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

FastCRL: A Fast Network With Adaptive Fourier Transform and Offset Prediction for Fetal Crown-Rump Length Measurement and Position Estimation in Ultrasound Images FastCRL：一种基于自适应傅立叶变换和偏移预测的胎儿冠臀长度测量和超声图像位置估计快速网络。

IF 7.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2026-10-01 Epub Date: 2025-02-06 DOI: 10.1109/JBHI.2025.3539391

Jiatao Liu;Ying Tan;Chunlian Wang;Kenli Li;Guanghua Tan;Chubo Liu

{"title":"FastCRL: A Fast Network With Adaptive Fourier Transform and Offset Prediction for Fetal Crown-Rump Length Measurement and Position Estimation in Ultrasound Images","authors":"Jiatao Liu;Ying Tan;Chunlian Wang;Kenli Li;Guanghua Tan;Chubo Liu","doi":"10.1109/JBHI.2025.3539391","DOIUrl":"10.1109/JBHI.2025.3539391","url":null,"abstract":"Fetal crown-rump length (CRL) is one of the most accurate method for estimating gestational age in early pregnancy. Typically, the process of manual CRL measurement by physicians is cumbersome, prone to errors due to fetal position, and susceptible to inter-observer variability. To provide an accurate, real-time, and reliable fetal CRL measurement solution, we propose FastCRL that utilizes key landmarks detection for efficient CRL measurements and fetal position estimation. Specifically, fast and lightweight network blocks are employed for both the encoder and decoder. By outputting low-resolution heatmaps and axial offset maps of key landmarks, we achieve a balance between high accuracy and fast inference speed. A novel Lightweight Adaptive Fourier Transform (LAFT) module is introduced to globally filter noise in ultrasound images and enhance the features required for landmark prediction. Additionally, the challenge of evaluating fetal position flexion and extension is effectively addressed by analyzing the angles between key landmarks on the fetal head, buttocks, and neck. The experimental results on our dataset indicate that our method for determining fetal position is both objective and efficient. FastCRL achieves a performance level consistent with the average human expert. In terms of measuring CRL, FastCRL achieved an error rate of less than 3% in 99.1% of measurements with 32 ms latency, significantly outperforming other baselines and demonstrating substantial potential for clinical application.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"30 10","pages":"8232-8243"},"PeriodicalIF":7.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143541597","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 Segmentation of Placenta From MR Images Using a Novel BiGC U-Net 基于新型BiGC U-Net的胎盘自动分割。

IF 7.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2026-10-01 Epub Date: 2026-03-30 DOI: 10.1109/JBHI.2026.3678395

Dengjiang Wu;Suzie Coldman;Guanglong Zhou;Xinshan Li;Elspeth Whitby;Lingzhong Guo

{"title":"Automatic Segmentation of Placenta From MR Images Using a Novel BiGC U-Net","authors":"Dengjiang Wu;Suzie Coldman;Guanglong Zhou;Xinshan Li;Elspeth Whitby;Lingzhong Guo","doi":"10.1109/JBHI.2026.3678395","DOIUrl":"10.1109/JBHI.2026.3678395","url":null,"abstract":"Accurate segmentation of the placenta in Magnetic Resonance (MR) images is required for quantitative techniques such as texture and shape analysis, which have been proposed to improve placenta accreta spectrum (PAS) diagnostic rates. However, it is challenging due to the low contrast, image noise, blurred boundaries, and manual annotation variability. Hence, we proposed an enhanced U-Net architecture named BiGC U-Net, to automatically segment placental MR images. This deep learning (DL) structure incorporates a bidirectional gated convolutional module (BiGC) to capture complementary spatial dependencies, a hierarchical regularization mechanism (HRM) to enhance crosslayer semantic consistency, and an innovative data augmentation strategy to synthesize new images. The performance of BiGC U-Net was evaluated on three placental MR datasets: (1) public, (2) Sheffield Teaching Hospitals (STH) and (3) combined (public + STH + augmented), and compared against existing DL models including U-Net, Attention U-Net, ResNet, UNet++, TransUNet, nnUNet, and SSM-Mamba. The BiGC U-Net exhibited the best Dice similarity coefficient (90.74 ± 0.44), 95th percentile Hausdorff distance (3.84 mm ± 0.53 mm) and relative volume difference (9.06 ± 0.41) in comparison with other DL models in the combined dataset. These findings indicate the effectiveness and robustness of the BiGC U-Net in accurately and automatically segmenting the placenta.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"30 10","pages":"8813-8823"},"PeriodicalIF":7.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147580942","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

