IEEE Journal of Biomedical and Health Informatics最新文献_第9页

IEEE Journal of Biomedical and Health Informatics Information for Authors

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-06 DOI: 10.1109/JBHI.2025.3541766

引用次数: 0

Fluid Intake Action Detection Based on Egocentric Videos and YOLOv8 Models.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-06 DOI: 10.1109/JBHI.2025.3548512

Xin Chen, Xinqi Bao, Ernest Kamavuako

{"title":"Fluid Intake Action Detection Based on Egocentric Videos and YOLOv8 Models.","authors":"Xin Chen, Xinqi Bao, Ernest Kamavuako","doi":"10.1109/JBHI.2025.3548512","DOIUrl":"10.1109/JBHI.2025.3548512","url":null,"abstract":"Dehydration in older adults poses significant health risks, requiring effective monitoring solutions. This study addresses the challenge of detecting fluid intake accurately using a first-person, vision-based approach with wearable cameras and advanced object detection models. We developed a comprehensive dataset comprising 17 hours of drinking footage (∼3100 events) and 15 hours of nondrinking activities (∼3600 events) recorded as interference, from 36 participants, collected between October 2022 and January 2023 at King's College London. We include various container types and daily activities to enhance the model's robustness and generalizability. YOLOv8 models were used to detect drinking-related objects, and a mechanism was developed to analyse the size and position of the detection output to identify hand-container interactions and movements. The models achieved mAP@50 over 0.97 and F1-score over 0.95 in detecting drinking-related objects. Action detection testing results from video streams demonstrated an F1-score of 0.917, which dropped to 0.863 when interference activities were added. Additionally, the model detected the start of drinking activities with an average latency of 0.24 seconds and the end with 0.04 seconds, indicating high temporal accuracy. These results demonstrate the feasibility of egocentric, vision-based fluidintake detection and its potential application in preventing dehydration. To our knowledge, this is the first vision-based dataset focusing on fluid-intake actions from a first-person viewpoint-offering a novel foundation for advancing hydration monitoring in older adults and various real-world contexts.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143572936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IM-Diff: Implicit Multi-Contrast Diffusion Model for Arbitrary Scale MRI Super-Resolution.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-05 DOI: 10.1109/JBHI.2025.3544265

Lanqing Liu, Jing Zou, Cheng Xu, Kang Wang, Jun Lyu, Xuemiao Xu, Zhanli Hu, Jing Qin

{"title":"IM-Diff: Implicit Multi-Contrast Diffusion Model for Arbitrary Scale MRI Super-Resolution.","authors":"Lanqing Liu, Jing Zou, Cheng Xu, Kang Wang, Jun Lyu, Xuemiao Xu, Zhanli Hu, Jing Qin","doi":"10.1109/JBHI.2025.3544265","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3544265","url":null,"abstract":"Diffusion models have garnered significant attention for MRI Super-Resolution (SR) and have achieved promising results. However, existing diffusion-based SR models face two formidable challenges: 1) insufficient exploitation of complementary information from multi-contrast images, which hinders the faithful reconstruction of texture details and anatomical structures; and 2) reliance on fixed magnification factors, such as 2× or 4×, which is impractical for clinical scenarios that require arbitrary scale magnification. To circumvent these issues, this paper introduces IM-Diff, an implicit multi-contrast diffusion model for arbitrary-scale MRI SR, leveraging the merits of both multi-contrast information and the continuous nature of implicit neural representation (INR). Firstly, we propose an innovative hierarchical multi-contrast fusion (HMF) module with reference-aware cross Mamba (RCM) to effectively incorporate target-relevant information from the reference image into the target image, while ensuring a substantial receptive field with computational efficiency. Secondly, we introduce multiple wavelet INR magnification (WINRM) modules into the denoising process by integrating the wavelet implicit neural non-linearity, enabling effective learning of continuous representations of MR images. The involved wavelet activation enhances space-frequency concentration, further bolstering representation accuracy and robustness in INR. Extensive experiments on three public datasets demonstrate the superiority of our method over existing state-of-the-art SR models across various magnification factors.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143566929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Counterfactual Bidirectional Co-Attention Transformer for Integrative Histology-Genomic Cancer Risk Stratification.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-05 DOI: 10.1109/JBHI.2025.3548048

