IEEE transactions on neural networks and learning systems最新文献_第7页

RSME: Respiration-Driven Synchronized Motion Estimator for Real-Time Thoracic 3-D CT Reconstruction Using Low-Rank Motion Fields and Dose-Free Surface Imaging RSME：呼吸驱动同步运动估计器，用于低秩运动场和无剂量表面成像的实时胸部三维CT重建

IF 10.4 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2025-05-09 DOI: 10.1109/tnnls.2025.3564947

Ziwen Wei, Xiaolong Wu, Qi Wang, Yunbiao Zhou, Shaozhuang Zhai, Tao Jiang, Zhihua Liu, Yang Zhang, Hongcang Gu, Shuanghu Yuan, Junchao Qian

引用次数: 0

Improving the Transferability of Adversarial Examples by Feature Augmentation 利用特征增强提高对抗性示例的可转移性

IF 10.4 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2025-05-08 DOI: 10.1109/tnnls.2025.3563855

Donghua Wang, Wen Yao, Tingsong Jiang, Xiaohu Zheng, Junqi Wu

引用次数: 0

Chest X-Ray Visual Saliency Modeling: Eye-Tracking Dataset and Saliency Prediction Model. 胸部x线视觉显著性建模：眼动追踪数据集和显著性预测模型。

IF 10.4 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2025-05-08 DOI: 10.1109/tnnls.2025.3564292

Jianxun Lou,Huasheng Wang,Xinbo Wu,John Cho Hui Ng,Richard White,Kaveri A Thakoor,Padraig Corcoran,Ying Chen,Hantao Liu

{"title":"Chest X-Ray Visual Saliency Modeling: Eye-Tracking Dataset and Saliency Prediction Model.","authors":"Jianxun Lou,Huasheng Wang,Xinbo Wu,John Cho Hui Ng,Richard White,Kaveri A Thakoor,Padraig Corcoran,Ying Chen,Hantao Liu","doi":"10.1109/tnnls.2025.3564292","DOIUrl":"https://doi.org/10.1109/tnnls.2025.3564292","url":null,"abstract":"Radiologists' eye movements during medical image interpretation reflect their perceptual-cognitive processes of diagnostic decisions. The eye movement data can be modeled to represent clinically relevant regions in a medical image and potentially integrated into an artificial intelligence (AI) system for automatic diagnosis in medical imaging. In this article, we first conduct a large-scale eye-tracking study involving 13 radiologists interpreting 191 chest X-ray (CXR) images, establishing a best-of-its-kind CXR visual saliency benchmark. We then perform analysis to quantify the reliability and clinical relevance of saliency maps (SMs) generated for CXR images. We develop CXR image saliency prediction method (CXRSalNet), a novel saliency prediction model that leverages radiologists' gaze information to optimize the use of unlabeled CXR images, enhancing training and mitigating data scarcity. We also demonstrate the application of our CXR saliency model in enhancing the performance of AI-powered diagnostic imaging systems.","PeriodicalId":13303,"journal":{"name":"IEEE transactions on neural networks and learning systems","volume":"20 1","pages":""},"PeriodicalIF":10.4,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143926508","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

CellMix: A General Instance Relationship-Based Method for Data Augmentation Toward Pathology Image Classification CellMix：一种基于一般实例关系的病理图像分类数据增强方法

IF 10.4 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2025-05-07 DOI: 10.1109/tnnls.2025.3554752

Tianyi Zhang, Zhiling Yan, Chunhui Li, Nan Ying, Yanli Lei, Shangqing Lyu, Yunlu Feng, Yu Zhao, Guanglei Zhang

引用次数: 0

Generalized Cross-Domain Industrial Process Monitoring via Adaptive Discriminative Transfer Dictionary Pair Learning With Attribute Embedding 基于属性嵌入的自适应判别迁移字典对学习的广义跨域工业过程监控

IF 10.4 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2025-05-07 DOI: 10.1109/tnnls.2025.3563618

Ziqing Deng, Xiaofang Chen, Yongfang Xie, Hongliang Zhang, Weihua Gui

引用次数: 0

FedSI: Federated Subnetwork Inference for Efficient Uncertainty Quantification 联邦子网推理的有效不确定性量化

IF 10.4 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2025-05-06 DOI: 10.1109/tnnls.2025.3562164

Hui Chen, Hengyu Liu, Zhangkai Wu, Xuhui Fan, Longbing Cao

引用次数: 0

Learning to Coordinate With Different Teammates via Team Probing. 通过团队探索学习与不同的队友协调。

IF 10.4 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2025-05-06 DOI: 10.1109/tnnls.2025.3563773

Hao Ding,Chengxing Jia,Zongzhang Zhang,Cong Guan,Feng Chen,Lei Yuan,Yang Yu

{"title":"Learning to Coordinate With Different Teammates via Team Probing.","authors":"Hao Ding,Chengxing Jia,Zongzhang Zhang,Cong Guan,Feng Chen,Lei Yuan,Yang Yu","doi":"10.1109/tnnls.2025.3563773","DOIUrl":"https://doi.org/10.1109/tnnls.2025.3563773","url":null,"abstract":"Coordinating with different teammates is essential in cooperative multiagent systems (MASs). However, most multiagent reinforcement learning (MARL) methods assume fixed team compositions, which leads to agents overfitting their training partners and failing to cooperate well with different teams during the deployment phase. A common way to mitigate the problem is to anticipate teammate behaviors and adapt policies accordingly during cooperation. However, these methods use the same policy for both collecting information for modeling teammates and maximizing cooperation performance. We argue that these two goals may conflict and reduce the effectiveness of both. In this work, we propose coordinating with different teammates via team probing (CDP), a novel approach that rapidly adapts to different teams by disentangling probing and adaptation phases. Specifically, we first generate a diverse population of teams as training partners with a novel value-based diversity objective. Then, we train a probing module to probe and reveal the coordination pattern of each team with policy-dynamics reconstruction and get a representation space of the population. Finally, we train a generalist meta-policy consisting of several expert policies with module selection based on the clustering of the learned representation space. We empirically show that CDP surpasses existing policy adaptation methods in various complex multiagent scenarios with both seen and unseen teammates.","PeriodicalId":13303,"journal":{"name":"IEEE transactions on neural networks and learning systems","volume":"102 1","pages":""},"PeriodicalIF":10.4,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143915049","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

