IEEE Journal of Biomedical and Health Informatics最新文献_第4页

Hybrid Multi-View MRI Fusion for csPCa Diagnosis via Intra- and Inter-View Transformers 基于视点内和视点间变压器的混合多视点MRI融合诊断csPCa。

IF 7.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2026-10-01 Epub Date: 2026-03-10 DOI: 10.1109/JBHI.2026.3672887

Yuchen Zhao;Danyan Li;Teng Zhang;Xiangxue Wang;Jun Xu;Kai Xuan

{"title":"Hybrid Multi-View MRI Fusion for csPCa Diagnosis via Intra- and Inter-View Transformers","authors":"Yuchen Zhao;Danyan Li;Teng Zhang;Xiangxue Wang;Jun Xu;Kai Xuan","doi":"10.1109/JBHI.2026.3672887","DOIUrl":"10.1109/JBHI.2026.3672887","url":null,"abstract":"Accurate diagnosis of clinically significant prostate cancer (csPCa) from multi-view MRI scans (axial, sagittal, coronal) is essential for effective treatment planning and improved outcomes. Although deep learning has advanced prostate MRI analysis, many approaches adopt late fusion strategies aggregating flattened features independently from each view, resulting in loss of spatial information and anatomical correspondence across views, ultimately limiting diagnostic performance. While Vision Transformers offer flexibility for multi-view patches, their memory requirements scale quadratically, hindering efficient concurrent processing. Conversely, Swin Transformers efficiently capture local features but are restricted to single-view processing due to regular-grid input constraints. To overcome these limitations, we propose a hybrid fusion framework that decomposes multi-view integration into iterative intra-view and inter-view interactions across multiple resolutions. The framework preserves spatial coherence and enables fine-grained feature integration while maintaining computational efficiency. Specifically, the <italic>inter-view feature exchange module, based on the Vision Transformer, employs <italic>bridge tokens to summarize information from localized patch windows, reducing memory usage while preserving spatial relationships across views. The <italic>intra-view feature extraction module, built on the Swin Transformer, facilitates dynamic, attention-driven interactions among image patches and bridge tokens within each window. Moreover, shared positional embeddings are explicitly incorporated to enhance spatial correspondence across views. Extensive experiments on a public dataset demonstrate the superiority of our method in csPCa classification. Ablation studies highlight component contributions, while attention map visualizations validate integration of anatomical structures across views.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"30 10","pages":"8763-8773"},"PeriodicalIF":7.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147432619","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

SPSID: A Single-Parameter Shrinkage Inverse-Diffusion for Denoising Gene-Regulatory Networks SPSID：用于去噪基因调控网络的单参数收缩反扩散。

IF 7.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2026-10-01 Epub Date: 2026-03-17 DOI: 10.1109/JBHI.2026.3674779

Hao Chen;Ge Han;Wenze Ding;Clara Grazian

{"title":"SPSID: A Single-Parameter Shrinkage Inverse-Diffusion for Denoising Gene-Regulatory Networks","authors":"Hao Chen;Ge Han;Wenze Ding;Clara Grazian","doi":"10.1109/JBHI.2026.3674779","DOIUrl":"10.1109/JBHI.2026.3674779","url":null,"abstract":"Inferring gene regulatory networks (GRNs) from expression data is a fundamental problem in systems biology, but its accuracy is often undermined by structural noise arising from transitive correlations. These indirect interactions can obscure the true regulatory architecture, leading to a high rate of false positives. To address this, we introduce SPSID (Single-Parameter Shrinkage Inverse-Diffusion), a novel and robust network denoising framework. SPSID is a deterministic post-processing operator applied to an inferred GRN score matrix, rather than a generative diffusion model for gene expression. SPSID employs a principled spectral filter, built upon a shrinkage-regularized inverse-diffusion operator, to mathematically distinguish direct, one-step interactions from multi-step, indirect paths. This approach guarantees numerical stability and, through a fixed default parameter, effectively eliminates the need for data-dependent tuning. We conducted a comprehensive evaluation of SPSID on both extensive simulations and the gold-standard DREAM5 benchmark. The results demonstrate that SPSID outperforms state-of-the-art baseline methods in both AUROC and AUPR, exhibiting good stability across diverse network conditions. Furthermore, it functions as a post-processing tool, elevating the performance of multiple upstream GRN inference methods. By providing a computationally efficient and parameter-free solution to filter structural noise, SPSID offers a readily applicable tool for uncovering the underlying topology of complex biological networks with greater fidelity.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"30 10","pages":"8583-8596"},"PeriodicalIF":7.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147473480","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