Legal and Ethical Considerations for Translating Federated Learning Into Cross-Border Healthcare Innovation 将联邦学习转化为跨境医疗保健创新的法律和伦理考虑。

IF 7.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2026-10-01 Epub Date: 2026-03-25 DOI: 10.1109/JBHI.2026.3677593

Luana F. Alvarenga;Nickerson F. Ferreira;André S. C. Peres;Renan C. Moioli;César Rennó-Costa

{"title":"Legal and Ethical Considerations for Translating Federated Learning Into Cross-Border Healthcare Innovation","authors":"Luana F. Alvarenga;Nickerson F. Ferreira;André S. C. Peres;Renan C. Moioli;César Rennó-Costa","doi":"10.1109/JBHI.2026.3677593","DOIUrl":"10.1109/JBHI.2026.3677593","url":null,"abstract":"Federated Learning (FL) offers a privacy-enhancing architecture for training artificial intelligence on decentralized healthcare data, yet the prevailing mantra to “move the model, not the data” obscures significant privacy risks, ethical dilemmas, and regulatory conflicts. This article challenges the assumption that FL inherently solves cross-border compliance by analyzing a hypothetical consortium involving the United States, the U.K., EU, China, and Brazil. We identify a specific compliance deadlock arising from the friction between Western rights-based frameworks (HIPAA, GDPR, LGPD) and state-centric security models (China's PIPL/DSL), particularly regarding model inversion attacks and data localization. Moving beyond validatory analysis, we propose a multi-layered Federated Governance Framework to operationalize data diplomacy. This contribution introduces novel legal and structural mechanisms: the <italic>Federated Data Sharing & Use Agreement (F-DSA) to codify binding mandates like privacy-enhancing technologies and isolated training; <italic>Governance as a Service (GaaS) to neutralize conflicts of interest through third-party administration; and relational mechanisms, including blockchain-based dynamic consent to solve the cascade of consent problem. This framework provides a blueprint for converting theoretical FL potential into a legally compliant, ethically sound, and functioning international Learning Health System (LHS).","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"30 10","pages":"8684-8696"},"PeriodicalIF":7.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11455619","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147511711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unsupervised Contrastive Refinement With Graph-Aware Multimodal Interaction for Radiology Report Generation 无监督对比改进与图形感知多模态交互用于放射学报告生成。

IF 7.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2026-10-01 Epub Date: 2026-03-04 DOI: 10.1109/JBHI.2026.3670375

Akshay Daydar;Chenna Keshava Reddy;Sonal Kumar;Hanif Laskar;Arijit Sur;Subramani Kanagaraj

{"title":"Unsupervised Contrastive Refinement With Graph-Aware Multimodal Interaction for Radiology Report Generation","authors":"Akshay Daydar;Chenna Keshava Reddy;Sonal Kumar;Hanif Laskar;Arijit Sur;Subramani Kanagaraj","doi":"10.1109/JBHI.2026.3670375","DOIUrl":"10.1109/JBHI.2026.3670375","url":null,"abstract":"Automatic Radiology Report Generation (RRG) reduces medical workload by automatically generating diagnostic reports from X-ray images. In recent studies, an improved performance has been observed by first classifying the image and then incorporating clinical outcomes into the RRG pipeline, with cross-modal interactions between image and text. However, these studies have limitations (specifically on chest X-ray), including : (1) inconsistent cross-modal learning due to under-utilization of textual information, and (2) reliance on noisy classification labels, resulting in inaccurate attention and invariant report generation. To address these issues, a Medical Context-aware RRG Framework (MedCARF) is proposed, which introduces a Disease-Aware Visual Textual Alignment (D-ViTAlign) module to capture organ–abnormality relationships from scene graphs and visual features. Label refinement is achieved using an unsupervised Augmentation Distillation with Contrastive Refinement (AuDiCoR) loss, which iteratively refines the visual features via augmentation alignment and loss modelling. In addition, the knee OAI dataset is benchmarked for the RRG task, and the performance of MedCARF contributions is explicitly tested on this dataset. Furthermore, Knee RRG performance is evaluated using multimodal information, including biomechanical data. Experimental results showed that a minimum 3.2% improvement in METEOR and a 4.22% AUC gain over classification SOTA are achieved for chest X-ray, while a 9.69% average improvement across all NLG metrics is observed for knee RRG with biomechanical data integration. Overall, contextually accurate reports are generated, and consistent classification performance is achieved, including for minority classes, demonstrating the robustness and clinical applicability of the proposed framework. The Code is available at: <uri>https://github.com/adaydar/MedCARF</uri>.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"30 10","pages":"8787-8798"},"PeriodicalIF":7.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147354827","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 Lightweight Curriculum and Contrastive Learning Framework for Protein—Protein Interaction Prediction 蛋白质-蛋白质相互作用预测的轻量级课程和对比学习框架。

