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Stain-aware domain alignment for imbalance blood cell classification 不平衡血细胞分类的染色敏感区域比对
IF 15.5 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2026-01-20 DOI: 10.1016/j.inffus.2026.104166
Yongcheng Li , Lingcong Cai , Ying Lu , Xiao Han , Ma Li , Wenxing Lai , Xiangzhong Zhang , Xiaomao Fan
Blood cell identification is critical for hematological analysis as it aids physicians in diagnosing various blood-related diseases. In real-world scenarios, blood cell image datasets often present the issues of domain shift and data imbalance, posing challenges for accurate blood cell identification. To address these issues, we propose a novel blood cell classification method termed SADA via stain-aware domain alignment. The primary objective of this work is to mine domain-invariant features in the presence of domain shifts and data imbalance. To accomplish this objective, we propose a stain-based augmentation approach and a local alignment constraint to learn domain-invariant features. Furthermore, we propose a domain-invariant supervised contrastive learning strategy to capture discriminative features. We decouple the training process into two stages of domain-invariant feature learning and classification training, alleviating the problem of data imbalance. Experiment results on four public blood cell datasets and a private real dataset collected from the Third Affiliated Hospital of Sun Yat-sen University demonstrate that SADA can achieve a new state-of-the-art baseline, which is superior to the existing cutting-edge methods. The source code can be available at the URL (https://github.com/AnoK3111/SADA).
血细胞鉴定是血液分析的关键,因为它有助于医生诊断各种血液相关疾病。在现实场景中,血细胞图像数据集经常出现域移位和数据不平衡的问题,这给准确的血细胞识别带来了挑战。为了解决这些问题,我们提出了一种新的血细胞分类方法,称为SADA,通过染色感知结构域对齐。这项工作的主要目标是在存在域移位和数据不平衡的情况下挖掘域不变特征。为了实现这一目标,我们提出了一种基于染色的增强方法和局部对齐约束来学习域不变特征。此外,我们提出了一种领域不变的监督对比学习策略来捕获判别特征。我们将训练过程解耦为域不变特征学习和分类训练两个阶段,缓解了数据不平衡的问题。在中山大学附属第三医院的四个公共血细胞数据集和一个私人真实数据集上的实验结果表明,SADA可以实现新的最先进的基线,优于现有的前沿方法。源代码可以从URL (https://github.com/AnoK3111/SADA)获得。
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引用次数: 0
Validity-aware context modeling for gradient-guided image inpainting 基于有效性感知的梯度引导图像绘制上下文建模
IF 15.5 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2026-01-19 DOI: 10.1016/j.inffus.2026.104162
Wuzhen Shi , Wu Yang , Zhihao Wu , Yang Wen
Existing prior-guided image inpainting methods show state-of-the-art performance. But their prior extraction is computationally expensive and unstable in accuracy. Besides, most of them only focus on the structure guidance, which hardly facilitates the repair of realistic textures. Inspired by the fact that gradient maps are easy to extract and reflect both image structure and fine texture details, this paper proposes a gradient-guided network for image inpainting, which first uses the gradient context information and multi-level image compensation features to repair the gradient, and then uses the repaired gradient features to guide the generation of realistic image. A gradient-driven attention (GDA) module is introduced for efficient prior guidance. Additionally, a context validity-aware (CVA) module is proposed for progressively filling hole regions of images, which accurately utilizes both local and contextual information for image inpainting via validity-aware measurements. Furthermore, by artificially manipulating the generation of the gradient map, our gradient-guided image inpainting method enables user-guided image editing, which effectively increases the diversity of image generation and enhances the flexibility of image editing. Experiments on benchmark datasets show that the proposed method outperforms the state-of-the-art methods. Extensive ablation experiments are also conducted to demonstrate the effectiveness of each module.