Adaptive Feature Selection With Hierarchical Learning for Drug-Target Interaction Prediction 基于层次学习的自适应特征选择药物-靶标相互作用预测。

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.2026.3678266

Zhen Tian;Miao Jiang;Jin Li;Wenjie Zhang;Mingliang Xu

{"title":"Adaptive Feature Selection With Hierarchical Learning for Drug-Target Interaction Prediction","authors":"Zhen Tian;Miao Jiang;Jin Li;Wenjie Zhang;Mingliang Xu","doi":"10.1109/JBHI.2026.3678266","DOIUrl":"10.1109/JBHI.2026.3678266","url":null,"abstract":"Accurate prediction of drug–target interactions (DTIs) is essential for drug discovery and repurposing. Although deep learning has driven substantial progress, critical limitations remain: a singular focus on intermolecular associations results in suboptimal representation learning, and the failure to leverage key features during interactions constrains further performance gains. Here, we propose ASHL-DTI, a novel framework that integrates hierarchical learning with adaptive feature selection to significantly boost both feature quality and model generalizability. Specifically, the hierarchical learning component captures multi-level intramolecular associations to learn more discriminative representations. Simultaneously, we incorporate an adaptive Top-k selection mechanism to retain the most predictive features, facilitating effective interaction between drugs and targets. Experimental results across multiple public benchmark datasets demonstrate that ASHL-DTI achieves superior performance compared with state-of-the-art approaches. Moreover, ASHL-DTI exhibits strong generalization ability in predicting novel drug–target pairs, underscoring its potential in drug discovery.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"30 10","pages":"8624-8634"},"PeriodicalIF":7.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147580949","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

Diffusion Model With Relation-Aware Attention and Edge-Aware Constraint for Multi-Modal Brain Tumor Segmentation 基于关系感知注意力和边缘感知约束的多模态脑肿瘤分割扩散模型。

IF 7.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2026-10-01 Epub Date: 2025-09-08 DOI: 10.1109/JBHI.2025.3607446

Xu Xu;Jing Yang;Dayu Hu;Muhammad Attique Khan;Lip Yee Por;Congsheng Li

{"title":"Diffusion Model With Relation-Aware Attention and Edge-Aware Constraint for Multi-Modal Brain Tumor Segmentation","authors":"Xu Xu;Jing Yang;Dayu Hu;Muhammad Attique Khan;Lip Yee Por;Congsheng Li","doi":"10.1109/JBHI.2025.3607446","DOIUrl":"10.1109/JBHI.2025.3607446","url":null,"abstract":"Multi-modal brain tumors segmentation is a critical step for diagnosing and monitoring brain-related disease. Many studies have developed models for this task, but two challenges remain, i.e., weak feature aggregation and poorly segmented edges. To address these issues, we develop an improved Diffusion model with relation-aware attention and edge-aware constraint, namely Diff-RE, for multi-modal brain tumor segmentation. Specifically, the volume data and noisy segmentation label map are paralleled fed into encoder module to extract high-level features. During training, weights are shared to ensure consistency. Then, extracted features are channel-wise concatenated and passed through the relation-aware attention module, which enhances appearance features using global structural relationships. Finally, the decoder module processes the attention-enhanced features to generate segmentation results. To improve boundary accuracy, an edge-aware constraint module is introduced during training. Our framework is trained and evaluated using three benchmark datasets, i.e., BraTS 2018, 2019, and 2020. Experimental results demonstrate that Diff-RE is effective and highlight its superiority over peer methods.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"30 10","pages":"8190-8203"},"PeriodicalIF":7.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145023212","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

Deciphering lncRNA-Disease Associations Based on Multi-Representation Fusion and Boosting With Gaussian Process 基于多表示融合和高斯增强的lncrna -疾病关联解译。

IF 7.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2026-10-01 Epub Date: 2026-03-10 DOI: 10.1109/JBHI.2026.3672737

