检索结果-内蒙古大学图书馆

Uncertainty Quantification and Quality Control for Heatmap-based Landmark Detection Models

IEEE Transactions on medical Imaging 2025年

作者： Feng, Yong Yang, Jinzhu Tang, Lingzhi Sun, Song Wang, Yonghuai Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang China Northeastern University School of Computer Science and Engineering Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang China The First Hospital of China Medical University Department of Cardiovascular Ultrasound Shenyang China

Uncertainty quantification is a vital aspect of explainable artificial intelligence that fosters clinician trust in medical applications and facilitates timely interventions, leading to safer and more reliable outcomes. Although deep learning models have reached clinically acceptable accuracy in anatomical landmark detection, their predictions remain susceptible to contextual noise due to the small size of the target structures, making uncertainty quantification more challenging than in classification and segmentation tasks. This paper presents an end-to-end uncertainty quantification method tailored for heatmapbased anatomical landmark detection models, designed to improve both interpretability and controllability in clinical applications. Leveraging Dempster-Shafer Theory and Subjective Logic Theory, we implement probability assignment and uncertainty quantification through a single forward pass to ensure computational efficiency. We introduce an evidence map that captures the strength of landmark evidence, alongside an uncertainty map that calibrates predicted probabilities within the Subjective Logic framework. The interaction between these two components, facilitated by a cross-attention mechanism, further improves landmark detection accuracy and enhances the effectiveness of uncertainty quantification. Experimental results demonstrate that the proposed method maintains detection accuracy, even in noisy environments, while outperforming state-of-the-art methods in terms of uncertainty quantification and quality control. Furthermore, the model effectively identifies out-of-distribution data solely through calibrated probabilities when encountering inconsistencies in multi-center data and novel data, underscoring its potential for clinical applications. © 1982-2012 IEEE.

关键词： Model predictive control

来源：评论

学校读者我要写书评

暂无评论

A Hybrid-Feature-Based Autoencoder Model for Predicting Traffic Flow to Accelerate Ambulance medical Response Time in Internet of Vehicles

引用

IEEE Internet of Things Journal 2025年第9期12卷 11397-11407页

作者： Li, Yongmeng Tian, Yu Liu, Wenjian Gao, Longxiang Tian, Hui Xing, Lumin The First Affiliated Hospital of Shandong First Medical University Shandong Provincial Qianfoshan Hospital Shandong Key Laboratory of Digital Diagnosis and Treatment of Thoracic Oncology Shandong Engineering Research Center of Intelligent Surgery Shandong Jinan250014 China Qilu Hospital of Shandong University Department of Thoracic Surgery Shandong Jinan250012 China City University of Macau Faculty of Data Science China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250014 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250000 China The First Affiliated Hospital of Shandong First Medical University Shandong Provincial Qianfoshan Hospital Shandong Key Laboratory of Digital Diagnosis and Treatment of Thoracic Oncology Shandong Jinan250014 China

Traffic congestion during ambulance travel can delay medical response times. With the growing availability of traffic data, computational methods for traffic flow prediction are attracting significant attention. However, current computational prediction models have the following shortcomings. Fully connected networks require extensive feature engineering yet struggle to capture local traffic flow features. Their high parameter count increases training time and overfitting risk, while varying traffic conditions hinder model generalization. Therefore, in this work, we propose a hybrid-feature-based autoencoder (HFAE) model to predict traffic flow and accelerate ambulance medical response time in Internet of Vehicles. Specifically, the HFAE model simultaneously considers local features from the convolutional neural network and global features from the fully connected neural network. Additionally, the HFAE model uses an autoencoder to reconstruct the original input, capturing the intrinsic structure of the traffic flow data. Specifically, the root mean-squared error and mean absolute error scores of the HFAE model are better than those of the three mainstream models. © 2014 IEEE.