现有的先验引导图像绘制方法显示了最先进的性能。但它们的先验提取计算成本高,精度不稳定。此外,它们大多只注重结构引导,难以实现逼真纹理的修复。基于梯度图易于提取和反映图像结构和精细纹理细节的特点,本文提出了一种用于图像补图的梯度引导网络,该网络首先利用梯度上下文信息和多级图像补偿特征对梯度进行修复,然后利用修复后的梯度特征指导生成逼真的图像。引入梯度驱动注意力(GDA)模块,实现有效的事前引导。此外,提出了一种上下文有效性感知(CVA)模块,用于逐步填充图像的空洞区域,该模块通过有效性感知测量准确地利用局部和上下文信息进行图像绘制。此外,我们的梯度引导图像绘制方法通过人为操纵梯度图的生成,实现用户引导的图像编辑,有效地增加了图像生成的多样性,增强了图像编辑的灵活性。在基准数据集上的实验表明,该方法优于现有的方法。为了验证每个模块的有效性,还进行了大量的烧蚀实验。
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引用次数: 0
Data fusion for low-cost sensors: A systematic literature review 低成本传感器的数据融合:系统文献综述
IF 15.5 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2026-01-18 DOI: 10.1016/j.inffus.2026.104124
Gabriel Oduori , Chaira Cocco , Payam Sajadi , Francesco Pilla
Data fusion (DF) addresses the challenge of integrating heterogeneous data sources to improve decision-making and inference. Although DF has been widely explored, no prior systematic review has specifically focused on its application to low-cost sensor (LCS) data in environmental monitoring. To address this gap, we conduct a systematic literature review (SLR) following the PRISMA framework, synthesising findings from 82 peer-reviewed articles. The review addresses three key questions: (1) What fusion methodologies are employed in conjunction with LCS data? (2) In what environmental contexts are these methods applied? (3) What are the methodological challenges and research gaps? Our analysis reveals that geostatistical and machine learning approaches dominate current practice, with air quality monitoring emerging as the primary application domain. Additionally, artificial intelligence (AI)-based methods are increasingly used to integrate spatial, temporal, and multimodal data. However, limitations persist in uncertainty quantification, validation standards, and the generalisability of fusion frameworks. This review provides a comprehensive synthesis of current techniques and outlines key directions for future research, including the development of robust, uncertainty-aware fusion methods and broader application to less-studied environmental variables.
数据融合(DF)解决了集成异构数据源以改进决策和推理的挑战。虽然DF已被广泛探索,但尚未有系统综述专门关注其在环境监测中低成本传感器(LCS)数据中的应用。为了解决这一差距,我们根据PRISMA框架进行了系统的文献综述(SLR),综合了82篇同行评议文章的发现。该综述解决了三个关键问题:(1)结合LCS数据采用了哪些融合方法?(2)这些方法适用于什么环境背景?(3)方法论上的挑战和研究差距是什么?我们的分析表明,地质统计学和机器学习方法主导了当前的实践,空气质量监测正在成为主要的应用领域。此外,基于人工智能(AI)的方法越来越多地用于整合空间、时间和多模态数据。然而,在不确定度量化、验证标准和融合框架的通用性方面仍然存在局限性。这篇综述提供了对当前技术的全面综合,并概述了未来研究的关键方向,包括鲁棒性、不确定性感知融合方法的发展以及对较少研究的环境变量的更广泛应用。
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引用次数: 0
Speech emotion recognition: A systematic mega-review of techniques and pipelines 语音情感识别:技术和管道的系统综述
IF 15.5 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2026-01-18 DOI: 10.1016/j.inffus.2026.104161
Adil Chakhtouna, Sara Sekkate, Abdellah Adib
Speech Emotion Recognition (SER) is a rapidly evolving research field that aims to enable machines to automatically identify human emotions from vocal signals. This systematic review presents a comprehensive and structured synthesis of the SER literature, focusing on eleven key research questions that span the theoretical foundations, signal processing pipeline, and methodological advancements in the field. Unlike prior surveys, this review unifies both foundational and state-of-the-art insights across all stages of the SER pipeline through a question-driven structure, offering a clear road-map for both new and experienced researchers in the SER community. We first explore the psychological and computational modeling of emotions, followed by a detailed examination of the different modalities for emotion expression, with a particular emphasis on speech. The review highlights the most widely used emotional speech databases, common pre-processing techniques, and the diverse set of handcrafted and learned features employed in SER. We compare traditional machine learning approaches with recent deep learning models, emphasizing their respective strengths, limitations, and application contexts. Special attention is given to the recent shift toward self-supervised learning (SSL) models such as Wav2Vec2 and HuBERT, which have redefined the state-of-the-art in speech-based representation learning. Special attention is given to evaluation metrics, benchmarking strategies, and real-world deployment challenges, including issues of speaker-independence and environmental variability. The review concludes by identifying key limitations across the literature and articulating future research directions necessary for developing reliable, scalable, and context-aware emotion-aware systems. Overall, this work serves as a central reference for advancing SER research and practical deployment in real-world environments.