Longlong Liu;Liangliang Huang;Zongzheng Bai;Yan Jiang;Lihong Peng

{"title":"Deciphering lncRNA-Disease Associations Based on Multi-Representation Fusion and Boosting With Gaussian Process","authors":"Longlong Liu;Liangliang Huang;Zongzheng Bai;Yan Jiang;Lihong Peng","doi":"10.1109/JBHI.2026.3672737","DOIUrl":"10.1109/JBHI.2026.3672737","url":null,"abstract":"LncRNA-disease association (LDA) identification can provide valuable insights for understanding disease pathogenesis. Existing most deep learning-based LDA prediction models remain limitations in effectively fusing various features of lncRNAs and diseases and accurately classifying unknown lncRNA-disease pairs (LDPs). Here, we introduce a deep learning-based LDA prediction framework named LDA-RMGPB based on multi-representation fusion and boosting with Gaussian process. First, a randomized singular value decomposition model is presented to extract LDP linear features. Subsequently, a masked graph autoencoder is exploited to learn LDP nonlinear features. Finally, a boosting algorithm with Gaussian process takes the concatenation of LDP linear and nonlinear features as inputs and classifies unlabeled LDPs. To measure the LDA-RMGPB performance, we performed a series of experiments. Using six evaluation metrics, under four different 5-fold cross-validation strategies (i.e., cross validations on lncRNAs, diseases, LDPs, independent lncRNAs and independent diseases), LDA-RMGPB greatly surpassed seven state-of-the-art prediction methods on two LDA datasets. Further analysis, including ablation study, CeRNA theory analysis, lncRNA-related therapeutic drug analysis, and survival analysis, elucidated that LDA-RMGPB achieved superior LDA identification ability. Moreover, we predicted that lncRNAs ATP6V1G2-DDX39B and PSORS1C3 could have dense linkages with breast cancer and prostatic neoplasms, respectively. We anticipate that LDA-RMGPB contributes to the discovery of novel therapeutic molecular targets across diverse diseases.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"30 10","pages":"8570-8582"},"PeriodicalIF":7.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147432570","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

Leads-Adaptive Fetal Electrocardiogram Extraction Using Attention-Based BiLSTM 基于注意的BiLSTM引线自适应胎儿心电图提取。

IF 7.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2026-10-01 Epub Date: 2026-03-10 DOI: 10.1109/JBHI.2026.3672909

Ying Zhu;Le Xu;Shenao Chen;Limin Diao;Yi Zheng;Varun G Menon

{"title":"Leads-Adaptive Fetal Electrocardiogram Extraction Using Attention-Based BiLSTM","authors":"Ying Zhu;Le Xu;Shenao Chen;Limin Diao;Yi Zheng;Varun G Menon","doi":"10.1109/JBHI.2026.3672909","DOIUrl":"10.1109/JBHI.2026.3672909","url":null,"abstract":"Extracting the fetal electrocardiogram (fECG) from the abdominal electrocardiogram (AECG) will help clinicians accurately discern fetal cardiac rhythm patterns. Nevertheless, the intricacies inherent in the clinical setting often precipitate signal anomalies within AECG recordings, thereby rendering traditional extraction methodologies suboptimal. This work proposes a deep learning-based fECG extraction method designed for the adaptive extraction of fECG signals from multi-lead AECG inputs. The proposed methodology is predicated upon a bidirectional long short-term memory (BiLSTM) architecture, augmented with a deep supervision subnetwork and an attention mechanism module. The attention module quantifies the inter-channel relevance of the input AECG through the computation of attention weights, facilitating subsequent feature fusion along the channel axis. This process effectively mitigates the impact of defective channels on the output. Comprehensive evaluations were conducted on publicly available datasets, encompassing scenarios with channel loss and benchmarked against established models. The results demonstrate the proposed model's efficacy in extracting fECG signals under diverse channel defect conditions. Ablation studies further validate the critical role of the attention module in enhancing the model's resilience to channel anomalies. Additionally, the reliability of the extracted fECG signals was corroborated through experiments involving input signal masking. The method proposed in this work is helpful for the clinical deployment of the fECG extraction in fetal cardiac rhythm assessment.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"30 10","pages":"8907-8916"},"PeriodicalIF":7.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147432635","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

RIHA: Report-Image Hierarchical Alignment for Radiology Report Generation RIHA：用于放射学报告生成的报告-图像分层对齐。