关键词： Ambulances

来源：评论

学校读者我要写书评

暂无评论

MCHNet: An Efficient Cross Attention-Guided Hierarchical Multiscale Network for Segmentation of Organs at Risk in CT images

引用

IEEE Transactions on Radiation and Plasma medical Sciences 2025年第5期9卷 598-612页

作者： Guo, Huimin Gu, Yin Du, Wu Chen, Boyang Cui, Ming Zhang, Teng Sun, Deyu Qian, Wei Ma, He Northeastern University College of Medicine and Biological Information Engineering Shenyang110169 China Cancer Hospital of Dalian University of Technology Department of Radiation Oncology Gastrointestinal and Urinary and Musculoskeletal Cancer Shenyang110042 China Northeastern University College of Medicine and Biological Information Engineering Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110169 China

In radiotherapy, precisely contoured organs at risk (OARs) near the target areas are essential for effective treatment planning. Manual delineation of OARs is labor-intensive and varies among experts. Deep learning has improved accuracy and consistency, but current methods use complex architectures with many parameters, risking overfitting on small medical image datasets. Addressing this, this article introduces MCHNet, a cross attention (CA)-guided hierarchical multiscale segmentation network based on CT images. MCHNet adopts a typical U-shaped structure, with MobileVit as its backbone. An efficient CA-guided block is utilized to enhance feature extraction while minimizing model parameters. Additionally, a novel skip-connection strategy is proposed to preserve critical medical image information during multiple down-sampling operations and bridge the gap between deep and shallow features. We conducted extensive experiments on three public datasets, i.e., multiatlas labeling beyond the cranial vault dataset, Segmentation of thoracic OARs dataset, and multimodality abdominal multiorgan segmentation challenge 2022 dataset. Experiment results demonstrate that the proposed method outperforms other related Transformer-based or hybrid models in terms of computational complexity, quantitative and qualitative results. We believe that the proposed method can offer an optimal blend of precision and efficiency that advances the capabilities of OARs segmentation in radiotherapy. © 2017 IEEE.

关键词： image segmentation

来源：评论

学校读者我要写书评

暂无评论

Cramér-Rao Bound Minimization for a Passive Sensing Multi-ISAC System

引用

IEEE Transactions on Vehicular Technology 2025年

作者： Chen, Jian Chen, Qikai Deng, Yansha Jia, Jie Yin, Baoxin Wang, Xingwei Northeastern University School of Computer Science and Engineering Shenyang110819 China Northeastern University Engineering Research Center of Security Technology of Complex Network System Ministry of Education Shenyang110819 China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110819 China King's College London LondonWC2R 2LS United Kingdom

The integrated sensing and communication (ISAC) system can simultaneously provide communication and radar sensing, effectively improving spectrum utilization. However, existing research mainly focuses on the mono-static sensing ISAC system, which has shortcomings in limited sensing range and accuracy. We thus investigate a passive sensing multi-ISAC system with multiple communication users and sensing targets, and consider two cases for multi-target sensing without and with prior target knowledge at the base stations (BSs), in which the BSs are interested in estimating the targets' response matrix and channel coefficients, respectively. We also formulate a joint beamforming problem to minimize the Cramér-Rao bound (CRB) of the worst estimation constrained by communication requirements, and obtain the optimal solution with semidefinite relaxation (SDR) assisted transformation. To verify the advantages of our proposed scheme, we consider the bi-static BSs system (B-B) and beamforming with sensing interference cancellation (W-S) as two benchmark schemes. Numerical results illustrate that the proposed scheme obtains better sensing performance and effectively alleviates the effect caused by sensing interference. © 1967-2012 IEEE.

关键词： Cramer-Rao bounds

来源：评论

学校读者我要写书评

暂无评论

Coarse for Fine: Bounding Box Supervised Thyroid Ultrasound image Segmentation Using Spatial Arrangement and Hierarchical Prediction Consistency

引用

IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Chi, Jianning Lin, Geng Li, Zelan Zhang, Wenjun Chen, Jia-Hui Huang, Ying Northeastern University Faculty of Robot Science and Engineering Shenyang110167 China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110167 China University of Saskatchewan Division of Biomedical Engineering SaskatoonS7V5A9 Canada Shengjing Hospital of China Medical University Department of Ultrasound Shenyang110004 China

