IEEE Journal of Biomedical and Health Informatics最新文献

{"title":"CPGNet: Multimodal Graph Learning With Hierarchical Category Guidance for Multi-Label Whole Slide Image Classification","authors":"Haoyun Zhao;Dapeng Tao;Yibing Zhan;Jun Ni;Yang Chen","doi":"10.1109/JBHI.2025.3620443","DOIUrl":"10.1109/JBHI.2025.3620443","url":null,"abstract":"The analysis of WSI categories in digital pathology serves as a critical basis for clinicians’ decision-making regarding the diagnosis, treatment, and prognosis of cancer patients. However, current automated methods for cancer type identification are predominantly formulated as single-label classification problems. These methods typically rely on datasets with relatively balanced and abundant samples, where each sample belongs to a single category. This approach does not fully align with real-world clinical scenarios, where cancer subtypes often exhibit multi-label characteristics and class imbalance, posing significant challenges. To address this issue, this paper proposes CPGNet, a category-prompted graph network designed as a multi-label whole slide image classifier that is better suited for clinical scenarios. CPGNet employs the MaskSLIC algorithm for superpixel segmentation of whole slide images, effectively capturing the nonlinear spatial distribution of cellular and tissue structures. The segmented superpixels are then encoded as graph nodes along with their corresponding features, while edges and edge features are constructed to abstractly model WSIs as graphs. Furthermore, the method introduces a Graph Local-Global Feature Interaction module, which aggregates features of neighboring nodes and edges via a Graph Neural Network to capture local information while leveraging a multi-head self-attention mechanism to model global dependencies, mimicking the diagnostic behavior of pathologists. Additionally, the Vision-Category Interaction module exploits semantic relationships between categories to guide visual feature classification, providing supplementary cues for accurate predictions. To enhance the model’s focus on hard-to-classify positive samples, we also implement a reweighting strategy. The proposed approach is evaluated on a private dataset (YNLUAD) and two public challenge datasets (BCNB and AGGC22). The experimental results demonstrate the superiority, generalizability, and robustness of CPGNet.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"30 10","pages":"8327-8341"},"PeriodicalIF":7.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145285986","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}

{"title":"IEEE Journal of Biomedical and Health Informatics Publication Information","authors":"","doi":"10.1109/JBHI.2026.3733795","DOIUrl":"https://doi.org/10.1109/JBHI.2026.3733795","url":null,"abstract":"","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"30 10","pages":"C2-C2"},"PeriodicalIF":7.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11720472","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097553","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}

{"title":"BWS-Net: An Optimal Deep Learning Architecture for the Anterior Bladder Wall Segmentation Using Ultrasound Imaging","authors":"Manali Saini;Yifei Jiang;Tuhinangshu Gangopadhyay;David P. Rosen;Azra Alizad;Mostafa Fatemi","doi":"10.1109/JBHI.2026.3675965","DOIUrl":"10.1109/JBHI.2026.3675965","url":null,"abstract":"Urodynamic tests are used to assess bladder function by measuring detrusor pressure, which requires invasive catheterization. Ultrasound bladder vibrometry offers a non-invasive approach to evaluate bladder compliance for diagnostic purposes by estimating detrusor pressure from ultrasound-generated Lamb waves in the bladder wall. Therefore, it is crucial to precisely segment the anterior portion of bladder wall prior to these assessments. Traditional segmentation methods are time-consuming and require manual intervention, while deep learning offers a promising alternative. Existing deep learning approaches are limited and often lack clinical validation. Therefore, we propose a novel deep learning network for precise segmentation of anterior bladder wall. It comprises blueprint separable convolutional layers in an encoder-decoder structure with adaptive attention-based skip connections. Performance evaluation on 8592 distinct images acquired from 64 patients using 5-fold cross-validation demonstrates that it achieves a mean Dice score and sensitivity of 0.82 and 0.85 respectively, along with a mean root mean square error of <inline-formula><tex-math>$ 0.67 pm 0.35 ;text{mm}$</tex-math></inline-formula> between the thickness of predicted and ground truth portions of the bladder wall. Blueprint separable convolutions and adaptive attention-skip connections improve segmentation performance with fewer computations compared to respective standard counterparts. A comparative analysis demonstrates improvements between 2-20% in the dice score with respect to some but one of the existing networks and reductions in computational complexity by <inline-formula><tex-math>$94-96%$</tex-math></inline-formula> with respect to all existing networks analyzed in this work. Therefore, the proposed method can be effective for accurate bladder wall segmentation and demonstrates potential for real-time application in clinical settings.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"30 10","pages":"8774-8786"},"PeriodicalIF":7.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11443231","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147485670","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}

