IEEE Journal of Biomedical and Health Informatics最新文献_第2页

Verification is All You Need: Prompting Large Language Models for Zero-Shot Clinical Coding. 验证就是你所需要的：为零射击临床编码提示大型语言模型。

IF 6.8 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-07-28 DOI: 10.1109/JBHI.2025.3593028

Shaoxin Li, Can Zheng, Jiaxiang Wu, Qinwei Xu, Xingkun Xu, Hanyang Wang, Yingkai Sun, Zhian Bai, Yuchen Xu, Lifeng Zhu, Weiguo Hu, Feiyue Huang

{"title":"Verification is All You Need: Prompting Large Language Models for Zero-Shot Clinical Coding.","authors":"Shaoxin Li, Can Zheng, Jiaxiang Wu, Qinwei Xu, Xingkun Xu, Hanyang Wang, Yingkai Sun, Zhian Bai, Yuchen Xu, Lifeng Zhu, Weiguo Hu, Feiyue Huang","doi":"10.1109/JBHI.2025.3593028","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3593028","url":null,"abstract":"Clinical coding translates medical information from Electronic Health Records (EHRs) into structured codes such as ICD-10, which are essential for healthcare applications. Advances in deep learning and natural language processing have enabled automatic ICD coding models to achieve notable accuracy metrics on in-domain datasets when adequately trained. However, the scarcity of clinical medical texts and the variability across different datasets pose significant challenges, making it difficult for current state-of-the-art models to ensure robust generalization performance across diverse data distributions. Recent advances in Large Language Models (LLMs), such as GPT-4o, have shown great generalization capabilities across general domains and potential in medical information processing tasks. However, their performance in generating clinical codes remains suboptimal. In this study, we propose a novel ICD coding paradigm based on code verification to leverage the capabilities of LLMs. Instead of directly generating accurate codes from a vast code space, we simplify the task by verifying the code assignment from a given candidate set. Through extensive experiments, we demonstrate that LLMs function more effectively as code verifiers rather than code generators, with GPT-4o achieving the best performance on the CodiEsp dataset under zero-shot settings. Furthermore, our results indicate that LLM-based systems can perform on par with state-of-the-art clinical coding systems while offering superior generalizability across institutions, languages, and ICD versions.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144730107","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

Exploration on Bubble Entropy. 关于气泡熵的探讨。

IF 6.8 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-07-28 DOI: 10.1109/JBHI.2025.3593153

George Manis, Dimitrios Platakis, Roberto Sassi

{"title":"Exploration on Bubble Entropy.","authors":"George Manis, Dimitrios Platakis, Roberto Sassi","doi":"10.1109/JBHI.2025.3593153","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3593153","url":null,"abstract":"Bubble entropy is a recently proposed entropy metric. Having certain advantages over popular definitions, bubble entropy finds its place in the research community map. It belongs to the family of entropy estimators which embed the signal into an m-dimensional space. Two are the main drawbacks for which those methods are criticized: the high computational cost and the dependence on parameters. Bubble entropy can be an answer to both, since computation can be performed in linear time and the dependence on parameters can be considered minimal in many practical situations. Popular entropy definitions, which are built over an embedding of the signal, mainly rely on two parameters: the size of the embedding space m and a tolerance r, which set a threshold over the distance between two points in the m-dimensional space to be considered similar. Bubble entropy totally eliminates the necessity to define a threshold distance, while it largely decouples the entropy estimation from the selection of the actual size of the embedding space in stationary conditions. Bubble entropy is compared to popular entropy definitions on theoretical and experimental basis. Theoretical analyses reveal significant advantages. Experimental analyses, comparing congestive heart failure patients and controls subjects, show that bubble entropy outperforms other popular, well established, entropy estimators in discriminating those two groups. Furthermore, machine learning-based feature ranking and experiments show that bubble entropy serves as a valuable source of features for AI decision-support algorithms.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144730088","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

Global Habitat Analysis with Multi-graph Fusion Framework of Postoperative MRI for Predicting Radiotherapy Treatment Response in Glioma Patients. 脑胶质瘤患者术后MRI多图融合框架预测放疗反应的全局生境分析。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-07-25 DOI: 10.1109/JBHI.2025.3592811

