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

Fetal Cerebellum Landmark Detection Based on 3D MRI: Method and Benchmark. 基于3D MRI的胎儿小脑地标检测：方法和基准。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-04-10 DOI: 10.1109/JBHI.2025.3559702

Haifan Gong, Huixian Liu, Yitao Wang, Xiaoling Liu, Xiang Wan, Qiao Shi, Haofeng Li

{"title":"Fetal Cerebellum Landmark Detection Based on 3D MRI: Method and Benchmark.","authors":"Haifan Gong, Huixian Liu, Yitao Wang, Xiaoling Liu, Xiang Wan, Qiao Shi, Haofeng Li","doi":"10.1109/JBHI.2025.3559702","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3559702","url":null,"abstract":"Fetal cerebellum landmark detection is crucial for assessing fetal brain development. Although deep learning has become the standard for automatic landmark detection, most previous methods have focused on using 2D ultrasound or thick Magnetic Resonance Imaging (MRI) . To improve accuracy, landmarks should be located on thin 3D MRIs. However, abnormal development, high noise, and fuzzy boundaries in 3D fetal brain images make traditional methods less effective for cerebellum landmark detection. To address this, we introduce the Anatomical Pseudo-label Guided Attention (APGA) network alongside a 3D MRI-based benchmark for fetal cerebellum landmark detection. During training, we use a shared encoder to extract image features and two decoders for landmark regression and anatomical pseudo-label segmentation. We design a Feature Decoupling Transformer (FDT) and embed it into the encoder to better calibrate the features for the two tasks. We only need the encoder, the FDT, and the landmark decoder during the inference phase. Extensive experiments on our proposed benchmark and out-of-domain test set have shown the effectiveness of our method. Our simulations also demonstrated that 3D biometrics are better than 2D biometrics. Code is available at https://github.com/lhaof/LFC.","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":"143984640","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

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

Topological GCN Guided Improved Conformer for Detection of Hip Landmarks from Ultrasound Images. 拓扑GCN引导改进的超声图像髋关节地标检测的一致性。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-04-09 DOI: 10.1109/JBHI.2025.3559383

Tianxiang Huang, Jing Shi, Ge Jin, Juncheng Li, Jun Wang, Qian Wang, Jun Du, Jun Shi

{"title":"Topological GCN Guided Improved Conformer for Detection of Hip Landmarks from Ultrasound Images.","authors":"Tianxiang Huang, Jing Shi, Ge Jin, Juncheng Li, Jun Wang, Qian Wang, Jun Du, Jun Shi","doi":"10.1109/JBHI.2025.3559383","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3559383","url":null,"abstract":"The B-mode ultrasound based computeraided diagnosis (CAD) has shown its effectiveness for diagnosis of Developmental Dysplasia of the Hip (DDH) in infants within 6 months. Hip landmark detection is a feasible way for the CAD of DDH according to the Graf's method. However, existing landmark detection algorithms mainly focus on designing special models to capture the features from hip ultrasound images, but generally ignore the important spatial relations among different landmarks. To this end, a novel weakly supervised learning-based algorithm, the Topological Graph Convolutional Network (TGCN) guided Improved Conformer (TGCN-ICF), is proposed for detecting landmarks from hip ultrasound images. The TGCN-ICF includes two subnetworks: an Improved Conformer (ICF) subnetwork to generate heatmaps and constraint vectors from ultrasound images, and a TGCN subnetwork to additionally explore topological relations among hip landmarks with the guidance of class labels for further refining and improving the detection accuracy. Moreover, a new Mutual Modulation Fusion (MMF) module is developed to fully exchange and fuse the extracted feature information from the convolutional neural network (CNN) and Transformer branches in ICF. Meanwhile, a novel Mutual Supervision Constraint (MSC) strategy is designed to provide a constraint for detection of each hip landmark. The experimental results on two realworld DDH datasets demonstrate that the TGCN-ICF outperforms all the compared algorithms, suggesting its potential applications. The source code is publicly available on https://github.com/Tianxiang-Huang/TGCN-ICF.","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":"144005587","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

Postoperative Recovery Assessment for Parkinson's Patients via Light-weighted Topological Pose Estimation. 基于轻量级拓扑位姿估计的帕金森病患者术后恢复评估。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-04-09 DOI: 10.1109/JBHI.2025.3559493

Zeping Ma, Zhiyao Qin, Botao Jiang, Guosong Zhu, Zhen Qin, Ji Geng, Mohammed J F Alenazi, Saru Kumari

{"title":"Postoperative Recovery Assessment for Parkinson's Patients via Light-weighted Topological Pose Estimation.","authors":"Zeping Ma, Zhiyao Qin, Botao Jiang, Guosong Zhu, Zhen Qin, Ji Geng, Mohammed J F Alenazi, Saru Kumari","doi":"10.1109/JBHI.2025.3559493","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3559493","url":null,"abstract":"The UPDRS III scale plays a critical role in diagnosing the progression of Parkinson's disease. Current methods often involve doctors guiding patients through specific actions on the scale, recording their performance, and assigning scores. However, this approach has several drawbacks, including the lengthy time required for doctorpatient communication, the high costs of patients traveling to hospitals for follow-up visits, and the reliance on subjective judgments from doctors, which lack standardized criteria. With advancements in artificial intelligence, many traditional processes have been partially automated. To help patients reduce diagnosis time, lower medical costs, and provide more accurate and objective evaluation results, this paper proposes a Transformer-based pose estimation model for assessing UPDRS III scale actions. By integrating skeleton-based evaluations from the network with a series of post-processing operations, the model enables patients to perform self-assessments of their post-treatment recovery at home, saving doctors significant time. This work introduces a cascaded graph self-attention module, SGAM (Spatial-Graphical Attention Module), to enhance the network's understanding of human topology. Additionally, it proposes a lightweight convolutional block, Chi-block, which employs a novel approach leveraging the attribute invariance of filters to interpret model performance and guide compression. This approach reduces computational costs and model parameters while preserving accuracy. The proposed method demonstrates robust performance on human pose estimation (HPE) datasets and showcases impressive lightweight performance on benchmark datasets such as ImageNet-1K and CIFAR-10. These results demonstrate the potential of artificial intelligence in enabling automated remote diagnosis and treatment for Parkinson's patients.","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":"143990808","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