IEEE Journal of Biomedical and Health Informatics最新文献_第2页

Neighbor-Guided Unbiased Framework for Generalized Category Discovery in Medical Image Classification. 邻域引导无偏框架在医学图像分类中的广义类别发现。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-04-02 DOI: 10.1109/JBHI.2025.3556984

Wei Feng, Sijin Zhou, Yiwen Jiang, Feilong Tang, Zongyuan Ge

{"title":"Neighbor-Guided Unbiased Framework for Generalized Category Discovery in Medical Image Classification.","authors":"Wei Feng, Sijin Zhou, Yiwen Jiang, Feilong Tang, Zongyuan Ge","doi":"10.1109/JBHI.2025.3556984","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3556984","url":null,"abstract":"Generalized category discovery (GCD) utilizes seen category knowledge to automatically discover new semantic categories that are not defined in the training phase. Nevertheless, there has been no research conducted on identifying new classes using medical images and disease categories, which is essential for understanding and diagnosing specific diseases. Moreover, existing methods still produce predictions that are biased towards seen categories since the model is mainly supervised by labeled seen categories, which in turn leads to sub-optimal clustering performance. In this paper, we propose a new neighbor-guided unbiased framework (NGUF) that leverages neighbor information to mitigate prediction bias to address the GCD problem in medical tasks. Specifically, we devise a neighbor-guided cross- pseudo-clustering strategy, which exploits the knowledge of the nearest-neighbor samples to adjust the model predictions thereby generating unbiased pseudo-clustering supervision. Then, based on the unbiased pseudo-clustering supervision, we use a view-invariant learning strategy to assign labels to all samples. In addition, we propose an adaptive weight learning strategy that dynamically determines the degree of adjustment of the predictions of different samples based on the distance density values. Finally, we further propose a cross-batch knowledge distillation module to utilize information from successive iterations to encourage training consistency. Extensive experiments on four medical image datasets show that NGUF is effective in mitigating the model's prediction bias and has superior performance to other state-of-the-art GCD algorithms. Our code will be released soon.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143772139","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

Improvement of Non-invasive Glucose Estimation Accuracy through Multi-wavelength PPG. 多波长PPG技术提高无创血糖检测精度。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-04-01 DOI: 10.1109/JBHI.2025.3556666

Taixiang Li, Quangui Wang, Linghao Lei, Ying An, Lin Guo, Lina Ren, Xianlai Chen

{"title":"Improvement of Non-invasive Glucose Estimation Accuracy through Multi-wavelength PPG.","authors":"Taixiang Li, Quangui Wang, Linghao Lei, Ying An, Lin Guo, Lina Ren, Xianlai Chen","doi":"10.1109/JBHI.2025.3556666","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3556666","url":null,"abstract":"Effective diabetes management requires regular and accurate blood glucose monitoring; however, traditional invasive methods often cause discomfort and inconvenience. Non-invasive techniques such as photoplethysmography (PPG) have been explored, though single-wavelength PPG systems are limited by the overlapping absorption characteristics between glucose and other biological components, such as water and fat. In this study, a novel multi-wavelength PPG system integrated with temperature and humidity sensors is introduced, coupled with a neural network framework featuring attention mechanisms to enhance glucose prediction. The system employs six optical sensors covering wavelengths from the visible to near-infrared (NIR) spectrum, enabling deeper tissue penetration and enhanced glucose specificity by targeting distinct absorption peaks-especially those above 1000 nm. The system was validated using a robust dataset of 26,063 measurements from 254 participants. The experimental results demonstrate significant improvements, with the model achieving 86.49% compliance with the ISO 15197: 2013 standards and 91.80% of measurements falling within Zone A of the Parkes error grid. The introduction of multiple wavelengths clearly improves performance over single-wavelength systems, and wavelengths above 1000 nm were shown to have a higher contribution in glucose prediction. In addition, the incorporation of temperature and humidity data also enhanced performance by accounting for environmental and physiological factors, and that demographic and meal-related factors significantly impact prediction accuracy, thereby underscoring the potential of this system as a reliable, non-invasive, and personalized glucose monitoring tool.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143763837","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

