfit specific features and local neighborhood structures of minority class nodes rather than learning general patterns, resulting in poor generalization ability. On the other hand, the scarcity of samples leads to high variance in model training. Model performance is highly dependent on specific training samples and local graph structures, and is extremely sensitive to data partitioning, ultimately resulting in severe performance fluctuations and unstable results. In this work, to address the issues of minority class overfitting and high model variance faced by GNNs in imbalanced scenarios, we propose the dual-graph framework, A similarity-Guided Dual-Graph Learning Framework (SG-DGLF). To address the problem of overfitting for minority classes, the framework introduces a dynamic threshold random capture mechanism based on similarity, which supplements minority class samples by generating pseudo labels. Secondly, we leverage graph diffusion-based propagation and random edge dropping strategy to create new graphs, thereby increasing node diversity to alleviate the problem of excessive model variance. Empirically, SG-DGLF significantly outperforms advanced baseline methods on multiple imbalanced datasets. This validates the effectiveness of our framework in mitigating the problems of overfitting minority classes and high model variance.

图神经网络（Graph neural network, gnn）在节点分类方面表现优异，但在处理不平衡节点分类时面临严峻挑战。一方面，由于少数类样本数量较少，模型容易出现过拟合。GNN的消息传递机制放大了这一问题，导致模型过度拟合少数类节点的特定特征和局部邻域结构，而不是学习一般模式，导致泛化能力差。另一方面，样本的稀缺性导致模型训练的方差很大。模型性能高度依赖于特定的训练样本和局部图结构，对数据划分极其敏感，最终导致性能波动严重，结果不稳定。在这项工作中，为了解决gnn在不平衡场景下面临的少数类过拟合和高模型方差问题，我们提出了双图框架，即相似度引导的双图学习框架（SG-DGLF）。为了解决少数类的过拟合问题，该框架引入了基于相似性的动态阈值随机捕获机制，该机制通过生成伪标签来补充少数类样本。其次，我们利用基于图扩散的传播和随机丢边策略来创建新图，从而增加节点多样性，以缓解模型方差过大的问题。从经验上看，SG-DGLF在多个不平衡数据集上显著优于先进的基线方法。这验证了我们的框架在缓解过拟合少数类和高模型方差问题方面的有效性。

{"title":"SG-DGLF: A similarity-guided dual-graph learning framework","authors":"Menglin Yu , Shuxia Lu , Jiacheng Cong","doi":"10.1016/j.inffus.2026.104127","DOIUrl":"10.1016/j.inffus.2026.104127","url":null,"abstract":"<div><div>Graph neural networks (GNNs) perform exceptionally well in node classification, but graph neural networks face severe challenges when dealing with imbalanced node classification. On the one hand, the model is prone to overfitting due to the small number of minority class samples. GNN’s message passing mechanism amplifies this problem, causing the model to overfit specific features and local neighborhood structures of minority class nodes rather than learning general patterns, resulting in poor generalization ability. On the other hand, the scarcity of samples leads to high variance in model training. Model performance is highly dependent on specific training samples and local graph structures, and is extremely sensitive to data partitioning, ultimately resulting in severe performance fluctuations and unstable results. In this work, to address the issues of minority class overfitting and high model variance faced by GNNs in imbalanced scenarios, we propose the dual-graph framework, A similarity-Guided Dual-Graph Learning Framework (SG-DGLF). To address the problem of overfitting for minority classes, the framework introduces a dynamic threshold random capture mechanism based on similarity, which supplements minority class samples by generating pseudo labels. Secondly, we leverage graph diffusion-based propagation and random edge dropping strategy to create new graphs, thereby increasing node diversity to alleviate the problem of excessive model variance. Empirically, SG-DGLF significantly outperforms advanced baseline methods on multiple imbalanced datasets. This validates the effectiveness of our framework in mitigating the problems of overfitting minority classes and high model variance.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"130 ","pages":"Article 104127"},"PeriodicalIF":15.5,"publicationDate":"2026-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145939897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

WDASR: A wavelet-based deformable attention network for cardiac cine MRI super-resolution with spatiotemporal motion modeling WDASR：一种基于小波的可变形注意网络，用于心脏电影MRI超分辨率的时空运动建模