{"title":"Deep Learning-Based Vitiligo Activity Evaluation Using Wood’s Lamp Imaging: A Clinical Decision Support","authors":"Zheng Wang;Zixuan Nie;Hui Hu;Kaibin Lin;Chong Wang;Hongyang Fu;Jianglin Zhang","doi":"10.1109/JBHI.2026.3670244","DOIUrl":"10.1109/JBHI.2026.3670244","url":null,"abstract":"Vitiligo is an autoimmune disorder characterized by heterogeneous and unpredictable depigmentation, which poses substantial challenges for objective disease monitoring and treatment evaluation in routine clinical practice. To address the lack of automated and quantitative follow-up tools, we designed and validated an end-to-end deep learning–based system using Wood’s lamp imaging to support lesion localization, longitudinal tracking, and activity assessment. The framework employs Mask R-CNN for automated lesion detection and segmentation, followed by quantitative pigmentation-state analysis and disease activity evaluation using t-distributed stochastic neighbor embedding (t-SNE) and the Vitiligo Disease Activity (VIDA) score. Disease progression is further characterized through risk, correlation, and survival analyses. Quantitatively, the Mask R-CNN model achieved robust performance, with a Dice coefficient of 90.5%, a mean intersection-over-union of 83.1%, and an area under the curve of 0.9567 on external validation, supporting reliable lesion delineation under Wood’s lamp imaging. When benchmarked against representative segmentation backbones (U-Net, U-Net++, U2-Net, and DeepLabV3), the Mask R-CNN–based pipeline yielded the highest mean IoU (83.1 ± 3.9% ) while maintaining a competitive Dice score (90.5 ± 1.38% ). t-SNE analysis effectively separated pigmentation states, revealing distinct patterns of hyperpigmentation, hypopigmentation, and apigmentation that were consistent with clinical activity. Longitudinal survival analysis demonstrated that stable lesions derived significant benefit from sustained treatment, whereas active lesions exhibited elevated early risk. Age-stratified analysis identified the highest relapse risk in patients aged 11–20 years, while patients aged 71–80 years showed reduced disease stability. Regression analysis further indicated that, during the stable phase, increased hypopigmentation was associated with subsequent pigment regeneration, underscoring stage-dependent treatment effects. Integrating deep learning–based lesion analysis with clinically grounded activity and longitudinal risk modeling enables precise, objective monitoring of vitiligo and supports personalized, stage-aware treatment decision-making.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"30 10","pages":"8672-8683"},"PeriodicalIF":7.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147354802","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}

{"title":"Bridging Stepwise Lab-Informed Pretraining and Knowledge-Guided Learning for Diagnostic Reasoning","authors":"Pengfei Hu;Chang Lu;Fei Wang;Yue Ning","doi":"10.1109/JBHI.2026.3670251","DOIUrl":"10.1109/JBHI.2026.3670251","url":null,"abstract":"Despite the growing use of Electronic Health Records (EHR) for AI-assisted diagnosis prediction, most data-driven models struggle to incorporate clinically meaningful medical knowledge. They often rely on limited ontologies, lacking structured reasoning capabilities and comprehensive coverage. This raises an important research question: Will medical knowledge improve predictive models to support stepwise clinical reasoning as performed by human doctors? To address this problem, we propose DuaLK, a dual-expertise framework that combines two complementary sources of information. For external knowledge, we construct a Diagnosis Knowledge Graph (KG) that encodes both hierarchical and semantic relations enriched by large language models (LLM). To align with patient data, we further introduce a lab-informed proxy task that guides the model to follow a clinically consistent, stepwise reasoning process based on lab test signals. Experimental results on two public EHR datasets demonstrate that DuaLK consistently outperforms existing baselines across four clinical prediction tasks. These findings highlight the potential of combining structured medical knowledge with individual-level clinical signals to achieve more accurate and interpretable diagnostic predictions.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"30 10","pages":"8404-8416"},"PeriodicalIF":7.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147354885","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}

