Information Fusion最新文献_第9页

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

Information Fusion Pub Date : 2026-06-01 Epub Date: 2026-01-07 DOI: 10.1016/j.inffus.2026.104127

Menglin Yu , Shuxia Lu , Jiacheng Cong

{"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":"2026-06-01","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

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

Information Fusion Pub Date : 2026-06-01 Epub 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

{"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":"2026-06-01","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

Dimensional compensation for small-sample and small-size insulator burn mark via RGB-point cloud fusion in power grid inspection 基于rgb点云融合的电网检测中小样本小尺寸绝缘子烧痕尺寸补偿

IF 15.5 1区计算机科学

Information Fusion Pub Date : 2026-06-01 Epub Date: 2025-12-30 DOI: 10.1016/j.inffus.2025.104105

Junqiu Tang , Zhikang Yuan , Zixiang Wei , Shuojie Gao , Changyong Shen

{"title":"Dimensional compensation for small-sample and small-size insulator burn mark via RGB-point cloud fusion in power grid inspection","authors":"Junqiu Tang , Zhikang Yuan , Zixiang Wei , Shuojie Gao , Changyong Shen","doi":"10.1016/j.inffus.2025.104105","DOIUrl":"10.1016/j.inffus.2025.104105","url":null,"abstract":"<div><div>To address the challenge of scarce burn mark samples in power infrastructure inspection, we introduce the Insulator Burn Mark RGB-Point Cloud (IBMR) dataset, the first publicly available benchmark featuring RGB-point clouds with pixel-level annotations for both insulators and burn marks. To tackle the critical issue of severe class imbalance caused by the vast number of background points and the small size of burn marks, we propose a novel two-stage RGB-point cloud segmentation framework. This framework integrates DCCU-Sampling, an innovative downsampling algorithm that effectively suppresses background points while preserving critical structures of the targets, and BB-Backtracking, a geometric recovery method that reconstructs fine-grained burn mark details lost during downsampling process. Experimental results validate the framework’s effectiveness, achieving 81.21% mIoU with 32 training samples and 68.37% mIoU with only 14 samples. The dataset is publicly available at <span><span>https://huggingface.co/datasets/Junqiu-Tang/IBMR</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"130 ","pages":"Article 104105"},"PeriodicalIF":15.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145885637","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

Few-shot harmful meme detection via self-adaption mixture-of-experts 基于自适应混合专家的少量有害模因检测

IF 15.5 1区计算机科学

Information Fusion Pub Date : 2026-06-01 Epub Date: 2026-01-03 DOI: 10.1016/j.inffus.2026.104122

Zou Li , Jinzhi Liao , Jiting Li , Ji Wang , Xiang Zhao

{"title":"Few-shot harmful meme detection via self-adaption mixture-of-experts","authors":"Zou Li , Jinzhi Liao , Jiting Li , Ji Wang , Xiang Zhao","doi":"10.1016/j.inffus.2026.104122","DOIUrl":"10.1016/j.inffus.2026.104122","url":null,"abstract":"<div><div>The automatic detection of harmful memes is essential for healthy online ecosystems but remains challenging due to the intricate interaction between visual and textual elements. Recently, the remarkable capabilities of multimodal large language models (MLLMs) have significantly enhanced the detection performance, yet scarce labeled data still limits their effectiveness. Although pioneering few-shot studies have explored this regime, they merely leverage surface-level capabilities while ignoring deeper complexities. To approach the core of the problem, we identify its notorious challenges: (1) heterogeneous multimodal features are complex and may exhibit negative correlations; (2) the semantic patterns underlying single modal are hard to uncover; and (3) the insufficient training samples render models more reliant on commonsense. To address the challenges, we propose a structural self-adaption mixture-of-experts framework (SSMoE) for few-shot harmful meme detection, including universal and specialized experts to foster more effective knowledge sharing, modal synergy, and expert specialization within the MLLM structure. Specifically, SSMoE integrates four novel components: (1) Semantic Data Clustering module aims to partition heterogeneous source data and mitigate negative transfer; (2) Targeted Prompt Injection module aims to employ a teacher model for providing cluster-specific external guidance; (3) Asymmetric Expert Specialization module aims to introduce shared and specialized experts for efficient parameter adaptation and knowledge specialization; and (4) Cluster-conditioned Routing module aims to dynamically direct inputs to the most relevant expert pathway based on semantic cluster identity. Extensive experiments on three benchmark datasets (FHM, MAMI, HarM) demonstrate that SSMoE significantly outperforms state-of-the-art baseline methods, particularly in extremely low-data scenarios.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"130 ","pages":"Article 104122"},"PeriodicalIF":15.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145894680","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

