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

Continuous Monitoring of Sleep-Related Biomarkers via a Nearable Solution Based on Fiber Bragg Grating Technology. 基于光纤光栅技术的近距离连续监测睡眠相关生物标志物。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-04-10 DOI: 10.1109/JBHI.2025.3559724

Francesca De Tommasi, Federico D'Antoni, Daniela Lo Presti, Sergio Silvestri, Giancarlo Fortino, Emiliano Schena, Mario Merone, Carlo Massaroni

{"title":"Continuous Monitoring of Sleep-Related Biomarkers via a Nearable Solution Based on Fiber Bragg Grating Technology.","authors":"Francesca De Tommasi, Federico D'Antoni, Daniela Lo Presti, Sergio Silvestri, Giancarlo Fortino, Emiliano Schena, Mario Merone, Carlo Massaroni","doi":"10.1109/JBHI.2025.3559724","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3559724","url":null,"abstract":"This study explores the innovative application of a nearable solution (i.e., mattress) based on fiber Bragg grating (FBG) technology for continuously monitoring of critical sleep-related biomarkers. Based on biocompatible silicone compounds, the mattress embeds thirteen strategically positioned FBG sensors to detect bed occupancy, sleeping posture, respiratory rate (RR), and heart rate (HR). Our experimental protocol involves ten participants who underwent simulated sleeping conditions to evaluate the mattress's performance across different postures and respiratory patterns. Employing traditional machine learning algorithms, including decision tree, support vector machine (SVM), and Naïve-Bayes classifiers, the mattress achieves 100$%$ accuracy in bed occupancy detection. It also effectively distinguishes between axial and lateral sleeping positions, with SVM achieving the highest accuracy of 78.4$%$ for axial versus lateral differentiation and convolutional neural networks achieving 75.9$%$ in distinguishing left from right positions. Additionally, for most participants, the system successfully estimates RR and HR with mean absolute errors of less than 0.7 breaths per minute and 4 bpm, respectively, across various breathing patterns in terms of frequencies and amplitudes employing different algorithms (frequency and time-domain approaches). The promising findings highlight the potential of the proposed system for a comprehensive evaluation of sleep-related breathing disorders in clinical and home settings.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143962981","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 Deep Learning-Based Approach for the Diagnostic of Brucellar Spondylitis in Magnetic Resonance Images. 基于深度学习的布鲁氏杆菌脊柱炎磁共振影像诊断方法。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-04-10 DOI: 10.1109/JBHI.2025.3559909

Dan Shao, Jinquan Wei, Binyang Wang, Zhijun Wang, Pengying Niu, Lvlin Yang, Guangzhao Zhang, Pu Chen, Lin Lin, Jinhan Lv, Wei Zhao

{"title":"A Deep Learning-Based Approach for the Diagnostic of Brucellar Spondylitis in Magnetic Resonance Images.","authors":"Dan Shao, Jinquan Wei, Binyang Wang, Zhijun Wang, Pengying Niu, Lvlin Yang, Guangzhao Zhang, Pu Chen, Lin Lin, Jinhan Lv, Wei Zhao","doi":"10.1109/JBHI.2025.3559909","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3559909","url":null,"abstract":"Brucellar spondylitis (BS), a prevalent zoonotic disease caused by Brucella, poses a significant global health threat. Accurate and timely diagnosis of BS is crucial for effective treatment; however, no specialized deep learning model has been developed for detecting BS in MR images. In this study, we proposed Brucella Spondylitis MRI Diagnosis Network (BSMRINet), a fully automated diagnostic framework designed for the detection of BS from T2-weighted (T2W) MR images. The model was developed and validated using 582 cohorts collected from four hospitals between January 2018 and August 2023. The BSMRINet architecture comprised two key modules. The vertebral body lesion detection module was designed to detect BS in intact vertebral bodies by integrating a corner detection algorithm with a ResNet-based deep learning model. This module provided accurate identification and localization of potential lesions of Brucella and calculated intervertebral disc height (DH) values. The spine lesion detection module was specifically designed to detect BS in damaged vertebral bodies by utilizing a DenseNet architecture with modified squeeze-and-excitation (scSE) networks. This module further evaluated paravertebral injuries, including abscess formation, soft tissue swelling, and joint involvement. BSMRINet demonstrated strong robustness and generalization across both internal and external validation phases. Additionally, it outperformed two radiologists with 10 to 15 years of experience in diagnosing spinal MR images. The results suggested that BSMRINet can assist in the diagnostic process of BS and enhance the diagnostic capabilities of radiologists.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143965185","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