Yixin Wang, Lin Lin, Zongtao Hu, Hongzhi Wang, Qiupu Chen

{"title":"Global Habitat Analysis with Multi-graph Fusion Framework of Postoperative MRI for Predicting Radiotherapy Treatment Response in Glioma Patients.","authors":"Yixin Wang, Lin Lin, Zongtao Hu, Hongzhi Wang, Qiupu Chen","doi":"10.1109/JBHI.2025.3592811","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3592811","url":null,"abstract":"Traditional methods for predicting treatment response often rely on readily available clinical factors. However, these methods often lack the granularity to capture the complex interplay between tumor heterogeneity and treatment efficacy. A Multi-graph Fusion (MGF) model that uses habitat subregion-derived radiomic features may help predicting the response to radiotherapy in glioma patients. Firstly, three structural and three physiological habitat regions were delineated using multi-parametric magnetic resonance imaging sequences. Then radiomic features derived from these habitat subregions were used to construct MGF model, which were trained on different combinations of habitat subregions. Each view corresponded to a graph constructed from a specific tumor habitat subregion. Lastly, proposed multi-view fusion module was employed to interpret critical views and interactions for predicting treatment response, while GNNExplainer was used to elucidate the contributions of each view. The MGF model incorporating all habitats achieved the highest area under the curve values of 0.848 (95% CI: 0.832-0.863) for the training cohort and 0.792 (95% CI: 0.767-0.818) for the validation cohort in predicting treatment response. The attention values indicated that physiological habitat 3 held the highest significance. The GNNExplainer revealed key nodes and radiomic features in each view. The MGF model utilizing all habitats-derived radiomics demonstrated the best performance in predicting treatment response. The combination of multi-view fusion module and GNNExplainer enables the framework to capture complex contextual information across six habitat subregions and provides interpretability regarding the factors influencing treatment response predictions.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144715062","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

ECG Statement Classification and Lead Reconstruction using CNN-based Models. 基于cnn模型的心电报表分类与导联重建。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-07-25 DOI: 10.1109/JBHI.2025.3592852

Kiriaki J Rajotte, Bashima Islam, Xinming Huang, David D McManus, Edward A Clancy

{"title":"ECG Statement Classification and Lead Reconstruction using CNN-based Models.","authors":"Kiriaki J Rajotte, Bashima Islam, Xinming Huang, David D McManus, Edward A Clancy","doi":"10.1109/JBHI.2025.3592852","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3592852","url":null,"abstract":"ECG is an essential diagnostic tool that offers important insight into a person's cardiac and general health. The rise of intelligent wearable devices has opened a new avenue for clinicians and individuals to capture long term ECG data-albeit with fewer leads than the 12 leads that are typically used clinically, which can be vital for identifying and addressing health concerns. In this work, a multi-task convolutional neural network (CNN) classifier was used to study the influence of various combinations of ECG leads in interpretation of 71 cardiac statements spanning cardiac diagnostics, form, and rhythm. Results of this analysis suggest that the subset of limb leads I and II and chest leads V1, V3, and V6 can be used to identify several cardiac statements without loss of performance (average macro AUC of 0.903) when compared to a model trained using all 12- leads (average macro AUC of 0.905; p = 1). A hybrid CNNLSTM (long short-term memory) model was developed to reconstruct the missing chest leads. The highest performing lead reconstructor achieved an average R2 score of 0.835 when reconstructing three chest leads. This architecture was proposed as the foundation for a wearable system that could record a limited number of ECG leads while also providing a 12-lead ECG for clinical applications.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144715060","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

MSTG-Transformer: Multivariate Spatial-Temporal Gated Transformer Model for 3D Skeleton Data-based Fall Risk Prediction. MSTG-Transformer：基于三维骨架数据的多变量时空门控变压器模型。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-07-25 DOI: 10.1109/JBHI.2025.3592957