语音情感识别(SER)是一个快速发展的研究领域,旨在使机器能够从声音信号中自动识别人类的情感。本系统综述对SER文献进行了全面和结构化的综合,重点关注11个关键研究问题,这些问题涵盖了该领域的理论基础、信号处理管道和方法进展。与之前的调查不同,该综述通过一个问题驱动的结构,统一了SER管道所有阶段的基础和最新的见解,为SER社区的新研究人员和经验丰富的研究人员提供了清晰的路线图。我们首先探索情绪的心理和计算建模,然后详细检查情绪表达的不同方式,特别强调语言。这篇综述强调了最广泛使用的情绪语音数据库,常见的预处理技术,以及SER中使用的各种手工和学习特征。我们将传统的机器学习方法与最近的深度学习模型进行比较,强调它们各自的优势、局限性和应用环境。特别关注最近向自我监督学习(SSL)模型的转变,如Wav2Vec2和HuBERT,它们重新定义了基于语音的表示学习的最新技术。特别关注评估指标、基准策略和现实世界的部署挑战,包括说话者独立性和环境可变性问题。本文总结了文献中的关键限制,并阐明了开发可靠、可扩展和上下文感知的情感感知系统所需的未来研究方向。总的来说,这项工作是推进SER研究和在现实环境中实际部署的核心参考。
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引用次数: 0
An adaptive regularized topological segmentation network integrating inter-class relations and occlusion information for vehicle component recognition 一种融合类间关系和遮挡信息的自适应正则化拓扑分割网络用于车辆部件识别
IF 15.5 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2026-01-17 DOI: 10.1016/j.inffus.2026.104157
Xunqi Zhou , Zhenqi Zhang , Zifeng Wu , Qianming Wang , Jing Teng , Jinlong Liu , Yongjie Zhai
In intelligent vehicle damage assessment, component recognition faces challenges such as significant intra-class variability and minimal inter-class differences, which hinder detection, as well as occlusions and ambiguous boundaries, which complicate segmentation. We generalize these problems into three core aspects: inter-object relational modeling, semantic-detail information balancing, and occlusion-aware decoupling. To this end, we propose the Adaptive Regularized Topological Segmentation (ARTSeg) network, comprising three complementary modules: Inter-Class Graph Constraint (ICGC), Constrained Detail Feature Backtracking (CDFB), and Topological Decoupling Segmentation (TDS). Each module is purposefully designed, integrated in a progressive structure, and synergistically reinforces the others to enhance overall performance. Specifically, ICGC clusters intra-class features and establishes implicit topological constraints among categories during feature extraction, enabling the model to better capture inter-class relationships and improve detection representation. Subsequently, CDFB evaluates the impact of channel-wise feature information within each candidate region on segmentation accuracy and computational cost, dynamically selecting appropriate feature resolutions for individual instances while balancing the demands of detection and segmentation tasks. Finally, TDS introduces topological associations between occluded and occluding regions at the feature level and decouples them at the task level, explicitly modeling generalized occlusion regions and enhancing segmentation performance. We quantitatively and qualitatively evaluate ARTSeg on a 59-category vehicle component dataset constructed for insurance damage assessment, achieving notable improvements in addressing the aforementioned problems. Experiments on two public datasets, DSMLR and Carparts, further validate the generalization capability of the proposed method. Results indicate that ARTSeg provides practical guidance for component recognition in intelligent vehicle damage assessment.