IF 7.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2026-10-01 Epub Date: 2026-03-30 DOI: 10.1109/JBHI.2026.3678783

Hu Yuan;Yan Kang;Yingmei Tang;Ke Zhao;Yansheng Xu

{"title":"A Lightweight Curriculum and Contrastive Learning Framework for Protein—Protein Interaction Prediction","authors":"Hu Yuan;Yan Kang;Yingmei Tang;Ke Zhao;Yansheng Xu","doi":"10.1109/JBHI.2026.3678783","DOIUrl":"10.1109/JBHI.2026.3678783","url":null,"abstract":"Protein—protein interaction (PPI) prediction is essential for understanding cellular functions and enabling applications in drug development and disease research. PPI networks exhibit multi-scale structural and semantic heterogeneity and introduce representational bias under topological imbalances, leading to insufficient exploitation of subgraph-level semantic complexity. Moreover, many existing approaches rely heavily on external data during modeling, resulting in high computational costs for large-scale inferences. In this paper, we propose JCCLPPI, a joint curriculum- and contrastive-learning framework for lightweight PPI prediction. First, we implement a PPI network-structure encoding module designed to mitigate topological bias and learn biologically interpretable representations without relying on handcrafted features or external annotations. Next, we propose a motif-based curriculum learning module that incrementally introduces training samples according to their structural–semantic complexity, thereby enhancing model robustness to long-tail distributions and structural heterogeneity. Finally, our approach incorporates two graph neural network–based modules during PPI inference to perform local structural modeling and global context encoding, facilitating multi-scale feature extraction. Experiments conducted on two widely used human PPI benchmark datasets, SHS27k and SHS148k, demonstrate that JCCLPPI improves model generalization. It achieves an approximate 4% increase in micro-F1 score compared to state-of-the-art methods, while also improving computational efficiency by reducing memory consumption by 76% and inference time by 35%. Furthermore, JCCLPPI provides a scalable basis for therapeutic target prioritization and early-stage drug discovery.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"30 10","pages":"8610-8623"},"PeriodicalIF":7.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147580989","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

MVTT-GMamba: A Multimodal Graph Reasoning Framework for Anti-VEGF Efficacy Prediction in Diabetic Macular Edema mvtt - gamba：糖尿病黄斑水肿抗vegf疗效预测的多模态图推理框架。

IF 7.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2026-10-01 Epub Date: 2026-03-04 DOI: 10.1109/JBHI.2026.3670360

Shijing Wu;Yifan Zheng;Tianyu Chen;Gang Wu;Qinkun Zhi;Ailing Sui;Haixia Bai;Junhui Shen;Li Zhang;Yao Wang;Jian Ma;Xiaoyun Fang;Yanming Zhu;Feiwei Qin;Zhiqing Chen

{"title":"MVTT-GMamba: A Multimodal Graph Reasoning Framework for Anti-VEGF Efficacy Prediction in Diabetic Macular Edema","authors":"Shijing Wu;Yifan Zheng;Tianyu Chen;Gang Wu;Qinkun Zhi;Ailing Sui;Haixia Bai;Junhui Shen;Li Zhang;Yao Wang;Jian Ma;Xiaoyun Fang;Yanming Zhu;Feiwei Qin;Zhiqing Chen","doi":"10.1109/JBHI.2026.3670360","DOIUrl":"10.1109/JBHI.2026.3670360","url":null,"abstract":"Accurately predicting individual responses to Anti-Vascular Endothelial Growth Factor (Anti-VEGF) efficacy in diabetic macular edema (DME) remains a critical challenge in personalized ophthalmic care. Existing methods often rely on unimodal data or suffer from ineffective multimodal feature extraction and fusion, leading to modality redundancy and performance degradation. To address these limitations, we propose MVTT-GMamba, a novel multimodal learning framework that integrates optical coherence tomography (OCT) images and structured clinical indicators for early and accurate Anti-VEGF efficacy prediction. At the core of MVTT-GMamba is a feature-wise heterogeneous graph reasoning paradigm that explicitly models inter-patient and inter-feature relations, together with an adaptive, graph-guided prediction head that progressively anneals structural priors into the classifier. Building on this core, we adopt domain-tailored MambaVision and TabTransformer encoders and an early cross-attention fusion module to realize fine-grained multimodal representation learning. Extensive experiments on both a private clinical dataset (DMETHERA-ECSAHZU) and the public APTOS2021 benchmark demonstrate that MVTT-GMamba consistently outperforms state-of-the-art methods across all evaluation metrics. In addition, Grad-CAM visualizations reveal that the model attends to clinically relevant retinal regions, providing enhanced interpretability.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"30 10","pages":"8723-8736"},"PeriodicalIF":7.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147354860","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