Junjie Cao, Xuan Wang, Keyi Huang, Lisha Yu, Xiaomao Fan, Yang Zhao

{"title":"MSTG-Transformer: Multivariate Spatial-Temporal Gated Transformer Model for 3D Skeleton Data-based Fall Risk Prediction.","authors":"Junjie Cao, Xuan Wang, Keyi Huang, Lisha Yu, Xiaomao Fan, Yang Zhao","doi":"10.1109/JBHI.2025.3592957","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3592957","url":null,"abstract":"As the aging population continues to grow, falls among older adults have become a significant public health concern worldwide. Data-driven approaches for effective fall risk prediction, which integrate standard functional tests with 3D skeleton data from depth sensors, are gaining increasing attention. However, the complex physiological and functional interactions among skeletal keypoints during ambulation pose challenges for multidimensional feature extraction in most predictive models. In this study, we developed a novel approach based on preprocessed 3D skeleton data, named Multivariate SpatialTemporal Gated Transformer (MSTG-Transformer). This approach consists of three main stages. First, gait cycle sequences are constructed to sophisticatedly depict the movement patterns of subjects, amplifying the distinctions between groups. Then, spatial and topological features are extracted via convolutional modules, and a dual-stream encoder block is employed to encode the features of 3D skeleton data across both time steps and time channels. Finally, a voting scheme is used to determine fall risk by integrating the classification results of individual gait cycle segments. Validation experiments on a real-world dataset demonstrate that our proposed approach outperforms classical methods, achieving a superior prediction accuracy of 0.9510 ± 0.0240. Additionally, our study highlights the crucial role of potential interactions between skeletal keypoints in accurately predicting fall risk.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144715063","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

Efficient Video Polyp Segmentation by Deformable Alignment and Local Attention. 基于可变形对齐和局部关注的高效视频息肉分割。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-07-25 DOI: 10.1109/JBHI.2025.3592897

Yifei Zhao, Xiaoying Wang, Junping Yin

{"title":"Efficient Video Polyp Segmentation by Deformable Alignment and Local Attention.","authors":"Yifei Zhao, Xiaoying Wang, Junping Yin","doi":"10.1109/JBHI.2025.3592897","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3592897","url":null,"abstract":"Accurate and efficient Video Polyp Segmentation (VPS) is vital for the early detection of colorectal cancer and the effective treatment of polyps. However, achieving this remains highly challenging due to the inherent difficulty in modeling the spatial-temporal relationships within colonoscopy videos. Existing methods that directly associate video frames frequently fail to account for variations in polyp or background motion, leading to excessive noise and reduced segmentation accuracy. Conversely, approaches that rely on optical flow models to estimate motion and align frames incur significant computational overhead. To address these limitations, we propose a novel VPS framework, termed Deformable Alignment and Local Attention (DALA). In this framework, we first construct a shared encoder to jointly encode the feature representations of paired video frames. Subsequently, we introduce a Multi-Scale Frame Alignment (MSFA) module based on deformable convolution to estimate the motion between reference and anchor frames. The multi-scale architecture is designed to accommodate the scale variations of polyps arising from differing viewing angles and speeds during colonoscopy. Furthermore, Local Attention (LA) is employed to selectively aggregate the aligned features, yielding more precise spatial-temporal feature representations. Extensive experiments conducted on the challenging SUN-SEG dataset and PolypGen dataset demonstrate that DALA achieves superior performance compared to stateof-the-art models. The code will be publicly available at https://github.com/xff12138/DALA.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144715061","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

Hierarchical Temporal Attention Networks for Cancer Registry Abstraction: Leveraging Longitudinal Clinical Data with Interpretability. 癌症登记抽象的分层时间注意网络：利用具有可解释性的纵向临床数据。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-07-24 DOI: 10.1109/JBHI.2025.3592444

Hong-Jie Dai, Han-Hsiang Wu

{"title":"Hierarchical Temporal Attention Networks for Cancer Registry Abstraction: Leveraging Longitudinal Clinical Data with Interpretability.","authors":"Hong-Jie Dai, Han-Hsiang Wu","doi":"10.1109/JBHI.2025.3592444","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3592444","url":null,"abstract":"Cancer registration is a vital source of information for government-driven cancer prevention and control policies. However, cancer registry abstraction is a complex and labor-intensive process, requiring the extraction of structured data from large volumes of unstructured clinical reports. To address these challenges, we propose a hierarchical temporal attention network leveraging attention mechanisms at the word, sentence, and document levels, while incorporating temporal and report type information to capture nuanced relationships within patients' longitudinal data. To ensure robust evaluation, a stratified sampling algorithm was developed to balance the training, validation, and test datasets across 23 coding tasks, mitigating potential biases. The proposed method achieved an average F1-score of 0.82, outperforming existing approaches by prioritizing task-relevant words, sentences, and reports through its attention mechanism. An ablation study confirmed the critical contributions of the proposed components. Furthermore, a prototype visualization tool was developed to present interpretability, providing cancer registrars with insights into the decision-making process by visualizing attention at multiple levels of granularity. Overall, the proposed methods combined with the interpretabilityfocused visualization tool, represent a significant step toward automating cancer registry abstraction from unstructured clinical text in longitudinal settings.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144707314","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