{"title":"Explainable Convolutional Channel Ranking (ECCR) for EEG-Based Detection of Idiopathic Absence Seizures","authors":"Fozia Rajbdad;Murtaza Aslam;Jian Xu;Mort Naraghi-Pour","doi":"10.1109/JBHI.2026.3678222","DOIUrl":"10.1109/JBHI.2026.3678222","url":null,"abstract":"Accurate identification of EEG electrodes associated with epilepsy is essential for developing real-time diagnostic applications. This paper introduces the Explainable Convolutional Channel Ranking (ECCR) method for identifying diagnostically relevant EEG channels for Idiopathic Absence Seizure (IAS) detection by analyzing channel-specific feature contributions learned by a convolutional neural network (CNN). Unlike traditional saliency-based approaches that focus only on highly activated regions or pool contributions across seizure types and spatial locations, ECCR retains channel-specific contribution patterns and shows that channels with moderate contribution levels offer the most discriminative and physiologically consistent information. This finding suggests that channels with very high saliency are often affected by noise or subject-specific artifacts, while medium-contribution channels capture more stable seizure-related information dynamics. In 10-fold cross-validation, the ECCR-guided CNN achieved 82.21% accuracy and 92.01% sensitivity, while leave-one-subject-out (LOSO) validation yielded 73.78% accuracy, demonstrating improved subject-independent performance under a leakage-controlled protocol; ECCR consistently selected fronto–central, temporo–parietal, and occipital regions, reducing 29 channels to 7 in the subject-dependent evaluation. A validation using a Random Forest classifier confirmed that ECCR-selected channels provided stronger detection power than those excluded. These findings suggest that ECCR can guide the design of compact, interpretable EEG systems, supporting more reliable deep learning solutions for IAS diagnosis.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"30 10","pages":"8531-8541"},"PeriodicalIF":7.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147528449","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}

{"title":"Wavelet-Driven Spatial Frequency Mamba Network for Spine Image Segmentation","authors":"Yuefeng Zhao;Qifei Wang;Nai Zhou;Pengfei Sun;Junjie Wang;Nannan Hu","doi":"10.1109/JBHI.2026.3678728","DOIUrl":"10.1109/JBHI.2026.3678728","url":null,"abstract":"Accurate spine segmentation is crucial for diagnosing and treating various spine diseases. Recently, Mamba-based methods have been widely applied in medical image segmentation. However, the spatial domain scanning strategy of Mamba fails to fully capture the fine anatomical structures and global dependencies of the spine. Moreover, existing frequency-enhanced methods often suffer from the loss of spatial localization. To address these challenges, we propose the Wavelet-Driven Spatial Frequency Mamba Network (WDSFM-Net). Specifically, we integrate the Discrete Wavelet Transform (DWT) into Mamba to construct the Spatial-Frequency Mamba Block (SFMB). By decomposing features into distinct frequency subbands, SFMB explicitly captures global structural context from low-frequency components while enhancing local anatomical details through high-frequency components. To accommodate specific spinal morphology, the Global Strip Pooling Attention (GSPA) module aggregates directional contexts to model the elongated and anisotropic spinal anatomy, while the Multi-Scale Attention Enhancement (MSAE) module employs multi-scale convolutions to adapt to significant vertebral scale variations. Additionally, we introduce a Dual-Domain Loss (DDL) function, which optimizes both spatial and frequency domain representations for robust training. We evaluated our WDSFM-Net on two public spine MRI datasets. The results show that the WDSFM-Net outperforms other state-of-the-art methods, achieving average Dice similarity coefficients of 0.8885 and 0.8669 in the Spider and MRSpine datasets, respectively.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"30 10","pages":"8697-8709"},"PeriodicalIF":7.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147580924","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}

{"title":"MPAExpo-LM: Fine-Tuned Large Language Model for Mycophenolic Acid Exposure Estimation After Renal Transplantation","authors":"Dehua Chen;Huilin Liu;Qingyuan Ge;Ming Zuo;Huimin An;Quan Zhou;Shangxi Fu;Peijun Zhou;Xianghui Wang;Bing Chen;Kun Shao","doi":"10.1109/JBHI.2026.3678339","DOIUrl":"10.1109/JBHI.2026.3678339","url":null,"abstract":"An accurate estimation of mycophenolic acid (MPA) exposure after renal transplantation is important for reducing acute rejection in patients treated with mycophenolate mofetil (MMF) or enteric-coated mycophenolate sodium (MPS). We propose MPAExpo-LM, a fine-tuned large language model with a differential attention denoising module for this task. Experiments on real-world datasets demonstrate that MPAExpo-LM achieves superior results compared to existing methods in the task, offering a new pathway for precise immunosuppressive therapy. It attains an RMSE of 8.78 mg·h/L using three sampling points (C₂, C₄, C₈) and 7.43 mg·h/L with four points (C_0.5, C₂, C₆, C₈). Furthermore, we conducted additional fine-tuning by incorporating a limited amount of data from external data source from another hospital. In the subsequent multi-center validation, the model demonstrated robust generalization, achieving RMSEs of 7.342 mg·h/L (three points) and 7.281 mg·h/L (four points). Via reinforcement fine-tuning, the model demonstrates strong generalization capability in cross-hospital tests.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"30 10","pages":"8659-8671"},"PeriodicalIF":7.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147580947","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}