在智能车辆损伤评估中,部件识别面临着类内差异大、类间差异小等问题,这些问题阻碍了检测,以及遮挡和模糊的边界使分割变得复杂。我们将这些问题概括为三个核心方面:对象间关系建模、语义-细节信息平衡和闭塞感知解耦。为此,我们提出了自适应正则化拓扑分割(ARTSeg)网络,该网络由三个互补模块组成:类间图约束(ICGC)、约束细节特征回溯(CDFB)和拓扑解耦分割(TDS)。每个模块都有针对性地设计,集成在一个渐进的结构中,并协同加强其他模块以提高整体性能。具体来说,在特征提取过程中,ICGC对类内特征进行聚类,并在类别之间建立隐式拓扑约束,使模型能够更好地捕获类间关系,提高检测表示。随后,CDFB评估每个候选区域内通道特征信息对分割精度和计算成本的影响,在平衡检测和分割任务需求的同时,动态地为单个实例选择合适的特征分辨率。最后,TDS在特征层引入被遮挡区域和遮挡区域之间的拓扑关联,在任务层解耦,明确建模广义遮挡区域,提高分割性能。我们在用于保险损害评估的59类车辆部件数据集上对ARTSeg进行了定量和定性评估,在解决上述问题方面取得了显着改进。在DSMLR和Carparts两个公共数据集上的实验进一步验证了该方法的泛化能力。结果表明,ARTSeg为智能车辆损伤评估中的部件识别提供了实用的指导。
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引用次数: 0
Dual-layer prompt ensembles: Leveraging system- and user-level instructions for robust LLM-based query expansion and rank fusion 双层提示集成:利用系统级和用户级指令进行稳健的基于llm的查询扩展和秩融合
IF 15.5 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2026-01-17 DOI: 10.1016/j.inffus.2026.104160
Minghan Li , Ercong Nie , Huiping Huang , Xinxuan Lv , Guodong Zhou
Large Language Models (LLMs) show strong potential for query expansion (QE), but their effectiveness is highly sensitive to prompt design. This paper investigates whether exploiting the system-user prompt distinction in chat-based LLMs improves QE, and how multiple expansions should be combined. We propose Dual-Layer Prompt Ensembles, which pair a behavioural system prompt with varied user prompts to generate diverse expansions, and aggregate their BM25-ranked lists using lightweight SU-RankFusion schemes. Experiments on six heterogeneous datasets show that dual-layer prompting consistently outperforms strong single-prompt baselines. For example, on Touche-2020 a dual-layer configuration improves nDCG@10 from 0.4177 (QE-CoT) to 0.4696, and SU-RankFusion further raises it to 0.4797. On Robust04 and DBPedia, SU-RankFusion improves nDCG@10 over BM25 by 24.7% and 25.5%, respectively, with similar gains on NFCorpus, FiQA, and TREC-COVID. These results demonstrate that system-user prompt ensembles are effective for QE, and that simple fusion transforms prompt-level diversity into stable retrieval improvements.