MedSegAgent: A Universal and Scalable Multi-Agent System for Instructive Medical Image Segmentation MedSegAgent：一个通用的、可扩展的多agent系统，用于指导医学图像分割。

IF 7.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2026-10-01 Epub Date: 2026-03-25 DOI: 10.1109/JBHI.2026.3677444

Ziyan Huang;Haoyu Wang;Jin Ye;Yuanfeng Ji;Xiaowei Hu;Lihao Liu;Zhikai Yang;Wei Li;Ming Hu;Yanzhou Su;Tianbin Li;Yun Gu;Shaoting Zhang;Yu Qiao;Lixu Gu;Junjun He

{"title":"MedSegAgent: A Universal and Scalable Multi-Agent System for Instructive Medical Image Segmentation","authors":"Ziyan Huang;Haoyu Wang;Jin Ye;Yuanfeng Ji;Xiaowei Hu;Lihao Liu;Zhikai Yang;Wei Li;Ming Hu;Yanzhou Su;Tianbin Li;Yun Gu;Shaoting Zhang;Yu Qiao;Lixu Gu;Junjun He","doi":"10.1109/JBHI.2026.3677444","DOIUrl":"10.1109/JBHI.2026.3677444","url":null,"abstract":"Medical image segmentation is vital for clinical diagnosis and treatment; however, current solutions face three major limitations: (1) the lack of a universal framework capable of handling diverse modalities and anatomical targets, (2) the limited scalability to adapt to evolving clinical needs and new datasets, and (3) the lack of instructive interfaces that make models usable for non-expert users. To address these challenges, this paper presents MedSegAgent, a universal and scalable multi-agent system for instructive medical image segmentation. Specifically, MedSegAgent comprises five agents: one query parsing agent that processes natural language requests, three coarse-to-fine filtering agents (modality filtering, anatomical filtering, and label selection) for identifying relevant datasets and label values, and one execution agent responsible for model inference and result integration. Based on this framework, MedSegAgent utilizes 23 diverse datasets and pre-trained models to perform 343 types of segmentation across various modalities and anatomical targets. Experimental results demonstrate that MedSegAgent simplifies model selection while maintaining high performance, accurately identifying matching datasets and labels in 94.27% of queries and locating at least one suitable match in 99.03% of queries. MedSegAgent offers a universal and scalable solution for diverse medical image segmentation tasks, bridging the gap between user-friendly queries and the complexities of model selection and deployment.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"30 10","pages":"8751-8762"},"PeriodicalIF":7.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147511743","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

VL-AcneSeg: A Vision-Language Framework for Region-Aware Acne Lesion Segmentation. 基于区域感知的痤疮病灶分割的视觉语言框架。

IF 7.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2026-10-01 DOI: 10.1109/JBHI.2026.3738763

Sukju Oh, Soo Ick Cho, Dae Hun Suh, Sukkyu Sun

{"title":"VL-AcneSeg: A Vision-Language Framework for Region-Aware Acne Lesion Segmentation.","authors":"Sukju Oh, Soo Ick Cho, Dae Hun Suh, Sukkyu Sun","doi":"10.1109/JBHI.2026.3738763","DOIUrl":"https://doi.org/10.1109/JBHI.2026.3738763","url":null,"abstract":"Acne assessment is crucial for clinical decision-making, yet traditional grading and counting are subjective and fail to account for lesion size. While areabased assessment has emerged as a promising alternative, acne segmentation has continued to rely on generalpurpose architectures. To address this gap, we propose VLAcneSeg, a multimodal framework for acne lesion segmentation that leverages CLIP and region-level text prompts to incorporate spatial priors, enabling lesions to be localized across the whole face. Because region-level prompts indicate which facial areas contain lesions, we report a single global prompt, which requires no such information, as our primary setting. On our internal clinical dataset, VLAcneSeg achieves a Dice score of 0.5082 and an IoU of 0.3407 under this protocol, the highest among all compared methods, including recent vision-language segmentation methods that are themselves given region-level prompts; region-level prompting raises these to 0.5296 and 0.3602. Moreover, lesion area measurements derived from our segmentation correlate with IGA scores at a level comparable to expert annotations (Pearson r = 0.719 versus 0.658). Notably, our framework maintains consistent performance across external validation datasets, performing reliably even on uncontrolled smartphone images without requiring additional training or fine-tuning. By pairing a protocol that requires no lesion-location information with area-based severity estimation, this work provides a foundation for objective acne assessment outside the clinic.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":7.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149108870","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