Variability of Spatiotemporal-Rhythmic Network During Inhibitory Control in Repetitive Subconcussion. 重复次震荡抑制控制过程中时空节奏网络的变异性。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-04-01 DOI: 10.1109/JBHI.2025.3556595

Xiang Li, Zhenghao Fu, Hui Zhou, Yin Xiang, Yaqian Li, Yida He, Jiaqi Zhang, Huanhuan Li, Lijie Gao, Junfeng Gao, Jian Song

{"title":"Variability of Spatiotemporal-Rhythmic Network During Inhibitory Control in Repetitive Subconcussion.","authors":"Xiang Li, Zhenghao Fu, Hui Zhou, Yin Xiang, Yaqian Li, Yida He, Jiaqi Zhang, Huanhuan Li, Lijie Gao, Junfeng Gao, Jian Song","doi":"10.1109/JBHI.2025.3556595","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3556595","url":null,"abstract":"The inhibitory control dysfunction associated with the cognitive symptoms resulting from repetitive subconcussion (SC) is frequent. Implementing inhibitory control is temporally resolved and is likely related to the dynamic interactions in functional brain networks. However, investigations of the dynamic activity of these brain networks using electroencephalography (EEG) are often limited to specific frequency bands without entirely utilizing the spatiotemporal rhythmic information. Therefore, we proposed an innovative framework for constructing a large-scale spatiotemporal-rhythmic network (STRN) using the dynamic cross-frequency phase synchronization to track cognitive deficits induced by repetitive subconcussion during the inhibitory control. Seventeen parachuters with repeated subconcussive exposure and 17 healthy controls (HC) were subjected to a Stroop task while recording the continuous scalp EEG data. Our results indicated an STRN-specific activation pattern that achieved a high classification performance with an average accuracy of 90.98%, which may serve as a biomarker for identifying the repetitive subconcussion inhibitory control dysfunction. In this STRN state, the SC exhibited mostly lower network rhythmic information interactions than the HC. These findings suggested that the STRN presented in this study could be an effective analytical method for understanding the cognitive dysfunction observed in the repetitive subconcussion and other related conditions. The example code for calculating cross-frequency phase synchronization used to construct the STRN, as well as the code for computing the dynamic measures of STRN states (including frequency of occurrence, mean dwell time, and number of state transitions), is publicly available on GitHub at (https://github.com/Xiang-Li-Scholar/Variability-of-Spatiotemporal-Rhythmic-Network-during-Inhibitory-Control-in-Repetitive-Subconcussion).","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143763904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Interpretable Multi-Task Conditional Neural Networks Reveal Cancer Cell Adhesion Characteristics From Phonon Microscopy Images. 可解释的多任务条件神经网络从声子显微镜图像揭示癌细胞粘附特性。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-04-01 DOI: 10.1109/JBHI.2025.3556599

Yijie Zheng, Rafael Fuentes-Dominguez, Md Raihan Goni, Matt Clark, George S D Gordon, Fernando Perez-Cota

{"title":"Interpretable Multi-Task Conditional Neural Networks Reveal Cancer Cell Adhesion Characteristics From Phonon Microscopy Images.","authors":"Yijie Zheng, Rafael Fuentes-Dominguez, Md Raihan Goni, Matt Clark, George S D Gordon, Fernando Perez-Cota","doi":"10.1109/JBHI.2025.3556599","DOIUrl":"10.1109/JBHI.2025.3556599","url":null,"abstract":"Advances in artificial intelligence (AI) show significant promise in multiscale modeling and biomedical informatics, particularly in the analysis of phonon microscopy (high-frequency ultrasound) data for cancer detection. This study addresses critical issues in data engineering for time-resolved phonon microscopy of biomedical samples by tackling the 'batch effect,' which arises from unavoidable technical variations between experiments, creating confounding variables that AI models may inadvertently learn. We present a multi-task conditional neural network framework that simultaneously achieves inter-batch calibration by removing confounding variables and accurate cell classification from time-resolved phonon-derived signals. We validate our approach by training and validating on different experimental batches, achieving a balanced precision of 89.22% and an average cross-validated precision of 89.07% for classifying background, healthy and cancerous regions. Furthermore, our model enables reconstruction of denoised images, which enable the physical interpretation of salient features indicative of disease states, such as sound velocity, sound attenuation, and cell adhesion to substrates. This work demonstrates the potential of AI methodologies in improving health outcomes and advancing cancer-informatics platforms.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143763897","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

