IEEE Journal of Biomedical and Health Informatics最新文献_第3页

Phage Host Prediction Using Deep Neural Network with Multi-source Protein Language Models and Squeeze-and-Excitation Attention Mechanism. 基于多源蛋白语言模型和挤压-激发注意机制的深度神经网络噬菌体宿主预测。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-06-24 DOI: 10.1109/JBHI.2025.3582652

Peng Gao, Long Xu, Yuan Bai, Qiuzhen Lin, Junkai Ji, Lijia Ma

{"title":"Phage Host Prediction Using Deep Neural Network with Multi-source Protein Language Models and Squeeze-and-Excitation Attention Mechanism.","authors":"Peng Gao, Long Xu, Yuan Bai, Qiuzhen Lin, Junkai Ji, Lijia Ma","doi":"10.1109/JBHI.2025.3582652","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3582652","url":null,"abstract":"Phage therapy (PT) has become a promising alternative for treating infections with the increase of antimicrobial resistance. PT utilizes phages to bind to specific receptors on bacterial surfaces via receptor-binding proteins (RBPs), enabling precise destruction of targeted hosts. In PT, a key issue is the phage host prediction (PHP), which tries to match therapeutic phages to pathogenic hosts. However, traditional PHP methods are often hindered by the time-consuming and expensive wet-lab experiments, while recent computational methods neglect the evolutionary diversity and local feature patterns of RBPs. In this article, we propose a novel deep neural network (called PHPRBP) for PHP based on phage RBPs. In PHPRBP, we first utilize pre-trained protein language models (i.e., ESM2 and ProtT5) to learn the multi-source embedding representations from these RBPs, revealing diverse and complementary features. Then, we employ an adaptive synthetic technique to augment minority class samples, addressing the data scarcity issue. Subsequently, we design a deep neural network architecture, which uses a convolutional neural network to capture local sequence features, and applies a squeeze-and-excitation attention mechanism to enhance the contribution of important features. Finally, a fully connected network is used for host prediction. Experimental results show that PHPRBP outperforms the state-of-the-arts in host prediction at both genus and species levels. The data and code of PHPRBP are available at https://github.com/a1678019300/PHPRBP.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144484152","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 Comprehensive Framework for the Prediction of Intra-Operative Hypotension. 术中低血压预测的综合框架。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-06-24 DOI: 10.1109/JBHI.2025.3583044

B Aubouin-Pairault, M Reus, B Meyer, R Wolf, M Fiacchini, T Dang

{"title":"A Comprehensive Framework for the Prediction of Intra-Operative Hypotension.","authors":"B Aubouin-Pairault, M Reus, B Meyer, R Wolf, M Fiacchini, T Dang","doi":"10.1109/JBHI.2025.3583044","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3583044","url":null,"abstract":"In this paper, the problem of triggering early warning for intra-operative hypotension (IOH) is addressed. Recent studies on the Hypotension Prediction Index have demonstrated a gap between the results presented during model development and clinical evaluation. Thus, there is a need for better collaboration between data scientists and clinicians who need to agree on a common basis to evaluate those models. In this paper, we propose a comprehensive framework for IOH prediction: to address several issues inherent to the commonly used fixed-time-to-onset approach in the literature, a sliding window approach is suggested. The risk prediction problem is formalized with consistent precision-recall metrics rather than the receiveroperator characteristic. For illustration, a standard machine learning method is applied using two different datasets from non-cardiac and cardiac surgery. Training is done on a part of the non-cardiac surgery dataset and tests are performed separately on the rest of the non-cardiac dataset and cardiac dataset. Compared to a realistic clinical baseline, the proposed method achieves a significant improvement on the non-cardiac surgeries (precision of 48% compared to 32% for a recall of 28% (p<0.0001)) . For cardiac surgery, this improvement is less significant but still demonstrate the generalization of the model.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144484094","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

Confidence-aware adaptive fusion leaning of imbalance multi-modal data for cancer diagnosis and prognosis. 不平衡多模态数据的置信度感知自适应融合学习用于肿瘤诊断和预后。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-06-24 DOI: 10.1109/JBHI.2025.3582626