TEAM: Text-Augmented Medical Image Segmentation with Entity-Level Modal Alignment and Masked Modeling. 团队：文本增强医学图像分割与实体级模态对齐和蒙面建模。

IF 7.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2026-10-01 DOI: 10.1109/JBHI.2026.3739542

Ruiyang Li, Rui-Wei Zhao, Rui Feng, Xiaobo Zhang

{"title":"TEAM: Text-Augmented Medical Image Segmentation with Entity-Level Modal Alignment and Masked Modeling.","authors":"Ruiyang Li, Rui-Wei Zhao, Rui Feng, Xiaobo Zhang","doi":"10.1109/JBHI.2026.3739542","DOIUrl":"https://doi.org/10.1109/JBHI.2026.3739542","url":null,"abstract":"Text-augmented medical image segmentation has become a promising approach to improving medical image analysis by incorporating linguistic descriptions into dense prediction, especially for identifying and segmenting complex anatomical structures. However, existing methods often face challenges due to the semantic gap between concise textual descriptions and dense visual context. This would easily cause the over-alignment or under-alignment issues, resulting in suboptimal segmentation performance. In this paper, we propose a novel framework named TEAM to improve the alignment between visual and textual features and mitigate the potential over-alignment and under-alignment problems. Specifically, our approach introduces an entity contrastive learning (ECL) strategy to enhance the fine-grained correspondence between textual and visual features at their semantic entity levels. In addition, we develop the vision-language feature reconstruction (VL-R) module, which combines masked vision modeling (MVM) with masked latent language modeling (MLM) to further strengthen multimodal feature alignment while preserving the critical and discriminative visual features for precise segmentation. Evaluated on the QaTa-COV19, MosMedData+, and Kvasir-SEG benchmarks, our framework consistently outperforms state-of-the-art baselines. Extensive ablation studies and qualitative visualizations further confirm the effectiveness of our design in enhancing cross-modal alignment and segmentation accuracy, highlighting its potential as a robust solution for text-augmented medical image segmentation.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":7.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149135966","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

Interpretable and Adaptive GAN-BiLSTM Approach for Cyber Threat Detection in IoMT-Based Healthcare 5.0 基于iom的医疗保健5.0中网络威胁检测的可解释和自适应GAN-BiLSTM方法。

IF 7.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2026-10-01 Epub Date: 2025-03-23 DOI: 10.1109/JBHI.2025.3573097

Zabeehullah;Fahim Arif;Nauman Ali Khan;Mudassar Ali Khan;Ikram Ud Din;Ahmad Almogren;Ayman Altameem

{"title":"Interpretable and Adaptive GAN-BiLSTM Approach for Cyber Threat Detection in IoMT-Based Healthcare 5.0","authors":"Zabeehullah;Fahim Arif;Nauman Ali Khan;Mudassar Ali Khan;Ikram Ud Din;Ahmad Almogren;Ayman Altameem","doi":"10.1109/JBHI.2025.3573097","DOIUrl":"10.1109/JBHI.2025.3573097","url":null,"abstract":"Healthcare 5.0, driven by the Internet of Medical Things (IoMT), introduces transformative changes in the medical field but also exposes systems to growing cybersecurity threats. While Deep Learning (DL) offers high accuracy in attack detection, its effectiveness is often limited by data imbalance and difficulty in identifying key features dynamically. Additionally, DL models are often criticized for their lack of interpretability, as their internal decision-making remains obscure. To overcome these limitations, this paper presents an explainable and adaptive DL-based security framework. It integrates a Generative Adversarial Network (GAN) to balance the dataset by generating realistic samples for underrepresented attack classes, and employs Bidirectional Long Short-Term Memory (BiLSTM) to identify temporal patterns and critical features. To enhance transparency, SHapley Additive exPlanations (SHAP) and Permutation Feature Importance (PFI) are used for interpreting the model's decisions. Experiments conducted on the NSL-KDD dataset demonstrate the effectiveness of the proposed method, achieving 93.81% accuracy and an F1-score of 82.95%.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"30 10","pages":"8459-8466"},"PeriodicalIF":7.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144132365","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