大型语言模型(llm)具有很强的查询扩展潜力,但其有效性对提示设计高度敏感。本文研究了在基于聊天的llm中利用系统-用户提示区别是否可以提高QE,以及多个扩展应该如何组合。我们提出了双层提示合集,它将行为系统提示与不同的用户提示配对,以生成不同的扩展,并使用轻量级的SU-RankFusion方案聚合它们的bm25排名列表。在六个异构数据集上的实验表明,双层提示始终优于强单提示基线。例如,在touch -2020上,双层配置将nDCG@10从0.4177 (q - cot)提高到0.4696,SU-RankFusion进一步将其提高到0.4797。在Robust04和DBPedia上,SU-RankFusion比BM25分别提高了24.7%和25.5%,在NFCorpus、FiQA和TREC-COVID上也有类似的提高。这些结果表明,系统-用户提示集成对于QE是有效的,并且简单的融合将提示级多样性转化为稳定的检索改进。
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引用次数: 0
A granular consensus-reaching process using blockchain-based mechanisms to foster trust relationships in fuzzy group decision-making 使用基于区块链的机制在模糊群体决策中建立信任关系的粒状共识达成过程
IF 15.5 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2026-01-17 DOI: 10.1016/j.inffus.2026.104152
Juan Carlos González-Quesada , Ignacio Javier Pérez , Enrique Herrera-Viedma , Francisco Javier Cabrerizo
Granular computing is a framework encompassing tools, techniques, and theories that utilize information granules to address complex problems. Recently, it has become a popular area of study for managing uncertainty in group decision-making. Numerous models using granular computing have been developed to tackle issues such as incomplete information, consistency, and consensus in fuzzy group decision-making. However, existing granular-based approaches fail to consider two critical factors in managing consensus: (i) the individual’s willingness to engage and (ii) the necessity of mitigating bias in interpersonal interactions. To address these gaps, we propose a new granular consensus-reaching process inspired by the blockchain technology, which helps create trust among participants. Unlike most previous methods, our approach minimizes biased interactions among participants by using a communication structure based on blockchain and smart contracts. In this setup, participants’ identities, opinions, and decisions regarding the acceptance or rejection of received recommendations remain private from other peers. Additionally, our approach includes a trust-building mechanism, also based on blockchain, encouraging individuals to rethink and adjust their opinions. It differs from most previous trust-building methods by removing the requirement of opinion similarity and avoiding trust propagation. Instead, it builds trust among participants by allowing them to see how many peers have accepted suggested modifications. This enhances computational efficiency in creating trust and speeds up consensus. To demonstrate how effective our approach is, we provide a numerical example, along with a sensitivity analysis of its key assumptions and a discussion of its strengths and weaknesses. The results confirm that this new granular consensus-reaching process is valid, effective, and practical.
颗粒计算是一个包含工具、技术和理论的框架,它利用信息颗粒来解决复杂问题。近年来,不确定性管理已成为群体决策中的一个热门研究领域。已经开发了许多使用颗粒计算的模型来解决模糊群体决策中的不完全信息、一致性和共识等问题。然而,现有的基于粒度的方法未能考虑管理共识的两个关键因素:(i)个人参与的意愿和(ii)在人际交往中减轻偏见的必要性。为了解决这些差距,我们提出了一个受区块链技术启发的新的细化共识达成过程,这有助于在参与者之间建立信任。与之前的大多数方法不同,我们的方法通过使用基于区块链和智能合约的通信结构,最大限度地减少了参与者之间的偏见交互。在这种情况下,参与者的身份、意见和关于接受或拒绝收到的建议的决定对其他同伴来说是保密的。此外,我们的方法还包括一个同样基于b区块链的信任建立机制,鼓励个人重新思考和调整自己的观点。它与以往大多数信任构建方法的不同之处在于,它消除了对意见相似性的要求,避免了信任传播。相反,它可以让参与者看到有多少同伴接受了建议的修改,从而在参与者之间建立信任。这提高了创建信任和加速共识的计算效率。为了证明我们的方法是多么有效,我们提供了一个数值示例,以及对其关键假设的敏感性分析和对其优缺点的讨论。结果证实,这种新的细粒度共识达成过程是有效的、有效的和实用的。
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引用次数: 0
Federated learning in oncology: Bridging artificial intelligence innovation and privacy protection 肿瘤学中的联合学习:连接人工智能创新和隐私保护
IF 15.5 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2026-01-16 DOI: 10.1016/j.inffus.2026.104154
Xin Qi , Tao Xu , Chengrun Dang , Zhuang Qi , Lei Meng , Han Yu