Weakly-supervised learning methods have become increasingly attractive for medical image segmentation, but suffered from a high dependence on quantifying the pixel-wise affinities of low-level features, which are easily corrupted in thyroid ultrasound images, resulting in segmentation over-fitting to weakly annotated regions without precise delineation of target boundaries. We propose a dual-branch weakly-supervised learning framework to optimize the backbone segmentation network by calibrating semantic features into rational spatial distribution under the indirect, coarse guidance of the bounding box mask. Specifically, in the spatial arrangement consistency branch, the maximum activations sampled from the preliminary segmentation prediction and the bounding box mask along the horizontal and vertical dimensions are compared to measure the rationality of the approximate target localization. In the hierarchical prediction consistency branch, the target and background prototypes are encapsulated from the semantic features under the combined guidance of the preliminary segmentation prediction and the bounding box mask. The secondary segmentation prediction induced from the prototypes is compared with the preliminary prediction to quantify the rationality of the elaborated target and background semantic feature perception. Experiments on three thyroid datasets illustrate that our model outperforms existing weakly-supervised methods for thyroid gland and nodule segmentation and is comparable to the performance of fully-supervised methods with reduced annotation time. The proposed method has provided a weakly-supervised segmentation strategy by simultaneously considering the target's location and the rationality of target and background semantic features distribution. It can improve the applicability of deep learning based segmentation in the clinical practice. The source code and relative datasets will be available at https://***/LanLanUp/SAHP-Net. © 2013 IEEE.

关键词： Ultrasonic imaging

来源：评论

学校读者我要写书评

暂无评论

Enhancing Cross-Dataset EEG Emotion Recognition: A Novel Approach with Emotional EEG Style Transfer Network

引用

IEEE Transactions on Affective computing 2025年

作者： Zhou, Yijin Li, Fu Li, Yang Ji, Youshuo Zhang, Lijian Chen, Yuanfang Wang, Huaning Xidian University Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial Intelligence Xi'an710071 China Beijing Institute of Mechanical Equipment Beijing100000 China Fourth Military Medical University Department of Psychiatry at Xijing Hospital Xi'an China

Electroencephalogram (EEG)-based emotion recognition has achieved remarkable success in both subject-dependent and subject-independent scenarios. However, overcoming the challenges associated with reduced performance in EEG emotion recognition across devices, time, space, and subjects (i.e., cross-dataset) remains a significant obstacle for affective brain-computer interfaces (aBCIs). The key issue lies in the distributional mismatch between source and target domain EEG signals. To tackle the significant inter-domain differences in cross-dataset EEG emotion recognition, this paper introduces an innovative framework termed the Emotional EEG Style Transfer Network (E2STN), which aims to effectively capture the emotional content information from the source domain and the style features from the target domain, facilitating the reconstruction of stylized emotion EEG representations. These stylized EEG representations significantly enhance the discriminative prediction performance in cross-dataset EEG emotion recognition. Specifically, E2STN consists of three key modules: a Transfer Module for domain style transfer, a Transfer Evaluation Module for evaluating transfer quality, and a Discriminative Module for making discriminative predictions. Extensive experiments demonstrate that EE2STN achieves state-of-the-art performance in cross-dataset emotion EEG recognition. To the best of our knowledge, this is the first work to explicitly address cross-dataset emotion EEG recognition. The experimental results provide a valuable benchmark for future research in this area. The associated code is publicly available at https://***/zhouyijin1998/EESTN. © 2010-2012 IEEE.

关键词： Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

Memristor-Based Circuit of Sequential Associative Memory with Memory Interactions and Stimulus Similarity Effect

引用

IEEE Transactions on Cognitive and Developmental Systems 2025年

作者： Xiong, Dongdong Wang, Xiaoping Jiang, Yiming Yang, Chao Jiang, Man Lai, Jingang Zeng, Zhigang Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan430074 China Hubei Key Laboratory of Braininspired Intelligent Systems Wuhan430074 China Huazhong University of Science and Technology Institute of Artificial Intelligence School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan430074 China Guangxi University of Science and Technology School of Computer Science and Technology Liuzhou545006 China Huazhong University of Science and Technology Department of Physiology School of Basic Medicine and Tongji Medical College Wuhan430030 China

Most proposed memristor-based circuits of associative memory consider various mechanisms in only one associative memory. Few works on circuit design of sequential associative memory have been reported. In this paper, a memristor-based circuit of sequential associative memory with memory interactions and stimulus similarity effect is designed. Two associative memories, the prior associative memory (PAM) and the later associative memory (LAM), are formed successively. The PAM modifies the rate of the formation of the LAM, and the LAM, in turn, affects the strength of the PAM, which are two interactions in sequential associative memory, called transfer and retroaction. In addition, the magnitudes of transfer and retroaction are determined by the similarity of the conditioned stimuli. The above functions are realized by the ring input processing module, memory module, transfer module and retroaction module. It has been identified in circuit analysis that the proposed circuit is power efficient and has good robustness. Furthermore, the proposed circuit has promising applications for fire rescue robots. © 2016 IEEE.