Vox-MMSD: Voxel-wise Multi-scale and Multi-modal Self-Distillation for Self-supervised Brain Tumor Segmentation. 基于体素的多尺度多模态自蒸馏的自监督脑肿瘤分割。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-07-24 DOI: 10.1109/JBHI.2025.3592116

Yubo Zhou, Jianghao Wu, Jia Fu, Qiang Yue, Wenjun Liao, Shichuan Zhang, Shaoting Zhang, Guotai Wang

{"title":"Vox-MMSD: Voxel-wise Multi-scale and Multi-modal Self-Distillation for Self-supervised Brain Tumor Segmentation.","authors":"Yubo Zhou, Jianghao Wu, Jia Fu, Qiang Yue, Wenjun Liao, Shichuan Zhang, Shaoting Zhang, Guotai Wang","doi":"10.1109/JBHI.2025.3592116","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3592116","url":null,"abstract":"Many deep learning methods have been proposed for brain tumor segmentation from multi-modal Magnetic Resonance Imaging (MRI) scans that are important for accurate diagnosis and treatment planning. However, supervised learning needs a large amount of labeled data to perform well, where the time-consuming and expensive annotation process or small size of training set will limit the model's performance. To deal with these problems, self-supervised pre-training is an appealing solution due to its feature learning ability from a set of unlabeled images that is transferable to downstream datasets with a small size. However, existing methods often overlook the utilization of multi-modal information and multi-scale features. Therefore, we propose a novel Self-Supervised Learning (SSL) framework that fully leverages multi-modal MRI scans to extract modality-invariant features for brain tumor segmentation. First, we employ a Siamese Block-wise Modality Masking (SiaBloMM) strategy that creates more diverse model inputs for image restoration to simultaneously learn contextual and modality-invariant features. Meanwhile, we proposed Overlapping Random Modality Sampling (ORMS) to sample voxel pairs with multi-scale features for self-distillation, enhancing voxel-wise representation which is important for segmentation tasks. Experiments on the BraTS 2024 adult glioma segmentation dataset showed that with a small amount of labeled data for fine-tuning, our method improved the average Dice by 3.80 percentage points. In addition, when transferred to three other small downstream datasets with brain tumors from different patient groups, our method also improved the dice by 3.47 percentage points on average, and outperformed several existing SSL methods. The code is availiable at https://github.com/HiLab-git/Vox-MMSD.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144707317","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

MRLF-DDI: A Multi-view Representation Learning Framework for Drug-Drug Interaction Event Prediction. MRLF-DDI：药物-药物相互作用事件预测的多视图表示学习框架。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-07-24 DOI: 10.1109/JBHI.2025.3592643

Jian Zhong, Haochen Zhao, Xiao Liang, Qichang Zhao, Jianxin Wang

{"title":"MRLF-DDI: A Multi-view Representation Learning Framework for Drug-Drug Interaction Event Prediction.","authors":"Jian Zhong, Haochen Zhao, Xiao Liang, Qichang Zhao, Jianxin Wang","doi":"10.1109/JBHI.2025.3592643","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3592643","url":null,"abstract":"Accurately predicting drug-drug interaction events (DDIEs) is critical for improving medication safety and guiding clinical decision-making. However, existing graph neural network (GNN)-based methods often struggle to effectively integrate multi-view features and generalize to novel or understudied drugs. To address these limitations, we propose MRLF-DDI, a multi-view representation learning framework that jointly models information from individual drug features, local interaction contexts, and global interaction patterns. MRLF-DDI introduces the use of atomlevel structural features enriched with bond angle information-marking the first incorporation of this geometryaware feature in DDIE prediction. It further employs a multigranularity GNN and a gated knowledge transfer strategy to enhance feature learning and cold-start generalization. Extensive experiments on benchmark datasets demonstrate that MRLF-DDI achieves superior performance in both warm-start and cold-start scenarios. Case studies and visualization analyses further highlight its practical utility in identifying clinically relevant DDIEs. The code for MRLFDDI is available at https://github.com/jianzhong123/MRLFDDI.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144707316","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

MAT: Mixing Attention Transfer from Multiple Transformers for Medical Tasks. MAT：混合注意力转移从多个变压器医疗任务。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-07-24 DOI: 10.1109/JBHI.2025.3592452

Zi-Hao Bo, Yuchen Guo, Xiangru Chen, Jing Xie, Lishan Ye, Feng Xu

引用次数: 0