IF 15.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Information Fusion

Pub Date : 2026-01-06 DOI: 10.1016/j.inffus.2025.104116

Jun Lyu , Xunkang Zhao , Jing Qin , Chengyan Wang

Cardiac cine MRI is the clinical gold standard for dynamic cardiac assessment, but reducing k-space sampling to accelerate acquisition results in low-resolution images that fail to depict fine anatomical details. Existing super-resolution methods struggle to preserve spatial details and temporal coherence due to limitations in handling non-rigid cardiac deformations and lossy feature downsampling. This paper proposes a Wavelet-based Deformable Attention Super-Resolution Network (WDASR) that addresses these limitations through two key innovations. First, a Frequency Subband Adaptive Alignment (FSAA) module applies deformable convolution to wavelet-decomposed frequency subbands, enabling lossless downsampling that prevents offset over-shifting and allows targeted alignment across neighboring and remote frames. Second, a Cross-Resolution Wavelet Attention (CRWA) module uses temporally-aggregated frequency subbands as low-resolution keys and values, and the current frame as high-resolution query, reducing computational complexity by 75% while effectively integrating multi-scale spatiotemporal information for enhanced texture representation. A bidirectional recurrent mechanism further propagates the enhanced features to maintain temporal consistency. Experiments on public and private datasets demonstrate that WDASR achieves 4 ×  super-resolution with state-of-the-art performance and potential for clinical application.

心脏电影MRI是动态心脏评估的临床金标准，但减少k空间采样以加速采集会导致低分辨率图像无法描绘精细的解剖细节。由于处理非刚性心脏变形和有损特征下采样的局限性，现有的超分辨率方法难以保持空间细节和时间相干性。本文提出了一种基于小波的可变形注意力超分辨率网络（WDASR），通过两个关键创新解决了这些限制。首先，频率子带自适应对准（FSAA）模块对小波分解的频率子带进行可变形卷积，实现无损下采样，防止偏移过移，并允许在相邻帧和远程帧之间进行目标对准。其次，交叉分辨率小波注意（Cross-Resolution Wavelet Attention， CRWA）模块采用时间聚合的频率子带作为低分辨率键和值，当前帧作为高分辨率查询，在有效整合多尺度时空信息的同时，将计算复杂度降低了75%，增强了纹理表征。双向循环机制进一步传播增强的特征以保持时间一致性。在公共和私有数据集上的实验表明，WDASR达到了4 × 超分辨率，具有最先进的性能和临床应用潜力。

{"title":"WDASR: A wavelet-based deformable attention network for cardiac cine MRI super-resolution with spatiotemporal motion modeling","authors":"Jun Lyu , Xunkang Zhao , Jing Qin , Chengyan Wang","doi":"10.1016/j.inffus.2025.104116","DOIUrl":"10.1016/j.inffus.2025.104116","url":null,"abstract":"<div><div>Cardiac cine MRI is the clinical gold standard for dynamic cardiac assessment, but reducing k-space sampling to accelerate acquisition results in low-resolution images that fail to depict fine anatomical details. Existing super-resolution methods struggle to preserve spatial details and temporal coherence due to limitations in handling non-rigid cardiac deformations and lossy feature downsampling. This paper proposes a Wavelet-based Deformable Attention Super-Resolution Network (WDASR) that addresses these limitations through two key innovations. First, a Frequency Subband Adaptive Alignment (FSAA) module applies deformable convolution to wavelet-decomposed frequency subbands, enabling lossless downsampling that prevents offset over-shifting and allows targeted alignment across neighboring and remote frames. Second, a Cross-Resolution Wavelet Attention (CRWA) module uses temporally-aggregated frequency subbands as low-resolution keys and values, and the current frame as high-resolution query, reducing computational complexity by 75% while effectively integrating multi-scale spatiotemporal information for enhanced texture representation. A bidirectional recurrent mechanism further propagates the enhanced features to maintain temporal consistency. Experiments on public and private datasets demonstrate that WDASR achieves 4 ×  super-resolution with state-of-the-art performance and potential for clinical application.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"130 ","pages":"Article 104116"},"PeriodicalIF":15.5,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0