Zheyi Ji, Yongxin Ge, Chijioke Chukwudi, Kaicheng U, Sophia Meixuan Zhang, Yulong Peng, Junyou Zhu, Hossam Zaki, Xueling Zhang, Sen Yang, Xiyue Wang, Yijiang Chen, Junhan Zhao

{"title":"Counterfactual Bidirectional Co-Attention Transformer for Integrative Histology-Genomic Cancer Risk Stratification.","authors":"Zheyi Ji, Yongxin Ge, Chijioke Chukwudi, Kaicheng U, Sophia Meixuan Zhang, Yulong Peng, Junyou Zhu, Hossam Zaki, Xueling Zhang, Sen Yang, Xiyue Wang, Yijiang Chen, Junhan Zhao","doi":"10.1109/JBHI.2025.3548048","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3548048","url":null,"abstract":"Applying deep learning to predict patient prognostic survival outcomes using histological whole-slide images (WSIs) and genomic data is challenging due to the morphological and transcriptomic heterogeneity present in the tumor microenvironment. Existing deep learning-enabled methods often exhibit learning biases, primarily because the genomic knowledge used to guide directional feature extraction from WSIs may be irrelevant or incomplete. This results in a suboptimal and sometimes myopic understanding of the overall pathological landscape, potentially overlooking crucial histological insights. To tackle these challenges, we propose the CounterFactual Bidirectional Co-Attention Transformer framework. By integrating a bidirectional co-attention layer, our framework fosters effective feature interactions between the genomic and histology modalities and ensures consistent identification of prognostic features from WSIs. Using counterfactual reasoning, our model utilizes causality to model unimodal and multimodal knowledge for cancer risk stratification. This approach directly addresses and reduces bias, enables the exploration of 'what-if' scenarios, and offers a deeper understanding of how different features influence survival outcomes. Our framework, validated across eight diverse cancer benchmark datasets from The Cancer Genome Atlas (TCGA), represents a major improvement over current histology-genomic model learning methods. It shows an average 2.5% improvement in c-index performance over 18 state-of-the-art models in predicting patient prognoses across eight cancer types. Our code is released at https://github.com/BusyJzy599/CFBCT-main.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143566928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SecProGNN: Predicting Bronchoalveolar Lavage Fluid Secreted Protein Using Graph Neural Network.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-05 DOI: 10.1109/JBHI.2025.3548263

Dan Shao, Guangzhao Zhang, Lin Lin, Yucong Xiong, Kai He, Liyan Sun

引用次数: 0

A Hierarchical Graph Convolutional Network with Infomax-Guided Graph Embedding for Population-Based ASD Detection.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-04 DOI: 10.1109/JBHI.2025.3544302

Xiaoke Hao, Mingming Ma, Jiaqing Tao, Jiahui Cao, Jing Qin, Feng Liu, Daoqiang Zhang, Dong Ming

{"title":"A Hierarchical Graph Convolutional Network with Infomax-Guided Graph Embedding for Population-Based ASD Detection.","authors":"Xiaoke Hao, Mingming Ma, Jiaqing Tao, Jiahui Cao, Jing Qin, Feng Liu, Daoqiang Zhang, Dong Ming","doi":"10.1109/JBHI.2025.3544302","DOIUrl":"10.1109/JBHI.2025.3544302","url":null,"abstract":"Recently, functional magnetic resonance imaging (fMRI)-based brain networks have been shown to be an effective diagnostic tool with great potential for accurately detecting autism spectrum disorders (ASD). Meanwhile, the successful use of graph convolution networks (GCNs) methods based on fMRI information has improved the classification accuracy of ASD. However, many graph convolution-based methods do not fully utilize the topological information of the brain functional connectivity network (BFCN) or ignore the effect of non-imaging information. Therefore, we propose a hierarchical graph embedding model that leverage both the topological information of the BFCN and the non-imaging information of the subjects to improve the classification accuracy. Specifically, our model first use the Infomax Module to automatically identify embedded features in regions of interests (ROIs) in the brain. Then, these features, along with non-imaging information, is used to construct a population graph model. Finally, we design a graph convolution framework to propagate and aggregate the node features and obtain the results for ASD detection. Our model takes into account both the significance of the BFCN to individual subjects and relationships between subjects in the population graph. The model performed autism detection using the Autism Brain Imaging Data Exchange (ABIDE) dataset and obtained an average accuracy of 77.2% and an AUC of 87.2%. These results exceed those of the baseline approach. Through extensive experiments, we demonstrate the competitiveness, robustness and effectiveness of our model in aiding ASD diagnosis.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143556708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