EPSO-net: A multi-objective evolutionary neural architecture search with PSO-guided mutation fusion for explainable brain tumor segmentation EPSO-Net：基于pso引导的突变融合的多目标进化神经结构搜索，用于可解释的脑肿瘤分割

IF 15.5 1区计算机科学

Information Fusion Pub Date : 2026-06-01 Epub Date: 2026-01-03 DOI: 10.1016/j.inffus.2025.104119

Farhana Yasmin , Yu Xue , Mahade Hasan , Ghulam Muhammad

{"title":"EPSO-net: A multi-objective evolutionary neural architecture search with PSO-guided mutation fusion for explainable brain tumor segmentation","authors":"Farhana Yasmin , Yu Xue , Mahade Hasan , Ghulam Muhammad","doi":"10.1016/j.inffus.2025.104119","DOIUrl":"10.1016/j.inffus.2025.104119","url":null,"abstract":"<div><div>Accurate brain tumor segmentation from magnetic resonance imaging (MRI) remains a significant challenge due to early loss of spatial detail, inadequate contextual representation, and ineffective decoder fusion. In this paper, we propose EPSO-Net, a multi-objective evolutionary neural architecture search (NAS) framework that integrates three specialized modules: UTSA for preserving spatial encoding and enhancing low-level feature representation, Astra for capturing semantic abstraction and multi-scale context, and Revo for improving decoder refinement through attention-guided fusion of feature maps. These modules work synergistically within a flexible modular 3D search space, enabling dynamic architecture optimization during the evolutionary process. EPSO-Net utilizes a particle swarm optimization (PSO)-guided mutation fusion mechanism that enables efficient exploration of the search space, adjusting mutation behavior based on performance feedback. To the best of our knowledge, this is the first multi-objective evolutionary NAS framework employing PSO-guided mutation fusion to adapt mutation strategies, driving the search towards optimal solutions in a resource-efficient manner. Experiments on the BraTS 2021, BraTS 2020, and MSD Brain Tumor datasets demonstrate that EPSO-Net outperforms nine state-of-the-art methods, achieving high dice similarity coefficients (DSC) of 93.89%, 95.02%, and 91.25%, low Hausdorff distance (HD95) of 1.14 mm, 1.02 mm, and 1.44 mm, and strong Grad-CAM IoU (GIoU) of 89.32%, 90.12%, and 85.68%, respectively. EPSO-Net also demonstrates reliable generalization to the CHAOS, PROMISE12, and ACDC datasets. Furthermore, it significantly reduces model complexity, lowers FLOPS, accelerates inference, and enhances interpretability. The full code will be publicly available at: <span><span>https://github.com/Farhana005/EPSO-Net</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"130 ","pages":"Article 104119"},"PeriodicalIF":15.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145894686","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

Vision-language models for person re-identification: a survey and outlook 人物再识别的视觉语言模型：综述与展望

IF 15.5 1区计算机科学

Information Fusion Pub Date : 2026-06-01 Epub Date: 2025-12-24 DOI: 10.1016/j.inffus.2025.104095

Guorong Lin , Wei-Shi Zheng , Zuoyong Li , Yao Lu , Xiaowen Ma , Zhenhua Huang

{"title":"Vision-language models for person re-identification: a survey and outlook","authors":"Guorong Lin , Wei-Shi Zheng , Zuoyong Li , Yao Lu , Xiaowen Ma , Zhenhua Huang","doi":"10.1016/j.inffus.2025.104095","DOIUrl":"10.1016/j.inffus.2025.104095","url":null,"abstract":"<div><div>Person re-identification (ReID) is a crucial task aimed at retrieving individuals of interest across multiple non-overlapping cameras. Previous methods typically rely on pre-trained visual models as backbones, which are then fine-tuned on person ReID datasets to extract discriminative features. However, due to the lack of semantic alignment between visual and textual modalities in pre-trained visual models, these methods face challenges in effectively leveraging the relationships between these modalities for ReID tasks. In recent years, Vision-Language Models (VLMs) have gained significant attention due to their ability to capture rich correlations between visual and linguistic information. Inspired by this potential, numerous researchers have proposed a series of VLM-based methods to address the diverse challenges in person ReID. This paper provides a systematic review of VLMs for person ReID. Specifically, we provide a comprehensive overview of commonly used VLM frameworks and fine-tuning strategies, while offering an in-depth analysis of the advantages of VLMs in tackling person ReID tasks. Building on this, we further provide an extensive analysis of existing VLM-based person ReID methods. Based on the modalities and learning approaches involved in the person ReID, we categorize existing VLM-based methods into five main approaches: image-based, video-based, cross-modal, multi-scene, and unsupervised person ReID methods. Finally, we outline the key research challenges and potential directions for future studies in the application of VLMs to person ReID. We believe this review will provide valuable insights and serve as an essential reference for researchers working in this field.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"130 ","pages":"Article 104095"},"PeriodicalIF":15.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145822971","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