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.2026.3670023

Yucheng Chen;Yang Yu;Yufei Shi;Conghao Xiong;Xulei Yang;Si Yong Yeo

{"title":"RIHA: Report-Image Hierarchical Alignment for Radiology Report Generation","authors":"Yucheng Chen;Yang Yu;Yufei Shi;Conghao Xiong;Xulei Yang;Si Yong Yeo","doi":"10.1109/JBHI.2026.3670023","DOIUrl":"10.1109/JBHI.2026.3670023","url":null,"abstract":"Radiology report generation (RRG) has emerged as a promising approach to alleviate radiologists’ workload and reduce human errors by automatically generating diagnostic reports from medical images. A key challenge in RRG is achieving fine-grained alignment between complex visual features and the hierarchical structure of long-form radiology reports. Although recent methods have improved image-text representation learning, they often treat reports as flat sequences, overlooking their structured sections and semantic hierarchies. This simplification hinders precise cross-modal alignment and weakens RRG accuracy. To address this challenge, we propose RIHA (Report-Image Hierarchical Alignment Transformer), a novel end-to-end framework that performs multi-level alignment between radiological images and their corresponding reports across paragraph, sentence, and word levels. This hierarchical alignment enables more precise cross-modal mapping, essential for capturing the nuanced semantics embedded in clinical narratives. Specifically, RIHA introduces a Visual Feature Pyramid (VFP) to extract multi-scale visual features and a Text Feature Pyramid (TFP) to represent multi-granularity textual structures. These components are integrated through a Cross-modal Hierarchical Alignment (CHA) module, leveraging optimal transport to effectively align visual and textual features across various levels. Furthermore, we incorporate Relative Positional Encoding (RPE) into the decoder to model spatial and semantic relationships among tokens, enhancing the token-level alignment between visual features and generated text. Extensive experiments on two benchmark chest X-ray datasets, IU-Xray and MIMIC-CXR, demonstrate that RIHA outperforms existing state-of-the-art models in both natural language generation and clinical efficacy metrics.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"30 10","pages":"8737-8750"},"PeriodicalIF":7.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147354857","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

Development of a Multimodal Obstructive Sleep Apnea Diagnostic Prediction Model Using Two-Dimensional Facial Images and Clinical Data 利用二维面部图像和临床数据建立多模态阻塞性睡眠呼吸暂停诊断预测模型。

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.2026.3678664

Hakje Yoo;Gangmi Kim;Taeuk Kim;Hyo Yeol Kim;Yong Gi Jung;Sang Duk Hong;Myung Jin Chung;Gwanghui Ryu

{"title":"Development of a Multimodal Obstructive Sleep Apnea Diagnostic Prediction Model Using Two-Dimensional Facial Images and Clinical Data","authors":"Hakje Yoo;Gangmi Kim;Taeuk Kim;Hyo Yeol Kim;Yong Gi Jung;Sang Duk Hong;Myung Jin Chung;Gwanghui Ryu","doi":"10.1109/JBHI.2026.3678664","DOIUrl":"10.1109/JBHI.2026.3678664","url":null,"abstract":"Obstructive sleep apnea (OSA) is a prevalent disorder in middle-aged and obese men with increased risk of cardiovascular disease, metabolic dysfunction, and neurocognitive impairments. Delays in diagnosis and treatment of patients with OSA increase long-term all-cause mortality. In this study, we developed a multimodal artificial intelligence (AI) model that utilizes two-dimensional facial photograph, cephalometric radiograph, and clinical data to enhance OSA screening. Clinical parameters of sex, age, body mass index, neck circumference, and abdominal circumference and sleep questionnaires were assessed. Data from 710 patients who underwent polysomnography were used to train and validate a deep learning model combining ShuffleNet-V2 for image feature extraction and a deep neural network for clinical data analysis. The model was evaluated using five-fold cross-validation and a holdout test set. Performance metrics were area under the curve (AUC), sensitivity, specificity, accuracy, and F1 score. The trimodal AI model significantly outperformed uni- and bimodal approaches, achieving an AUC of 0.859 in distinguishing moderate-severe OSA from normal-mild OSA. Subgroup analyses of data from obese, elderly, and male patients showed higher classification accuracy, and data from patients with smaller abdominal circumferences had the lowest sensitivity compared to other subgroups. The Grad-CAM analysis demonstrated that the model focused on airway structures and low part of face, aligning with clinical expectations. This study presents a novel AI-driven OSA screening approach that integrates data from facial images and clinical data. This AI-based OSA screening model may facilitate early diagnosis of OSA and improve patient outcomes.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"30 10","pages":"8635-8644"},"PeriodicalIF":7.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147580956","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

Autonomously Adjusting Multi-Relational Hypergraphs Structure for Predicting circRNA-miRNA Associations 预测circRNA-MiRNA关联的自主调节多关系超图结构。

IF 7.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2026-10-01 Epub Date: 2025-01-17 DOI: 10.1109/JBHI.2025.3531427