NciaNet: A Non-Covalent Interaction-Aware Graph Neural Network for the Prediction of Protein-Ligand Interaction in Drug Discovery.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-04 DOI: 10.1109/JBHI.2025.3547741

Guanyu Song, Meifeng Deng, Yunzhi Chen, Shijie Jia, Zhenguo Nie

{"title":"NciaNet: A Non-Covalent Interaction-Aware Graph Neural Network for the Prediction of Protein-Ligand Interaction in Drug Discovery.","authors":"Guanyu Song, Meifeng Deng, Yunzhi Chen, Shijie Jia, Zhenguo Nie","doi":"10.1109/JBHI.2025.3547741","DOIUrl":"10.1109/JBHI.2025.3547741","url":null,"abstract":"Precise quantification of protein-ligand interaction is critical in early-stage drug discovery. Artificial intelligence (AI) has gained massive popularity in this area, with deep-learning models used to extract features from ligand and protein molecules. However, these models often fail to capture intermolecular non-covalent interactions, the primary factor influencing binding, leading to lower accuracy and interpretability. Moreover, such models overlook the spatial structure of protein-ligand complexes, resulting in weaker generalization. To address these issues, we propose Non-covalent Interaction-aware Graph Neural Network (NciaNet), a novel method that effectively utilizes intermolecular non-covalent interactions and 3D protein-ligand structure. Our approach achieves excellent predictive performance on multiple benchmark datasets and outperforms competitive baseline models in the binding affinity task, with the benchmark core set v.2016 achieving an RMSE of 1.208 and an R of 0.833, and the core set v.2013 achieving an RMSE of 1.409 and an R of 0.805, under the high-quality refined v.2016 training conditions. Importantly, NciaNet successfully learns vital features related to protein-ligand interactions, providing biochemical insights and demonstrating practical utility and reliability. However, despite these strengths, there may still be limitations in generalizability to unseen protein-ligand complexes, suggesting potential avenues for future work.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143556725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamic Local Conformal Reinforcement Network (DLCR) for Aortic Dissection Centerline Tracking.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-04 DOI: 10.1109/JBHI.2025.3547744

Jingliang Zhao, An Zeng, Jiayu Ye, Dan Pan

{"title":"Dynamic Local Conformal Reinforcement Network (DLCR) for Aortic Dissection Centerline Tracking.","authors":"Jingliang Zhao, An Zeng, Jiayu Ye, Dan Pan","doi":"10.1109/JBHI.2025.3547744","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3547744","url":null,"abstract":"Pre-extracted aortic dissection (AD) centerline is very useful for quantitative diagnosis and treatment of AD disease. However, centerline extraction is challenging because (i) the lumen of AD is very narrow and irregular, yielding failure in feature extraction and interrupted topology; and (ii) the acute nature of AD requires a quick algorithm, however, AD scans usually contain thousands of slices, centerline extraction is very time-consuming. In this paper, a fast AD centerline extraction algorithm, which is based on a local conformal deep reinforced agent and dynamic tracking framework, is presented. The potential dependence of adjacent center points is utilized to form the novel 2.5D state and locally constrains the shape of the centerline, which improves overlap ratio and accuracy of the tracked path. Moreover, we dynamically modify the width and direction of the detection window to focus on vessel-relevant regions and improve the ability in tracking small vessels. On a public AD dataset that involves 100 CTA scans, the proposed method obtains average overlap of 97.23% and mean distance error of 1.28 voxels, which outperforms four state-of-the-art AD centerline extraction methods. The proposed algorithm is very fast with average processing time of 9.54s, indicating that this method is very suitable for clinical practice.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143556720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Identification of Protein-nucleotide Binding Residues with Deep Multi-task and Multi-scale Learning.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-04 DOI: 10.1109/JBHI.2025.3547386