Ziye Zhang, Shijin Wang, Yuying Huang, XiaoRou Zheng, Shoubin Dong

{"title":"Confidence-aware adaptive fusion leaning of imbalance multi-modal data for cancer diagnosis and prognosis.","authors":"Ziye Zhang, Shijin Wang, Yuying Huang, XiaoRou Zheng, Shoubin Dong","doi":"10.1109/JBHI.2025.3582626","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3582626","url":null,"abstract":"The effective fusion of pathological images and molecular omics holds significant potential for precision medicine. However, pathological and molecular data are highly heterogeneous, and large-scale multi-modal cancer data often suffer from incomplete information. Predicting clinical tasks from such imbalanced multi-modal data presents a major challenge. Therefore, we propose a confidence-aware adaptive fusion framework CAFusion. The framework adopts a modular design, providing independent and flexible modal feature learning modules to capture high-quality features. To address issues of modal imbalance caused by heterogeneous and incomplete modal, we design a confidence-aware method that evaluates the features of each modal and automatically adjusts their weights. To effectively fuse pathological and molecular modals, we propose an adaptive deep network, which features a flexible, non-fixed layer structure that effectively extracts hidden joint information from multi-modal features, ensuring high generalizability. Experiment results demonstrate that the performance of the CAFusion framework outperforms other state-of-the-art methods, both on complete and incomplete datasets. Moreover, the CAFusion framework offers reasonable medical interpretability. The source code is available at GitHub: https://github.com/SCUT-CCNL/CAFusion.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144484150","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 Machine Learning Analysis of Physiological Monitoring Signals to Detect Small Airway Narrowing Due to Cold Air Exposure in Asthma. 用机器学习方法分析哮喘患者因暴露于冷空气导致的小气道狭窄的生理监测信号。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-06-24 DOI: 10.1109/JBHI.2025.3582739

Shaghayegh Chavoshian, Yan Fossat, Xiaoshu Cao, Jaycee Kaufman, Matthew B Stanbrook, Susan M Tarlo, Azadeh Yadollahi

{"title":"A Machine Learning Analysis of Physiological Monitoring Signals to Detect Small Airway Narrowing Due to Cold Air Exposure in Asthma.","authors":"Shaghayegh Chavoshian, Yan Fossat, Xiaoshu Cao, Jaycee Kaufman, Matthew B Stanbrook, Susan M Tarlo, Azadeh Yadollahi","doi":"10.1109/JBHI.2025.3582739","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3582739","url":null,"abstract":"Asthma is a chronic inflammatory disease of the small airways, affecting over 200 million people globally. Cold air exposure is a potential risk factor for asthma exacerbations. We hypothesized that monitoring physiological signals during exposure to cold would help to detect potential worsening in asthma and can be used to help persons with asthma adjust their daily routine. Non-smoker adults (18-80 years) with asthma were asked to sit in a cold room of 0°C temperature for 10 minutes. During this period, Electrocardiogram (ECG) and thoraco-abdominal motion/respiration belt signals were measured continuously. At 0 and 10 min, small airway narrowing was assessed with oscillometry to estimate respiratory system impedance. Based on changes in respiratory impedance from 0 to 10 min, participants were grouped into with or without airway narrowing. After signal processing, we extracted time and frequency domain features from ECG and respiration signals. To classify airway narrowing, different machine learning classifiers were fine-tuned and evaluated using a leave-one-subject-out cross-validation approach. A total of 23 individuals (11 females, age: 56.3 ± 10.9 years, BMI: 27.4 ± 5.7 kg/m$^{2}$) with asthma were enrolled in the study. Up to 42% and 58% windows of signals were from individuals with and without airway narrowing, respectively. The support vector machine classifier performed the best compared to other models with an accuracy of 85%, precision of 87%, recall of 76%, specificity of 91%, and F1 score of 81%. These results provided proof of concept that technologies with embedded respiratory and cardiac signal monitoring may be able to predict airway narrowing during exposure to cold air in individuals with asthma.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144484095","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

Collaborative Relation Augmentation With Hierarchical Prescription Inference for Medication Recommendation. 基于分层处方推理的协同关系增强药物推荐。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-06-23 DOI: 10.1109/JBHI.2025.3582393