Style-augmented large-scale vision model with domain-generalized knowledge fusion for anomaly detection in powder bed additive manufacturing 基于领域广义知识融合的风格增强大尺度视觉模型用于粉末床增材制造异常检测

IF 15.5 1区计算机科学

Information Fusion Pub Date : 2026-06-01 Epub Date: 2025-12-30 DOI: 10.1016/j.inffus.2025.104108

Kang Wang , Xuan Liang , Jinghua Xu , Shuyou Zhang , Jianrong Tan

{"title":"Style-augmented large-scale vision model with domain-generalized knowledge fusion for anomaly detection in powder bed additive manufacturing","authors":"Kang Wang , Xuan Liang , Jinghua Xu , Shuyou Zhang , Jianrong Tan","doi":"10.1016/j.inffus.2025.104108","DOIUrl":"10.1016/j.inffus.2025.104108","url":null,"abstract":"<div><div>Metal Additive Manufacturing (AM) has revolutionized the production of complex parts across various industries, yet ensuring consistent quality remains a significant challenge. This study addresses the critical problem of reliable and efficient anomaly detection in metal AM processes, which is essential for maintaining product quality and reducing costly post-production inspections. In this study, we propose a novel full-life-cycle generalization method, namely style-augmented large-scale vision model (SLVM), for anomaly detection in metal additive manufacturing. Our approach leverages the power of large-scale vision models and incorporates style-based augmentation techniques to enhance the detection of anomalies in AM processes. A pre-trained large-scale vision model serves as the backbone in SLVM, providing robust feature extraction capabilities essential for capturing intricate details in AM images. Building upon this foundation, a style augmentation module generates diverse stylized versions of input images, significantly improving the model’s generalization across different AM processes and materials. An anomaly detection head utilizes these style-augmented features to effectively identify and localize defects, completing the comprehensive approach to AM quality control. We evaluate our SLVM on multiple metal AM datasets, including laser powder bed fusion and binder jetting processes, demonstrating its superior performance compared to existing state-of-the-art methods. Our experiments show that SLVM achieves higher detection accuracy, better generalization across different AM processes, and improved robustness to variations in part geometry and material properties. The proposed SLVM exhibits a promising solution for enhancing quality control in metal AM, potentially reducing the need for costly post-production inspections and improving overall manufacturing efficiency.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"130 ","pages":"Article 104108"},"PeriodicalIF":15.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145885638","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

Multi-source information fusion through tucker tensor decomposition-based transfer learning for handwriting-Based Alzheimer's disease detection 基于tucker张量分解的多源信息融合迁移学习用于手写阿尔茨海默病检测

IF 15.5 1区计算机科学

Information Fusion Pub Date : 2026-06-01 Epub Date: 2025-12-30 DOI: 10.1016/j.inffus.2025.104112

Yao Yao , Zhuoxi Yu , Dehui Wang , Chengzhe Wang , Congting Sun

{"title":"Multi-source information fusion through tucker tensor decomposition-based transfer learning for handwriting-Based Alzheimer's disease detection","authors":"Yao Yao , Zhuoxi Yu , Dehui Wang , Chengzhe Wang , Congting Sun","doi":"10.1016/j.inffus.2025.104112","DOIUrl":"10.1016/j.inffus.2025.104112","url":null,"abstract":"<div><div>With Alzheimer’s disease affecting approximately 50 million people globally, early detection has emerged as a critical public health priority in aging societies. This paper proposes a novel multi-level information fusion framework for handwriting-based Alzheimer’s disease detection, addressing the fundamental challenges of data scarcity and high-dimensional feature representation. Our approach integrates: (1) structural fusion through tensor representation preserving the multi-dimensional nature of handwriting data, (2) feature-level fusion via Tucker decomposition achieving 80% parameter reduction while maintaining discriminative information, (3) knowledge fusion through our proposed transferable source domain detection algorithm that selectively integrates relevant knowledge from related domains, and (4) decision-level fusion with a two-stage transfer-debias mechanism that mitigates negative transfer risks. Experiments on the DARWIN dataset demonstrate that our transfer learning approach achieves 93.33% accuracy and 99.10% sensitivity, substantially outperforming existing handwriting-based AD detection methods (best reported: 88.29% accuracy, 90.28% sensitivity). The framework exhibits exceptional robustness in small sample scenarios, maintaining 87.50% accuracy with just 10% of the training data. Our comprehensive analysis reveals kinematic features with an importance score of 35.3%, while temporal features collectively contribute 25.7%—among which total time (9.4%) emerges as a key marker within the temporal category. The proposed framework presents a promising non-invasive approach for early Alzheimer’s detection in aging populations, with the potential to facilitate earlier intervention and substantial healthcare cost reductions.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"130 ","pages":"Article 104112"},"PeriodicalIF":15.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145885639","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