Jiashun Wu, Fang Ge, Shanruo Xu, Yan Liu, Jiangning Song, Dong-Jun Yu

{"title":"Identification of Protein-nucleotide Binding Residues with Deep Multi-task and Multi-scale Learning.","authors":"Jiashun Wu, Fang Ge, Shanruo Xu, Yan Liu, Jiangning Song, Dong-Jun Yu","doi":"10.1109/JBHI.2025.3547386","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3547386","url":null,"abstract":"Accurate identification of protein-nucleotide binding residues is essential for protein functional annotation and drug discovery. Advancements in computational methods for predicting binding residues from protein sequences have significantly improved predictive accuracy. However, it remains a challenge for current methodologies to extract discriminative features and assimilate heterogeneous data from different nucleotide binding residues. To address this, we introduce NucMoMTL, a novel predictor specifically designed for identifying protein-nucleotide binding residues. Specifically, NucMoMTL leverages a pre-trained language model for robust sequence embedding and utilizes deep multi-task and multi-scale learning within parameter-based orthogonal constraints to extract shared representations, capitalizing on auxiliary information from diverse nucleotides binding residues. Evaluation of NucMoMTL on the benchmark datasets demonstrates that it outperforms state-of-the-art methods, achieving an average AUROC and AUPRC of 0.961 and 0.566, respectively. NucMoMTL can be explored as a reliable computational tool for identifying protein-nucleotide binding residues and facilitating drug discovery. The dataset used and source code are freely available at: https://github.com/jerry1984Y/NucMoMTL.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143556724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Feature Fusion Attention-based Deep Learning Algorithm for Mammographic Architectural Distortion Classification.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-03 DOI: 10.1109/JBHI.2025.3547263

Khalil Ur Rehman, Li Jianqiang, Anaa Yasin, Anas Bilal, Shakila Basheer, Inam Ullah, Muhammad Kashif Jabbar, Yibin Tian

{"title":"A Feature Fusion Attention-based Deep Learning Algorithm for Mammographic Architectural Distortion Classification.","authors":"Khalil Ur Rehman, Li Jianqiang, Anaa Yasin, Anas Bilal, Shakila Basheer, Inam Ullah, Muhammad Kashif Jabbar, Yibin Tian","doi":"10.1109/JBHI.2025.3547263","DOIUrl":"10.1109/JBHI.2025.3547263","url":null,"abstract":"Architectural Distortion (AD) is a common abnormality in digital mammograms, alongside masses and microcalcifications. Detecting AD in dense breast tissue is particularly challenging due to its heterogeneous asymmetries and subtle presentation. Factors such as location, size, shape, texture, and variability in patterns contribute to reduced sensitivity. To address these challenges, we propose a novel feature fusion-based Vision Transformer (ViT) attention network, combined with VGG-16, to improve accuracy and efficiency in AD detection. Our approach mitigates issues related to texture fixation, background boundaries, and deep neural network limitations, enhancing the robustness of AD classification in mammograms. Experimental results demonstrate that the proposed model achieves state-of-the-art performance, outperforming eight existing deep learning models. On the PINUM dataset, it attains 0.97 sensitivity, 0.92 F1-score, 0.93 precision, 0.94 specificity, and 0.96 accuracy. On the DDSM dataset, it records 0.93 sensitivity, 0.91 F1-score, 0.94 precision, 0.92 specificity, and 0.95 accuracy. These results highlight the potential of our method for computer-aided breast cancer diagnosis, particularly in low-resource settings where access to high-end imaging technology is limited. By enabling more accurate and timely AD detection, our approach could significantly improve breast cancer screening and early intervention worldwide.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143541453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0