Xiaobo Li, Xiaodi Hou, Fanjun Meng, Hai Cui, Yijia Zhang

{"title":"Collaborative Relation Augmentation With Hierarchical Prescription Inference for Medication Recommendation.","authors":"Xiaobo Li, Xiaodi Hou, Fanjun Meng, Hai Cui, Yijia Zhang","doi":"10.1109/JBHI.2025.3582393","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3582393","url":null,"abstract":"Medication recommendation systems have emerged as crucial tools in healthcare, offering personalized and effective drug combinations tailored to individual patient's clinical profiles. However, most existing approaches primarily focus on drug prediction by analyzing patient-drug interactions, often neglecting the intricate correlations between diseases and drugs. To address above limitation, this paper proposes a novel Collaborative Relation augmentation with Hierarchical Prescription inference network (CRHP) for effective medication recommendation. CRHP first constructs multiple covariance knowledge graphs to capture fine-grained interaction relationships between different entities from a global perspective. Based on self-built knowledge graphs, CRHP designs a collaborative relation augmented learning module, which introduces hypergraph convolutional networks to capture high-order association information between different entities. Moreover, CRHP devises a hierarchical prescription inference module that formulates drug prescriptions based on both current and historical patient information. The extensive experiments on two publicly available real-world medical datasets, MIMIC-III and MIMIC-IV, demonstrate the effectiveness of CRHP. The results indicate significant performance improvements over baseline methods, with gains of 2.12 and 1.31 in Jaccard, 1.91 and 1.83 in PRAUC, and 1.79 and 0.98 in F1-score (in percentage points).","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144474992","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

Federated Autoencoder Model for Secure Medical Image Analysis with Privacy Preservation and Assurance. 具有隐私保护和保证的安全医学图像分析的联邦自编码器模型。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-06-23 DOI: 10.1109/JBHI.2025.3546300

Saeed Iqbal, Adnan N Qureshi, Abdulatif Alabdultif, Faheem Khan, Rutvij H Jhaveri

{"title":"Federated Autoencoder Model for Secure Medical Image Analysis with Privacy Preservation and Assurance.","authors":"Saeed Iqbal, Adnan N Qureshi, Abdulatif Alabdultif, Faheem Khan, Rutvij H Jhaveri","doi":"10.1109/JBHI.2025.3546300","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3546300","url":null,"abstract":"This paper addresses the challenge of enhancing medical imaging analysis on edge devices while maintaining patient privacy and security. In this paper, we present a novel federated autoencoder model, U-NeTrans, which prioritizes security and privacy and is designed for medical image reconstruction on edge devices. U-NeTrans uses random masking to increase training complexity while maintaining manageability by using partial data. Data secrecy is ensured by the encoder processing visible patches and the decoder using encoded data to reassemble the original image. U-NeTrans improves the representation of high-order features in medical images by combining auxiliary reconstruction tasks and contrastive loss. This allows for precise analysis while maintaining patient privacy. The proposed method has wide ramifications for chest X-ray analysis and other medical imaging applications and offers the potential to improve healthcare device capabilities at the edge significantly. Comparative experimental results with benchmark datasets highlight the effectiveness of U-NeTrans compared to state-of-the-art approaches for edge-based medical image analysis while maintaining security and privacy. Accuracy, precision, sensitivity, specificity, and AUROC are measured across multiple scales and are shown to total 98.97%, 98.68%, 98.73%, and 99.19%, respectively.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144474993","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

Boosting Few-shot Semantic Segmentation of 3D Medical Images via Collaborative Slice Alignment. 协同切片对齐增强三维医学图像的少镜头语义分割。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-06-23 DOI: 10.1109/JBHI.2025.3582160