BFGTP: A BERT-Guided Two-Stage Molecular Representation Learning Framework for Toxicity Prediction. BFGTP: bert引导的毒性预测的两阶段分子表示学习框架。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-04-01 DOI: 10.1109/JBHI.2025.3556766

Kaimiao Hu, Yuan He, Jianguo Wei, Changming Sun, Jie Geng, Leyi Wei, Ran Su

{"title":"BFGTP: A BERT-Guided Two-Stage Molecular Representation Learning Framework for Toxicity Prediction.","authors":"Kaimiao Hu, Yuan He, Jianguo Wei, Changming Sun, Jie Geng, Leyi Wei, Ran Su","doi":"10.1109/JBHI.2025.3556766","DOIUrl":"10.1109/JBHI.2025.3556766","url":null,"abstract":"Accurate prediction of molecular toxicity is vital for drug development. Most mainstream methods rely on fingerprints or graph-based feature extraction, the emergence of large language models (LLMs) offers new prospects for molecular representation learning in toxicity prediction. Although several studies attempt to leverage LLMs to integrate molecular sequence data for pretraining molecular representations, certain limitations remain. Current LLM-based approaches usually utilize solely on class embedding features, overlooking the rich information in sequence embedding. Moreover, integrating pre-trained molecular representations with multi-modal molecular data may further enhance performance in toxicity prediction. To address these challenges, we propose BFGTP, a BERT-guided two-stage molecular representation learning framework for toxicity prediction. Firstly, we design independent encoders for molecular descriptions of three modalities, where the fingerprint encoder with dual level attention mechanisms effectively integrates multi-category fingerprints. Then, the two-stage guide strategy is introduced to fully utilize the prior knowledge of LLMs, employing contrastive learning to align and fuse the tri-modal representations and knowledge distillation to align predicted value distributions. BFGTP ultimately combines fingerprint and graph representations to predict molecular toxicity. Experiments on seven toxicity datasets show that BFGTP outperforms baselines, achieving the highest AUC on five datasets and the best average performance across five evaluation metrics. Ablation studies, t-SNE visualization and case study confirm the effectiveness of BFGTP's components and its ability to capture meaningful molecular representations.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143763723","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

Leveraging Channel Coherence in Long-Term iEEG Data for Seizure Prediction. 利用长期脑电图数据中的通道相干性预测癫痫发作。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-04-01 DOI: 10.1109/JBHI.2025.3556775

Sha Lu, Lin Liu, Jiuyong Li, Jordan Chambers, Mark J Cook, David B Grayden

{"title":"Leveraging Channel Coherence in Long-Term iEEG Data for Seizure Prediction.","authors":"Sha Lu, Lin Liu, Jiuyong Li, Jordan Chambers, Mark J Cook, David B Grayden","doi":"10.1109/JBHI.2025.3556775","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3556775","url":null,"abstract":"Epilepsy affects millions worldwide, posing significant challenges due to the erratic and unexpected nature of seizures. Despite advancements, existing seizure prediction techniques remain limited in their ability to forecast seizures with high accuracy, impacting the quality of life for those with epilepsy. This research introduces the Coherence-based Seizure Prediction (CoSP) method, which integrates coherence analysis with deep learning to enhance seizure prediction efficacy. In CoSP, electroencephalography (EEG) recordings are divided into 10-second segments to extract channel pairwise coherence. This coherence data is then used to train a four-layer convolutional neural network to predict the probability of being in a preictal state. The predicted probabilities are then processed to issue seizure warnings. CoSP was evaluated in a pseudo-prospective setting using long-term iEEG data from ten patients in the NeuroVista seizure advisory system. CoSP demonstrated promising predictive performance across a range of preictal intervals (4 to 180 minutes). CoSP achieved a median Seizure Sensitivity (SS) of 0.79, a median false alarm rate of 0.15 per hour, and a median Time in Warning (TiW) of 27%, highlighting its potential for accurate and reliable seizure prediction. Statistical analysis confirmed that CoSP significantly outperformed chance (p = 0.001) and other baseline methods (p <0.05) under similar evaluation configurations.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143763900","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