Enhancing Ultrasound Scanning Skills in a Leader-Follower Robotic System through Expert Hand Impedance Regulation. 通过专家手阻抗调节提高领导-跟随机器人系统的超声扫描技能。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-04-09 DOI: 10.1109/JBHI.2025.3559495

Baoshan Niu, Dapeng Yang, Le Zhang, Yiming Ji, Li Jiang, Hong Liu

{"title":"Enhancing Ultrasound Scanning Skills in a Leader-Follower Robotic System through Expert Hand Impedance Regulation.","authors":"Baoshan Niu, Dapeng Yang, Le Zhang, Yiming Ji, Li Jiang, Hong Liu","doi":"10.1109/JBHI.2025.3559495","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3559495","url":null,"abstract":"Traditional breast cancer surgeries require collaboration between ultrasound (US) doctors and surgeons, making the procedure complex and treating physicians prone to fatigue. In leader-follower robotic surgery, a surgeon controls an US robotic arm and an instrument robotic arm with their left and right hands, enabling independent surgical performance. However, the lack of US scanning skills among surgeons, as well as the physical separation in leader-follower operations, can negatively impact both the scanning and surgical outcomes. This paper proposes a robot-assisted scheme based on dynamic arm impedance compensation (IC) that references expert arm stiffness to compensate for novice arm stiffness. The impedance compensator adjusts the compensation strategy according to the scanning area and scanning stage. The impedance force generator estimates the scanning direction via Kalman filtering and applies stiffness and damping forces in the vertical direction to suppress tremors and other involuntary movements. The experimental results revealed that during the coarse and fine scanning phases, the probe position variance decreased by 57.9% and 73.6%, the contact force variance decreased by 55.2% and 42.5%, and the US image confidence increased by 22.0% and 23.8%, respectively. Compared with traditional filtering compensation (FC) schemes, this approach reduces the average position variance and contact force variance by 32.0% and 25.3%, respectively, and increases confidence by 7.3%. In a no-compensation test, the IC training group outperformed the FC group. This scheme can assist leader-follower US scanning and rapidly improve surgical skills.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143963279","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

Single-slice Semi-supervised 3D Medical Image Segmentation via Correlation Information Enhancement and Hybrid Pseudo Mask Generation. 基于相关信息增强和混合伪掩码生成的单片半监督三维医学图像分割。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-04-09 DOI: 10.1109/JBHI.2025.3559091

Quan Zhou, Mingwei Wen, Mingyue Ding, Yixin Su, Zhiwei Wang

{"title":"Single-slice Semi-supervised 3D Medical Image Segmentation via Correlation Information Enhancement and Hybrid Pseudo Mask Generation.","authors":"Quan Zhou, Mingwei Wen, Mingyue Ding, Yixin Su, Zhiwei Wang","doi":"10.1109/JBHI.2025.3559091","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3559091","url":null,"abstract":"Three-dimensional (3D) medical image segmentation typically demands extensive labeled training samples, which is prohibitively time-consuming and requires significant expertise. Although this demand can be mitigated by special learning paradigms such as semi-supervised learning, the cost is still high due to the reader-unfriendly 3D data structure. In this paper, we seek a solution of robust 3D segmentation using extremely simplified annotation that delineates only a single slice per each volume for only a subset of the 3D samples. To this end, we propose two innovative modules: a correlation-enhanced 3D segmentation model (CE-Seg) and a hybrid 3D pseudo mask generator (Hy-Gen). CE-Seg aims to comprehensively understand the 3D targets under super-sparse single-slice supervision by maximizing its ability to mine correlations across slices, spaces and scales. Specifically, CE-Seg mimics the radiologist's interpretation by 'seeing' a dynamically scrolling 3D image to enrich the slice-correlated context. It also introduces a drop-then-restoration self-played task to enhance the spatial correlations of features, and uses a bidirectional cascaded attention to interactively fuse features across different scales. To train CS-Seg, Hy-Gen combines learning-based and learning-free strategies to generate reliable pseudo 3D masks as supervisions. Concretely, Hy-Gen first employs a level-set evolution to 'spread' the single annotation to its neighboring slices as initialization. It then builds a teacher-student framework to progressively refine the initialized 3D mask by dynamically merging the predictions of the CS-Seg's teacher-copy. Extensive experiments on three public and one in-house datasets indicate that our method exceeds eight state-of-the-art semi-supervised methods by at least 3% in dice, and is even on par with the full-supervised counterpart.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143984355","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 Compressed-based Privacy-preserving Large Language Model for Sensitive Healthcare. 基于自适应压缩的敏感医疗保健隐私保护大语言模型。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-04-08 DOI: 10.1109/JBHI.2025.3558935