Ran Duan, Jialun Pei, Zhiwei Wang, Ruiheng Zhang, Qiang Li, Pheng-Ann Heng

{"title":"Boosting Few-shot Semantic Segmentation of 3D Medical Images via Collaborative Slice Alignment.","authors":"Ran Duan, Jialun Pei, Zhiwei Wang, Ruiheng Zhang, Qiang Li, Pheng-Ann Heng","doi":"10.1109/JBHI.2025.3582160","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3582160","url":null,"abstract":"Few-shot semantic segmentation (FSS) of 3D medical images requires finding a 2D slice from the labeled volume as support to 'query' slices of the unlabeled one. Accurately determining support slices is crucial for learning representative prototypical features, thereby enhancing segmentation accuracy. The existing methods typically resort to the true position of the query target to align the query with support slices or simply exploit one key support slice to segment all query slices, which inevitably results in poor practicality and mis-segmentation. In this regard, we seek a practical and efficient solution by proposing a novel Collaborative Slice Alignment (CSA) module, which densely assigns each query slice its own fittest support without knowing the target prior. Concretely, our CSA first estimates the confidence scores of slices from the sorting task to implicitly reflect their physical location in the human body. The estimated scores are considered as spatial references for aligning support slices and query slices so that each matching pair shares the most similar image contents. Moreover, the self-learnable ranking objective allows CSA to transfer internal knowledge into both support and query features to further boost the FSS performance. Additionally, we introduce an Information Reconciliation (InRe) module to mitigate the inconsistent feature distribution caused by the individual differences between support and query images. Experimental results demonstrate that the combination of CSA and InRe achieves an average Dice score improvement of at least 8.61% across three datasets, consistently outperforming other state-of-the-art methods.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144474991","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

Automatic Multi-Task Segmentation and Vulnerability Assessment of Carotid Plaque on Contrast-Enhanced Ultrasound Images and Videos via Deep Learning. 基于深度学习的超声造影图像和视频的颈动脉斑块自动多任务分割和易损性评估。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-06-20 DOI: 10.1109/JBHI.2025.3581686

Bokai Hu, Han Zhang, Caixia Jia, Ke Chen, Xiangjiang Tang, Da He, Luni Zhang, Shiyao Gu, Jing Chen, Jitong Zhang, Rong Wu, Sung-Liang Chen

{"title":"Automatic Multi-Task Segmentation and Vulnerability Assessment of Carotid Plaque on Contrast-Enhanced Ultrasound Images and Videos via Deep Learning.","authors":"Bokai Hu, Han Zhang, Caixia Jia, Ke Chen, Xiangjiang Tang, Da He, Luni Zhang, Shiyao Gu, Jing Chen, Jitong Zhang, Rong Wu, Sung-Liang Chen","doi":"10.1109/JBHI.2025.3581686","DOIUrl":"10.1109/JBHI.2025.3581686","url":null,"abstract":"Intraplaque neovascularization (IPN) within carotid plaque is a crucial indicator of plaque vulnerability. Contrast-enhanced ultrasound (CEUS) is a valuable tool for assessing IPN by evaluating the location and quantity of microbubbles within the carotid plaque. However, this task is typically performed by experienced radiologists. Here we propose a deep learning-based multi-task model for the automatic segmentation and IPN grade classification of carotid plaque on CEUS images and videos. We also compare the performance of our model with that of radiologists. To simulate the clinical practice of radiologists, who often use CEUS videos with dynamic imaging to track microbubble flow and identify IPN, we develop a workflow for plaque vulnerability assessment using CEUS videos. Our multi-task model outperformed individually trained segmentation and classification models, achieving superior performance in IPN grade classification based on CEUS images. Specifically, our model achieved a high segmentation Dice coefficient of 84.64% and a high classification accuracy of 81.67%. Moreover, our model surpassed the performance of junior and medium-level radiologists, providing more accurate IPN grading of carotid plaque on CEUS images. For CEUS videos, our model achieved a classification accuracy of 80.00% in IPN grading. Overall, our multi-task model demonstrates great performance in the automatic, accurate, objective, and efficient IPN grading in both CEUS images and videos. This work holds significant promise for enhancing the clinical diagnosis of plaque vulnerability associated with IPN in CEUS evaluations.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144336478","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

DynSeizureGAT: Multi-band Dynamic Graph Attention Network for Interpretable Seizure Detection and Analysis of Drug-Resistant Epilepsy Using SEEG. DynSeizureGAT：多波段动态图注意网络，用于可解释的癫痫发作检测和使用SEEG分析耐药癫痫。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-06-20 DOI: 10.1109/JBHI.2025.3581613

Yiping Wang, Jinjie Guo, Ziyu Jia, Gongpeng Cao, Yanfeng Yang, Guixia Kang, Jinguo Huang