Dual-Correlation-Guided Anchor Learning for Scalable Incomplete Multi-View Clustering. 可扩展不完全多视图聚类的双相关引导锚学习。

IF 10.4 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2025-05-06 DOI: 10.1109/tnnls.2025.3562297

Wen-Jue He,Zheng Zhang,Xiaofeng Zhu

{"title":"Dual-Correlation-Guided Anchor Learning for Scalable Incomplete Multi-View Clustering.","authors":"Wen-Jue He,Zheng Zhang,Xiaofeng Zhu","doi":"10.1109/tnnls.2025.3562297","DOIUrl":"https://doi.org/10.1109/tnnls.2025.3562297","url":null,"abstract":"Efficiently learning informative yet compact representations from heterogeneous data remains challenging in incomplete multi-view clustering (IMC). The prevalent resource-efficient IMC models excel in constructing small-size anchors for fast similarity learning and data partition. However, existing anchor-based methods still suffer from shared deficiencies: 1) unstable and less informative anchor generation by random anchor selection or clueless learning and 2) imbalanced coherence and versatility capabilities of the learned anchors among different views. To mitigate these issues, we propose a novel dual-correlation-guided anchor learning (DCGA) method for scalable IMC, which learns informative anchor spaces to simultaneously incorporate both intra-view and inter-view correlations. Specifically, the intra-view anchor space is constructed and stabilized by compressing the view-specific data under the guidance of the conceived anchors as a bottleneck (A3B) strategy, with a strict theoretic analysis. Importantly, we, for the first time, build an unsupervised anchor learning scheme for incomplete multi-view data under the guidance of the bottleneck of information flow with the well-defined IB principle. As such, our model can simultaneously eliminate information redundancy and preserve the versatile knowledge derived from each view. Moreover, to endow the coherence of the learned anchors, an informative anchor constraint (IAC) is imposed to align the anchor spaces across different views. Extensive experiments on seven datasets against 11 state-of-the-art IMC methods validate the effectiveness and efficiency of our method. Code is available at https://github.com/DarrenZZhang/TNNLS25-DCGA.","PeriodicalId":13303,"journal":{"name":"IEEE transactions on neural networks and learning systems","volume":"138 1","pages":""},"PeriodicalIF":10.4,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143915048","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

A Survey and Evaluation of Adversarial Attacks in Object Detection. 目标检测中对抗性攻击的研究与评价。

IF 10.4 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2025-05-06 DOI: 10.1109/tnnls.2025.3561225

Khoi Nguyen Tiet Nguyen,Wenyu Zhang,Kangkang Lu,Yu-Huan Wu,Xingjian Zheng,Hui Li Tan,Liangli Zhen

{"title":"A Survey and Evaluation of Adversarial Attacks in Object Detection.","authors":"Khoi Nguyen Tiet Nguyen,Wenyu Zhang,Kangkang Lu,Yu-Huan Wu,Xingjian Zheng,Hui Li Tan,Liangli Zhen","doi":"10.1109/tnnls.2025.3561225","DOIUrl":"https://doi.org/10.1109/tnnls.2025.3561225","url":null,"abstract":"Deep learning models achieve remarkable accuracy in computer vision tasks yet remain vulnerable to adversarial examples-carefully crafted perturbations to input images that can deceive these models into making confident but incorrect predictions. This vulnerability poses significant risks in high-stakes applications such as autonomous vehicles, security surveillance, and safety-critical inspection systems. While the existing literature extensively covers adversarial attacks in image classification, comprehensive analyses of such attacks on object detection systems remain limited. This article presents a novel taxonomic framework for categorizing adversarial attacks specific to object detection architectures, synthesizes existing robustness metrics, and provides a comprehensive empirical evaluation of state-of-the-art attack methodologies on popular object detection models, including both traditional detectors and modern detectors with vision-language pretraining. Through rigorous analysis of open-source attack implementations and their effectiveness across diverse detection architectures, we derive key insights into attack characteristics. Furthermore, we delineate critical research gaps and emerging challenges to guide future investigations in securing object detection systems against adversarial threats. Our findings establish a foundation for developing more robust detection models while highlighting the urgent need for standardized evaluation protocols in this rapidly evolving domain.","PeriodicalId":13303,"journal":{"name":"IEEE transactions on neural networks and learning systems","volume":"39 1","pages":""},"PeriodicalIF":10.4,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143915053","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

Toward Quantum Federated Learning 迈向量子联合学习

IF 10.4 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2025-05-06 DOI: 10.1109/tnnls.2025.3552643

Chao Ren, Rudai Yan, Huihui Zhu, Han Yu, Minrui Xu, Yuan Shen, Yan Xu, Ming Xiao, Zhao Yang Dong, Mikael Skoglund, Dusit Niyato, Leong Chuan Kwek

引用次数: 0