Artificial intelligence (AI), including machine learning and deep learning models, is increasingly transforming oncology by providing powerful tools to analyze complex multidimensional data. However, developing reliable and generalizable models requires large-scale training datasets, which are often constrained by privacy regulations and the decentralized nature of medical data across institutions. Federated learning has recently emerged as a promising approach that enables collaborative model training across multiple sites without sharing raw data. This survey presents the fundamental principles and architectural frameworks of federated learning, highlighting its strengths in protecting data privacy, improving model robustness, and facilitating the integration of multi-omics and multi-modal datasets. Key applications in cancer detection, prognosis prediction, and treatment response prediction are discussed, underscoring its potential to support clinical decision-making. Moreover, the survey highlights major challenges in applying federated learning to oncology and outlines key directions to advance precision medicine, including the integration of multi-modal data, foundation models, causal reasoning, and continual learning. With ongoing technological advancements, federated learning holds great promise to bridge AI innovation and privacy protection in oncology.
人工智能(AI),包括机器学习和深度学习模型,通过提供强大的工具来分析复杂的多维数据,正在日益改变肿瘤学。然而,开发可靠和可推广的模型需要大规模的训练数据集,这通常受到隐私法规和医疗数据跨机构分散性质的限制。联邦学习最近成为一种很有前途的方法,它可以在不共享原始数据的情况下跨多个站点进行协作模型训练。本研究介绍了联邦学习的基本原理和架构框架,强调了其在保护数据隐私、提高模型鲁棒性以及促进多组学和多模态数据集集成方面的优势。讨论了其在癌症检测、预后预测和治疗反应预测中的关键应用,强调了其支持临床决策的潜力。此外,该调查还强调了将联合学习应用于肿瘤学的主要挑战,并概述了推进精准医学的关键方向,包括多模式数据的集成、基础模型、因果推理和持续学习。随着技术的不断进步,联合学习在肿瘤学领域的人工智能创新和隐私保护方面有着巨大的前景。
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引用次数: 0
On the security and privacy of federated learning: A survey with attacks, defenses, frameworks, applications, and future directions 关于联邦学习的安全和隐私:攻击、防御、框架、应用和未来方向的调查
IF 15.5 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2026-01-16 DOI: 10.1016/j.inffus.2026.104155
Daniel M. Jimenez-Gutierrez , Yelizaveta Falkouskaya , José L. Hernandez-Ramos , Aris Anagnostopoulos , Ioannis Chatzigiannakis , Andrea Vitaletti
Federated Learning (FL) is an emerging distributed machine learning paradigm enabling multiple clients to train a global model collaboratively without sharing their raw data. While FL enhances data privacy by design, it remains vulnerable to various security and privacy threats. This survey provides a comprehensive overview of 203 papers regarding the state-of-the-art attacks and defense mechanisms developed to address these challenges, categorizing them into security-enhancing and privacy-preserving techniques. Security-enhancing methods aim to improve FL robustness against malicious behaviors such as byzantine attacks, poisoning, and Sybil attacks. At the same time, privacy-preserving techniques focus on protecting sensitive data through cryptographic approaches, differential privacy, and secure aggregation. We critically analyze the strengths and limitations of existing methods, highlight the trade-offs between privacy, security, and model performance, and discuss the implications of non-IID data distributions on the effectiveness of these defenses. Furthermore, we identify open research challenges and future directions, including the need for scalable, adaptive, and energy-efficient solutions operating in dynamic and heterogeneous FL environments. Our survey aims to guide researchers and practitioners in developing robust and privacy-preserving FL systems, fostering advancements safeguarding collaborative learning frameworks’ integrity and confidentiality.