{"title":"DynSeizureGAT: Multi-band Dynamic Graph Attention Network for Interpretable Seizure Detection and Analysis of Drug-Resistant Epilepsy Using SEEG.","authors":"Yiping Wang, Jinjie Guo, Ziyu Jia, Gongpeng Cao, Yanfeng Yang, Guixia Kang, Jinguo Huang","doi":"10.1109/JBHI.2025.3581613","DOIUrl":"10.1109/JBHI.2025.3581613","url":null,"abstract":"The dynamic propagation of epileptic discharges complicates Drug-Resistant Epilepsy (DRE) seizure detection using traditional machine learning methods and Stereotactic Electroencephalography (SEEG). Several challenges remain unresolved in prior studies: (1) incomprehensive representations of epileptic brain network features; (2) lacking of flexible and dynamic mechanisms to learn brain network evolving features; and (3) the absence of model mechanisms interpretation corresponds with seizure mechanisms. In response, we propose a novel multi-band dynamic graph attention network, DynSeizureGAT, to detect and analyze DRE seizures with precision and interpretability. Specifically, a seizure network sequence is first constructed by integrating a multi-band directed transfer function matrix and enhanced epileptic index node features. Second, a dynamic graph attention module is integrated to dynamically weigh the contribution of various spatial scales. Third, spatial-spectral-temporal attention mechanisms enhance the model's capacity to better characterize and interpret the ictal and interictal states. Extensive experiments are conducted on the large-scale public clinical SEEG dataset (OpenNeuro). The proposed model demonstrates high seizure detection performance, achieving an average of 94.6% accuracy, 93.4% sensitivity, and 96.4% specificity. In addition, the importance of frequency bands and dynamic abnormal connectivity patterns is successfully quantified and visualized, which contributes most to the explainability. Experimental results indicate that DynSeizureGAT demonstrates strong dynamic propagation feature learning capability, corresponding with seizure propagation mechanisms, and is promising to assist DRE epileptogenic zone localization.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144336479","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

MvKeTR: Chest CT Report Generation With Multi-View Perception and Knowledge Enhancement. MvKeTR：基于多视图感知和知识增强的胸部CT报告生成。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-06-19 DOI: 10.1109/JBHI.2025.3581289

Xiwei Deng, Xianchun He, Jianfeng Bao, Yudan Zhou, Shuhui Cai, Congbo Cai, Zhong Chen

{"title":"MvKeTR: Chest CT Report Generation With Multi-View Perception and Knowledge Enhancement.","authors":"Xiwei Deng, Xianchun He, Jianfeng Bao, Yudan Zhou, Shuhui Cai, Congbo Cai, Zhong Chen","doi":"10.1109/JBHI.2025.3581289","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3581289","url":null,"abstract":"CT report generation (CTRG) aims to automatically generate diagnostic reports for 3D volumes, relieving clinicians' workload and improving patient care. Despite clinical value, existing works fail to effectively incorporate diagnostic information from multiple anatomical views and lack related clinical expertise essential for accurate and reliable diagnosis. To resolve these limitations, we propose a novel Multi-view perception Knowledge-enhanced TansfoRmer (MvKeTR) to mimic the diagnostic workflow of clinicians. Just as radiologists first examine CT scans from multiple planes, a Multi-View Perception Aggregator (MVPA) with view-aware attention is proposed to synthesize diagnostic information from multiple anatomical views effectively. Then, inspired by how radiologists further refer to relevant clinical records to guide diagnostic decision-making, a Cross-Modal Knowledge Enhancer (CMKE) is devised to retrieve the most similar reports based on the query volume to incorporate domain knowledge into the diagnosis procedure. Furthermore, instead of traditional MLPs, we employ Kolmogorov-Arnold Networks (KANs) as the fundamental building blocks of both modules, which exhibit superior parameter efficiency and reduced spectral bias to better capture high-frequency components critical for CT interpretation while mitigating overfitting. Extensive experiments on the public CTRG-Chest-548 K dataset demonstrate that our method outpaces prior state-of-the-art (SOTA) models across almost all metrics. The code is available at https://github.com/xiweideng/MvKeTR.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144333015","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