Wenjing Yin;Shudong Wang;Yuanyuan Zhang;Sibo Qiao;Shuqiang Wang;Fazlullah Khan;Ryan Alturki;Zhihan Lyu

{"title":"Autonomously Adjusting Multi-Relational Hypergraphs Structure for Predicting circRNA-miRNA Associations","authors":"Wenjing Yin;Shudong Wang;Yuanyuan Zhang;Sibo Qiao;Shuqiang Wang;Fazlullah Khan;Ryan Alturki;Zhihan Lyu","doi":"10.1109/JBHI.2025.3531427","DOIUrl":"10.1109/JBHI.2025.3531427","url":null,"abstract":"Identifying circRNA-miRNA associations is critical for understanding gene regulatory mechanisms, discovering new biomarkers, and developing therapeutic strategies. The ongoing advancement of autonomous artificial intelligence (AI) technology, particularly in relational and graph learning, enables researchers to develop autonomous AI prediction models to process and analyze existing associations. These models can autonomously extract meaningful patterns and relationships, thereby accurately predicting unknown associations and providing efficient auxiliary tools for traditional experimental methods. Unfortunately, validated reliable circRNA-miRNA associations are often very sparse, making it difficult to learn the intrinsic associations between circRNAs and miRNAs from a static explicit graph structure. To alleviate this problem, we propose a new autonomous AI prediction framework that combines local simple associations with global high-order interactions for joint learning. The framework captures locally embedded representations based on the similarity relationships of RNA molecule attributes, and aggregates contextual information across the global region in different relational modes by applying message passing on multi-relational hypergraphs. Furthermore, we design an autonomous adjustment strategy for multi-relational hypergraphs. This strategy enables adaptive learning of potential node correlations and autonomous construction of hypergraph structures more suitable for downstream tasks, thereby enhancing higher-order relationships between RNA molecules and improving prediction performance and generalization capabilities. Experimental results on three real-world datasets demonstrate that this framework performs excellently in predicting circRNA-miRNA associations, significantly outperforming existing prediction models.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"30 10","pages":"8244-8253"},"PeriodicalIF":7.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143541677","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

Imaging Informatics for Thermal Ablation: CT Thermometry-Based Tissue Injury Mapping. 热消融成像信息学：基于CT温度测量的组织损伤制图。

IF 7.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2026-10-01 DOI: 10.1109/JBHI.2026.3738815

Tiansheng Liu, Xiaolong Wang, Jihong Chen, Jie Chen, Zhuting Fang, Bingwei He

{"title":"Imaging Informatics for Thermal Ablation: CT Thermometry-Based Tissue Injury Mapping.","authors":"Tiansheng Liu, Xiaolong Wang, Jihong Chen, Jie Chen, Zhuting Fang, Bingwei He","doi":"10.1109/JBHI.2026.3738815","DOIUrl":"https://doi.org/10.1109/JBHI.2026.3738815","url":null,"abstract":"Sparse CT acquisition during CT-guided thermal ablation limits direct estimation of cumulative ther mal injury, which depends on temperature history rather than endpoint temperature alone. This study developed an imaging-informatics pipeline for near real-time Arrhenius injury mapping from workflow-compatible endpoint CT thermometry. Endpoint temperature fields were derived from ROI-level HU-temperature calibration and deployed in baseline-referenced form. Protocol-specific normalized temperature-history templates reconstructed heating trajectories at 180 and 300 s, and precompiled endpoint temperature-to-logΩ mappings avoided voxel-wise temporal integration at runtime. Validation was limited to ex vivo liver specimens, using porcine tissue for model de velopment and validation and two bovine cases for ex ploratory cross-tissue evaluation. Across five indepen dent experiments, per-update latency was 793.7-1688.0 ms (1198.5±434.3 ms). The logΩ ≥ 0 delineation yielded mean absolute major- and minor-axis errors of 0.96±0.73 mm and 1.98 ± 1.37 mm, respectively, with Dice 0.9151 ± 0.0311 and HD952.07±0.27 mmrelative to gross specimen boundaries. Dice and HD95 quantify post-alignment two-dimensional contour agreement after scale normalization rather than absolute localization accuracy. Leakage-controlled calibration analysis indicated limited out-of-sample CT thermometry performance, whereas two-level validation supported the stability of the endpoint-temperature-to-injury mapping. These ex vivo results demonstrate protocol-specific feasibility and motivate independent thermometry validation, uncertainty propagation, and prospective in vivo evaluation.","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":"149108788","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