关键词： Associative storage

来源：评论

学校读者我要写书评

暂无评论

A Novel Construction of Two-Dimensional Z-Complementary Array Code Sets Based on Extended Boolean Functions

引用

IEEE Transactions on Vehicular Technology 2025年

作者： Tian, Liying Song, Xiaoshi Li, Yubo Zhang, Haijun Northeastern University School of Computer Science and Engineering Shenyang110819 China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110189 China Yanshan University School of Information Science and Engineering Qinhuangdao066004 China Hebei Key Laboratory of Information Transmission and Signal Processing Qinhuangdao066004 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China

Two-dimensional (2-D) array sets with good 2-D correlation properties have received considerable attention in wireless communication systems. This paper focuses on 2-D Z-complementary array code sets (ZCACSs), which have ideal 2-D correlation properties within a rectangular zone around the origin. By developing a theory linking 2-D extended Boolean functions with 2-D arrays, we establish an efficient construction of 2-D ZCACSs. In contrast to previous work, the proposed construction is capable of producing 2-D ZCACSs with new flexible parameters. Constructions of three-dimensional (3-D) and higher-dimensional ZCACSs are further investigated. We remark that 2-D and higher-dimensional complete complementary codes (CCCs) can also be generated from the proposed constructions. © 1967-2012 IEEE.

关键词： Codes (symbols)

来源：评论

学校读者我要写书评

暂无评论

Graph Contrastive Learning with Progressive Augmentations 25

Graph Contrastive Learning with Progressive Augmentations

引用

Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1

作者： Yuhai Zhao Yejiang Wang Zhengkui Wang Wen Shan Miaomiao Huang Xingwei Wang School of Computer Science and Engineering Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education Northeastern University Shenyang Liaoning China InfoComm Technology Cluster Singapore Institute of Technology Singapore Singapore Singapore University of Social Sciences Singapore Singapore School of Computer Science and Engineering Northeastern University Shenyang Liaoning China

ISBN: (纸本)9798400712456

To be still yet still moving. - Do Hyun ChoeGraph contrastive learning (GCL) has recently gained prominence in unsupervised graph representation learning. Traditional GCL approaches generally focus on creating a single contrastive view alongside the main graph view, targeting invariant representation learning in a static framework. Our study introduces a novel manner: despite using static graphs, we aim to learn invariant representations by generating a series of evolving contrastive views with temporal coherence and multi-viewpoint insights at various granularities. In this context, we propose the Progressive Augmentation framework for Graph Contrastive Learning (PaGCL). This framework advances beyond traditional methods by producing a sequence of augmented views, each evolving from the previous one, and assigning timestamps based on piecewise smoothness. This approach enables our model to more effectively extract invariant features from these dynamic views, capturing multi-grained structural and temporal information. Our experiments on diverse benchmark datasets demonstrate that PaGCL significantly outperforms current state-of-the-art methods.

关键词： graph representation learning}

来源：评论

学校读者我要写书评

暂无评论

Style Content Decomposition-based Data Augmentation for Domain Generalizable medical image Segmentation

arXiv

引用

arXiv 2025年

作者： Shen, Zhiqiang Cao, Peng Yang, Jinzhu Zaiane, Osmar R. Chen, Zhaolin School of Computer Science and Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang China Alberta Machine Intelligence Institute University of Alberta EdmontonAB Canada Monash Biomedical Imaging Monash University Australia Department of Data Science and AI Monash University Australia

Due to the domain shifts between training and testing medical images, learned segmentation models often experience significant performance degradation during deployment. In this paper, we first decompose an image into its style code and content map and reveal that domain shifts in medical images involve: style shifts (i.e., differences in image appearance) and content shifts (i.e., variations in anatomical structures), the latter of which has been largely overlooked. To this end, we propose StyCona, a style content decomposition-based data augmentation method that innovatively augments both image style and content within the rank-one space, for domain generalizable medical image segmentation. StyCona is a simple yet effective plug-and-play module that substantially improves model generalization without requiring additional training parameters or modifications to the segmentation model architecture. Experiments on cross-sequence, cross-center, and cross-modality medical image segmentation settings with increasingly severe domain shifts, demonstrate the effectiveness of StyCona and its superiority over state-of-the-arts. The code is available at https://***/Senyh/StyCona. Copyright © 2025, The Authors. All rights reserved.

关键词： medical imaging

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：