{"title":"CFRAFN: A Cross-Feature Residual Attention Fusion Network for Major Depressive Disorder Prediction Using Clinical Voice Recordings","authors":"Rumo Pan;Sidu Feng;Yi Sun;Jinqiu Xu;Tianzhang Zhai;Xiaochun Wu;Liangliang Tan;Yonggui Yuan;Hanlin Cao;Maierhaba Maimaitimin;Qing Yao;Hao Guo;Mah Wing Xuan;Jian Li;Zhi Xu","doi":"10.1109/JBHI.2026.3677472","DOIUrl":"10.1109/JBHI.2026.3677472","url":null,"abstract":"Major depressive disorder (MDD) is a prevalent mental disorder with a significant burden on individuals and society, and timely identification and intervention are essential for effective management. Voice data have been used as behavioral indicators of MDD, offering valuable insights into an individual's mental state. In this study, we collected voice data from 221 patients diagnosed with MDD at the inpatient ward of the Department of Psychiatry and Psychosomatics, Zhongda Hospital, Southeast University, alongside 113 healthy controls, to construct the Chinese depressive voice dataset. We proposed the cross-feature residual attention fusion network (CFRAFN), which leverages extended Geneva minimalistic acoustic parameter set features along with high-dimensional embeddings extracted from the pretrained VGGish model to effectively capture MDD-associated phonetic patterns. Specifically, CFRAFN utilizes differentiated residual blocks to maintain training stability in deep hierarchical structure. Furthermore, the self-attention fusion strategy dynamically weighted the significance of each feature modality, ensuring effective feature integration and consequently improving MDD prediction accuracy. Experimental results demonstrated that CFRAFN achieved an excellent predictive performance with an area under the receiver operating characteristic curve of 0.924 in an independent test set, and significantly outperformed 11 baseline models across 5-fold cross-validation.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"30 10","pages":"8645-8658"},"PeriodicalIF":7.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147511696","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}

{"title":"Multi-Modal Depression Recognition Via Label-Aware Contrastive Learning and Cross-Modal Alignment.","authors":"Shiqi Li, Xiaoyu Liu, Hao Zhang, Kai Xie, Xueqian Su, Liang Yang, Xiaochao Fan, Yufeng Diao","doi":"10.1109/JBHI.2026.3722700","DOIUrl":"https://doi.org/10.1109/JBHI.2026.3722700","url":null,"abstract":"<p><p>The diagnostic accuracy of depression is hindered by its heterogeneous presentation and the insufficiency of unimodal data. To address this, we introduce a novel framework, Multi-Modal Depression Recognition via Label-Aware Contrastive Learning and Cross-Modal Alignment (MMDR-LCA). Our core technical contribution is a cross-modal alignment mechanism that systematically models the dyadic relationships between text-audio, text-video, and audio-video modalities. Unlike conventional fusion, this mechanism explicitly mitigates latent modality conflicts, such as positive facial expressions deceptively masking negative verbal content, by extracting aligned and complementary emotional cues. Furthermore, this mechanism is integrated into a label-aware supervised contrastive learning framework. By leveraging continuous severity scores to construct soft-label contrastive losses, our approach bridges the semantic gap between symptom regression and binary diagnosis. This strategy encourages samples with similar psychological states to form closer representations, thereby mitigating the adverse effect of class imbalance in clinical datasets. Experimental results on the public E-DAIC benchmark, using the official training, validation, and test splits, show that MMDR-LCA achieves an MAE of 3.85 and an F1-score of 0.76. In addition, imbalance-aware analyses report sensitivity, specificity, and balanced accuracy, and a controlled class-balanced sampling experiment is included to examine the effect of the depressed/healthy imbalance. Comprehensive ablation studies further verify that cross-modal alignment helps handle inter-modal heterogeneity, while contrastive learning enhances discriminative decision boundaries and overall robustness. This work establishes a promising paradigm for AI-driven depression assessment.</p>","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":"149136040","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}