联邦学习(FL)是一种新兴的分布式机器学习范式,使多个客户端能够在不共享原始数据的情况下协作训练全局模型。虽然FL通过设计增强了数据隐私,但它仍然容易受到各种安全和隐私威胁。本调查提供了203篇论文的全面概述,这些论文涉及为应对这些挑战而开发的最先进的攻击和防御机制,将它们分为安全增强和隐私保护技术。安全增强方法旨在提高FL对拜占庭攻击、中毒攻击和Sybil攻击等恶意行为的鲁棒性。同时,隐私保护技术侧重于通过加密方法、差分隐私和安全聚合来保护敏感数据。我们批判性地分析了现有方法的优势和局限性,强调了隐私、安全性和模型性能之间的权衡,并讨论了非iid数据分布对这些防御有效性的影响。此外,我们确定了开放的研究挑战和未来的方向,包括在动态和异构FL环境中运行的可扩展、自适应和节能解决方案的需求。我们的调查旨在指导研究人员和从业人员开发强大的、保护隐私的FL系统,促进进步,保护协作学习框架的完整性和保密性。
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引用次数: 0
Lifting wavelet transform-guided network with histogram attention for liver segmentation in CT scans 基于直方图关注的提升小波变换引导网络在CT肝脏分割中的应用
IF 15.5 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2026-01-16 DOI: 10.1016/j.inffus.2026.104153
Huaxiang Liu , Wei Sun , Youyao Fu , Shiqing Zhang , Jie Jin , Jiangxiong Fang , Binliang Wang
Accurate liver segmentation in computed tomography (CT) scans is crucial for the diagnosis of hepatocellular carcinoma and surgical planning; however, manual delineation is laborious and prone to operator variability. Existing deep learning methods frequently sacrifice precise boundary delineation when expanding receptive fields or fail to leverage frequency-domain cues that encode global shape, while conventional attention mechanisms are less effective in processing low-contrast images. To address these challenges, we introduce LWT-Net, a novel network guided by a trainable lifting wavelet transform, incorporating a frequency-split histogram attention mechanism to enhance liver segmentation. LWT-Net incorporates a trainable lifting wavelet transform within an encoder-decoder framework to hierarchically decompose features into low-frequency components that capture global structure and high-frequency bands that preserve edge and texture details. A complementary inverse lifting stage reconstructs high-resolution features while maintaining spatial consistency. The frequency-spatial fusion module, driven by a histogram-based attention mechanism, performs histogram-guided feature reorganization across global and local bins, while employing self-attention to capture long-range dependencies and prioritize anatomically significant regions. Comprehensive evaluations on the LiTS2017, WORD, and FLARE22 datasets confirm LWT-Net’s superior performance, achieving mean Dice similarity coefficients of 95.96%, 97.15%, and 95.97%.
计算机断层扫描(CT)中准确的肝脏分割对肝癌的诊断和手术计划至关重要;然而,手工描绘是费力的,而且容易受到操作者的变化。现有的深度学习方法在扩展接受域或无法利用编码全局形状的频域线索时,往往会牺牲精确的边界描绘,而传统的注意机制在处理低对比度图像时效果较差。为了解决这些挑战,我们引入了LWT-Net,这是一种由可训练提升小波变换引导的新型网络,结合了频率分裂直方图注意机制来增强肝脏分割。LWT-Net在编码器-解码器框架内结合了可训练的提升小波变换,分层次将特征分解为捕获全局结构的低频分量和保留边缘和纹理细节的高频波段。互补的逆提升阶段重建高分辨率特征,同时保持空间一致性。频率-空间融合模块由基于直方图的注意机制驱动,在全局和局部bins中执行直方图引导的特征重组,同时利用自注意捕获远程依赖关系并优先考虑解剖上重要的区域。在LiTS2017、WORD和FLARE22数据集上的综合评价证实了LWT-Net的优越性能,平均Dice相似系数分别达到95.96%、97.15%和95.97%。
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引用次数: 0
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