Xinrong Gong, Jiaran Gao, Song Sun, Zhijie Zhong, Yifan Shi, Huanqiang Zeng, Kaixiang Yang

{"title":"Adaptive Compressed-based Privacy-preserving Large Language Model for Sensitive Healthcare.","authors":"Xinrong Gong, Jiaran Gao, Song Sun, Zhijie Zhong, Yifan Shi, Huanqiang Zeng, Kaixiang Yang","doi":"10.1109/JBHI.2025.3558935","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3558935","url":null,"abstract":"The emergence of large language models (LLMs) has been a key enabler of technological innovation in healthcare. People can conveniently obtain a more accurate medical consultation service by utilizing LLMs' powerful knowledge inference capability. However, existing LLMs require users to upload explicit requests during remote healthcare consultations, which involves the risk of exposing personal privacy. Furthermore, the reliability of the response content generated by LLMs is not guaranteed. To tackle the above challenges, this paper proposes a novel privacy-preserving LLM for user-activated health, called Adaptive Compressed-based Privacy-preserving LLM (ACP2LLM). Specifically, an adaptive token compression method based on information entropy is carefully designed to ensure that ACP2LLM can preserve user-sensitive information when invoking the medical consultation of LLMs deployed on the cloud platform. Moreover, a multi-doctor one-chief physician mechanism is proposed to rationally split and collaboratively infer the patients' requests to achieve the privacy-utility trade-off. Notably, the proposed ACP2LLM also provides highly competitive performance in various token compression rates. Extensive experiments on multiple Medical Question and Answers datasets demonstrate that the proposed ACP2LLM has strong privacy protection capabilities and high answer precision, outperforming current state-of-the-art LLM methods.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143811335","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

BSAN: A Self-Adapted Motor Imagery Decoding Framework Based on Contextual Information. 基于上下文信息的自适应运动意象解码框架。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-04-08 DOI: 10.1109/JBHI.2025.3557499

Zikai Wang, Ang Li, Zhenyu Wang, Ting Zhou, Tianheng Xu, Honglin Hu

{"title":"BSAN: A Self-Adapted Motor Imagery Decoding Framework Based on Contextual Information.","authors":"Zikai Wang, Ang Li, Zhenyu Wang, Ting Zhou, Tianheng Xu, Honglin Hu","doi":"10.1109/JBHI.2025.3557499","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3557499","url":null,"abstract":"In motor imagery (MI) decoding, it still remains challenging to excavate enough contextual information of MI in different brain regions and to bridge the cross-session variance in feature distributions. In light of these issues, our study presents an innovative Bi-Stream Adaptation Network (BSAN) to bolster network efficacy, aiming to improve MI-based brain-computer interface (BCI) robustness across sessions. Our framework consists of the Bi-attention module, feature extractor, classifier, and Bi-discriminator. Precisely, we devise the Bi-attention module to reveal granular context information of MI with performing multi-scale convolutions asymptotically. Then, after features extraction, Bi-discriminator is involved to align the features from different MI sessions such that a uniform and accurate representation of neural patterns is achieved. By such a workflow, the proposed BSAN allows for the effective fusion of context coherence and session-invariance within the network architecture, therefore diminishing the reliance of redundant MI trials for MI-BCI re-calibration. To empirically substantiate BSAN, comprehensive experiments are conducted based on two public MI datasets. With average accuracies of 78.97% and 83.79% on two public datasets, and an inference time of 2.99 ms on CPU-only devices, it is believed that our approach has the potential to accelerate the practical deployment of MI-BCI.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143811337","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