Internet meme on social media: A comprehensive review and new perspectives 社交媒体中的网络模因：综合回顾与新视角

IF 15.5 1区计算机科学

Information Fusion Pub Date : 2026-06-01 Epub Date: 2025-12-29 DOI: 10.1016/j.inffus.2025.104102

Bingbing Wang , Jingjie Lin , Zhixin Bai , Zihan Wang , Shengzhe Sun , Zhengda Jin , Zhiyuan Wen , Geng Tu , Jing Li , Erik Cambria , Ruifeng Xu

{"title":"Internet meme on social media: A comprehensive review and new perspectives","authors":"Bingbing Wang , Jingjie Lin , Zhixin Bai , Zihan Wang , Shengzhe Sun , Zhengda Jin , Zhiyuan Wen , Geng Tu , Jing Li , Erik Cambria , Ruifeng Xu","doi":"10.1016/j.inffus.2025.104102","DOIUrl":"10.1016/j.inffus.2025.104102","url":null,"abstract":"<div><div>Internet memes have become a dominant yet complex form of online communication, spurring a rapid growth of computational research. However, existing surveys remain largely confined to narrow classification tasks and fail to reflect the paradigm shift introduced by Multimodal Large Language Models (MLLMs). To address this gap, we introduce the TriR Framework, comprising Redefinition, Reconsolidation, and Revolution. Within this framework, we redefine the research scope through a taxonomy of higher-order cognitive tasks for meme comprehension, reconsolidate fragmented methodological progress around the unique capabilities of MLLMs, and articulate a trajectory that highlights key challenges and opportunities for advancing compositional and inferential modeling. By offering this structured perspective, the survey anchors the current state of the field while providing a systematic guide for its future development, fostering research that is computationally rigorous, empirically grounded, and ethically responsible.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"130 ","pages":"Article 104102"},"PeriodicalIF":15.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145939826","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

SymUnet-DynCFC: Multimodal MRI fusion for robust cartilage segmentation and clinically confirmed moderate-to-severe KOA diagnosis SymUnet-DynCFC：多模态MRI融合稳健软骨分割和临床证实的中重度KOA诊断

IF 15.5 1区计算机科学

Information Fusion Pub Date : 2026-06-01 Epub Date: 2026-01-12 DOI: 10.1016/j.inffus.2026.104145

Li Li , Jianbing Ma , Beiji Zou , Hao Xu , Shenghui Liao , Wenyi Xiong , Liqiang Zhi

{"title":"SymUnet-DynCFC: Multimodal MRI fusion for robust cartilage segmentation and clinically confirmed moderate-to-severe KOA diagnosis","authors":"Li Li , Jianbing Ma , Beiji Zou , Hao Xu , Shenghui Liao , Wenyi Xiong , Liqiang Zhi","doi":"10.1016/j.inffus.2026.104145","DOIUrl":"10.1016/j.inffus.2026.104145","url":null,"abstract":"<div><div>Knee osteoarthritis (KOA) is a globally prevalent degenerative joint disorder. A central challenge in its automated diagnosis is the efficient fusion of multimodal MRI data. This fusion aims to enhance the accuracy and generalizability of clinical cartilage segmentation, while simultaneously minimizing healthcare resource consumption. Therefore, this study introduces dynamic confidence fuzzy control (DynCFC) within the symmetric unet architecture (SymUnet), referred to as SymUnet-DynCFC, which is designed to enhance the accuracy and robustness of cartilage segmentation. Firstly, the SymUnet architecture is developed, with separate inputs from T1W and T2W modalities to facilitate comprehensive segmentation evaluation. Secondly, the DynCFC mechanism is implemented to compute the optimal weighting for each modality, enabling the fusion and optimization of multimodal features. Finally, the performance of the proposed SymUnet-DynCFC method is evaluated on clinical datasets from a multi-campus hospital system. Experimental results show that SymUnet-DynCFC achieves better segmentation performance than the baselines, with mean Dice, IoU, and HD95 values of 87.96 %, 79.93 %, and 1.29, respectively. In particular, SymUnet-DynCFC exhibits improved robustness compared to the baseline methods. This may facilitate automated cartilage segmentation in clinical workflows and could support the assessment of moderate-to-severe KOA by detecting outlier metrics.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"130 ","pages":"Article 104145"},"PeriodicalIF":15.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145957304","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