Illuminating The Path To Enhanced Resilience Of Machine Learning Models Against The Shadows Of Missing Labels. 照亮增强机器学习模型抵御缺失标签阴影的弹性之路。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-04-08 DOI: 10.1109/JBHI.2025.3558846

Simankov Nikolay, Tahzima Rachid, Massart Sebastien, Soyeurt Helene

{"title":"Illuminating The Path To Enhanced Resilience Of Machine Learning Models Against The Shadows Of Missing Labels.","authors":"Simankov Nikolay, Tahzima Rachid, Massart Sebastien, Soyeurt Helene","doi":"10.1109/JBHI.2025.3558846","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3558846","url":null,"abstract":"The sensitivity of state-of-the-art supervised classification models is compromised by contamination-prone biomedical datasets, which are vulnerable to the presence of missing or erroneous labels (i.e., inliers). Starting from codon frequencies, electrocardiogram signals, biomarkers, morphological features, and patient questionnaires, we attempted to cover a wide range of typical biomedical databases exposed to the risk of missing data labeled as negative values (inlier contamination). In some very niche fields, such as image recognition, missing labels have received a lot of attention, but in biomedical and clinical research, where outliers are almost systematically filtered, inliers have remained orphans. Our study introduced a pragmatic and innovative automated methodology that consists of upcycling one-class semi-supervised anomaly detection (OCSSAD) models for filtering potential inliers in training datasets. Five OCSSAD and two ensemble methods were benchmarked on 6 databases with 10 different contamination levels and 10 random samples, achieving an average Matthews correlation coefficient (MCC) of 78$pm$17% in validation, whereas 22 supervised classifiers achieved an average MCC score of 81$pm$9% trained with the complete and uncontaminated trainset.Therefore, by filtering the training set with an isolation forest, the average resilience to inliers of 22 tested Machine Learning models increased from 69$pm$11% to 95$pm$1%, including neural networks and gradient-boosting methods. Taken together, our study showcased the efficacy of our versatile approach in enhancing the resilience of Machine Learning models and highlighted the importance of accurately addressing the inliers challenge in the domains of medical and Life Sciences.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143811341","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

Enhancing Herbal Medicine-Drug Interaction Prediction Using Large Language Models. 利用大语言模型增强草药药物相互作用预测。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-04-07 DOI: 10.1109/JBHI.2025.3558667

Sisi Yuan, Zhecheng Zhou, Xinyuan Jin, Linlin Zhuo, Keqin Li

{"title":"Enhancing Herbal Medicine-Drug Interaction Prediction Using Large Language Models.","authors":"Sisi Yuan, Zhecheng Zhou, Xinyuan Jin, Linlin Zhuo, Keqin Li","doi":"10.1109/JBHI.2025.3558667","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3558667","url":null,"abstract":"Investigating potential interactions between drugs and herbal medicines helps optimize combined treatment strategies and supports personalized and precision medicine. Deep learning-based methods have been successful in predicting drug-related interactions. However, these methods face challenges such as low data quality and uneven distribution. Large language models (LLMs) effectively address these challenges through their extensive knowledge bases. Motivated by this, we integrate LLMs, one-hot encoding, and variational graph autoencoders (VGAEs) to propose a herbal medicine-drug interaction (HDI) prediction model. First, LLMs are employed to extract features from drug SMILES, generating high-quality molecular representations. Second, one-hot encoding is applied to herbal medicines with multiple natural products to construct feature vectors and improve model interpretability. Finally, VGAEs are utilized to reconstruct herbal medicine-drug graphs and predict unknown HDIs. Additionally, we differentiate between herbal medicine-drug similarity and the degree of individual drug or herbal medicine nodes to mitigate the dominance of high-degree nodes in VGAE message flow. Multiple experiments were conducted to validate the significance of the proposed model and its key components. This method shows great potential for applications in traditional Chinese medicine formulation optimization, new drug development, and precision medicine. Our code and data are accessible at: https://github.com/sisyyuan/HDI.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143802871","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

Progressive Mining and Dynamic Distillation of Hierarchical Prototypes for Disease Classification and Localisation. 用于疾病分类和定位的分层原型的渐进挖掘和动态提炼。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-04-07 DOI: 10.1109/JBHI.2025.3558508

Chong Wang, Fengbei Liu, Yuanhong Chen, Chun Fung Kwok, Michael Elliott, Carlos Pena-Solorzano, Davis James McCarthy, Helen Frazer, Gustavo Carneiro

{"title":"Progressive Mining and Dynamic Distillation of Hierarchical Prototypes for Disease Classification and Localisation.","authors":"Chong Wang, Fengbei Liu, Yuanhong Chen, Chun Fung Kwok, Michael Elliott, Carlos Pena-Solorzano, Davis James McCarthy, Helen Frazer, Gustavo Carneiro","doi":"10.1109/JBHI.2025.3558508","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3558508","url":null,"abstract":"Constructing effective representation of lesions is essential for disease classification and localization in medical image analysis. Prototype-based models address this by leveraging visual prototypes to capture representative lesion patterns, yet effectively handling the complexity of diverse lesion characteristics remains a critical challenge, as they typically rely on single-level, fixedsize prototypes and suffer from prototype redundancy. In this paper, we present HierProtoPNet, a new prototypebased framework designed to handle the complexity of lesions in medical images. HierProtoPNet leverages hierarchical visual prototypes across different semantic feature granularities to effectively capture diverse lesion patterns. To prevent redundancy and increase utility of the prototypes, we devise a novel prototype mining paradigm to progressively discover semantically distinct prototypes, offering multi-level complementary analysis of lesions. Also, we introduce a dynamic knowledge distillation strategy that allows transferring essential classification information across hierarchical levels, thereby improving generalisation performance. Comprehensive experiments show that HierProtoPNet achieves state-of-the-art classification performances in three benchmarks: binary breast cancer screening, multi-class retinal disease diagnosis, and multilabel chest X-ray classification. Quantitative assessments also illustrate HierProtoPNet's significant advantages in weakly-supervised disease localisation and segmentation.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143803050","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

Graph-Based Prediction of miRNA-Drug Associations with Multisource Information and Metapath Enhancement Matrices. 基于多源信息和元路径增强矩阵的mirna -药物关联图预测。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-04-04 DOI: 10.1109/JBHI.2025.3558303

Ming-Yang Wu, Peng-Wei Hu, Zhu-Hong You, Jun Zhang, Lun Hu, Xin Luo

{"title":"Graph-Based Prediction of miRNA-Drug Associations with Multisource Information and Metapath Enhancement Matrices.","authors":"Ming-Yang Wu, Peng-Wei Hu, Zhu-Hong You, Jun Zhang, Lun Hu, Xin Luo","doi":"10.1109/JBHI.2025.3558303","DOIUrl":"10.1109/JBHI.2025.3558303","url":null,"abstract":"Recent studies have demonstrated that miRNA expression dysregulation is closely related to the occurrence of various diseases; thus, miRNA-based drug development strategies have received increasing research interest. Most existing computational methods focus on the attribute information of individual nodes and are limited to the direct associations between nodes, thereby ignoring the complex associations inherent in the network. This limitation may lead to the loss of key potential information, which impacts the prediction accuracy. To address these issues, we propose a multisource information fusion and metapath enhancement matrix based graph autoencoder (MSMP-GAE) to predict the potential associations between miRNAs and drugs. The proposed MSMP-GAE model comprises a metapath instance extraction module, a metapath feature-enhanced encoder module, a weighted feature fusion module, and a graph autoencoder. First, we construct an miRNA-drug heterogeneous network using experimentally validated miRNA-drug interactions and integrate various miRNA and drug features into an initial feature matrix to comprehensively represent their intrinsic property information. Then, we extract metapath instances from the interaction network, generate multiple metapath enhancement matrices, and fuse them with the initial feature matrix to generate high-quality node feature embeddings. Finally, we employ the graph autoencoder for fivefold cross-validation on a public dataset and test it on an independent test set. Experimental results demonstrate that the proposed MSMP-GAE model obtained an area under the curve (AUC) and AUPR values of 98.61% and 98.23%, respectively, which is considerably better than the several state-of-the-art methods. This highlights the importance of the higher-order complex associations between nodes in the miRNA-drug association (MDA) prediction task and provides a new method and approach to advance MDA prediction.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143784452","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