BSMatch: Boundary Segmentation and Matching for Lipid Droplet Quantification in Diagnosis of Non-Alcoholic Fatty Liver Disease. BSMatch：非酒精性脂肪性肝病诊断中脂滴定量的边界分割与匹配。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-04-01 DOI: 10.1109/JBHI.2025.3556709

Tsung-Hsuan Wu, Hung-Chih Chiu, Jyun-Sin Wu, Wei-Jong Yang, Kuo-Sheng Cheng, Che-Wei Hsu, Shu-Hsien Wang, Joshua Tay Uyboco, Hung-Wen Tsai, Pau-Choo Chung

{"title":"BSMatch: Boundary Segmentation and Matching for Lipid Droplet Quantification in Diagnosis of Non-Alcoholic Fatty Liver Disease.","authors":"Tsung-Hsuan Wu, Hung-Chih Chiu, Jyun-Sin Wu, Wei-Jong Yang, Kuo-Sheng Cheng, Che-Wei Hsu, Shu-Hsien Wang, Joshua Tay Uyboco, Hung-Wen Tsai, Pau-Choo Chung","doi":"10.1109/JBHI.2025.3556709","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3556709","url":null,"abstract":"Hepatic steatosis is one of the most obvious indicators of nonalcoholic fatty liver disease. However, the presence of many regions with a similar color and shape as lipid droplets in the histopathological image complicates the task of detecting genuine lipid droplets using automated methods. Accordingly, the present study proposes a boundary segmentation and matching (BSMatch) algorithm for the segmentation of lipid droplets based on their unique boundary characteristics. A two-branch RnB-Unet model is trained to segment the regions and boundaries of the droplets, respectively, in accordance with a boundary matching (BM) loss which enforces the consistency between them. A boundary matching score (BMS) measure is then used to improve the precision of the instance segmentation evaluation process by discarding segmented regions which are not well-matched with their predicted boundaries. The experimental results obtained using a H&E-stained liver slide dataset show that BSMatch outperforms existing methods in terms of both the IoU and the F1-score. The BSMatch results are used to predict the fat percentage in hepatocytes (FPH) in liver whole slide images. The predicted FPH values are well correlated with the steatosis grades assigned by experienced pathologists. Thus, BSMatch appears to have significant promise for NAFLD diagnosis in clinical contexts.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143763726","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

Integrating Clinical Insights via Hierarchical Inference to Predict Conditions in Bilaterally Symmetric Organs. 通过层次推理整合临床见解来预测双侧对称器官的状况。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-04-01 DOI: 10.1109/JBHI.2025.3556717

Li Rong Wang, Si Yin Charlene Chia, Vivien Cherng-Hui Yip, Kelvin Zhenghao Li, Xiuyi Fan

{"title":"Integrating Clinical Insights via Hierarchical Inference to Predict Conditions in Bilaterally Symmetric Organs.","authors":"Li Rong Wang, Si Yin Charlene Chia, Vivien Cherng-Hui Yip, Kelvin Zhenghao Li, Xiuyi Fan","doi":"10.1109/JBHI.2025.3556717","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3556717","url":null,"abstract":"Substantial progress has been made in developing deep-learning models for clinical diagnosis. While excelling in diagnostics, the broader clinical decision-making process also involves establishing optimal follow-up intervals (TCU), crucial for prognosis and timely treatment. To fully support clinical practice, it is imperative that deep learning models contribute to both initial diagnosis and TCU prediction. However, relying on separate monolithic models is computationally demanding and lacks interpretability, hindering clinician trust. Our proposed bilateral model, emphasizing ophthalmological cases, offers both initial diagnoses and follow-up predictions, enhancing interpretability and trust in clinical applications as clinicians are more likely to trust recommendations, knowing the diagnosis used is correct. Inspired by clinical practice, the model integrates hierarchical inference and self-supervised learning techniques to enhance predictive accuracy and interpretability. Consisting of a sparse autoencoder, diagnosis classifier, and TCU classifier, the model leverages insights from clinicians and observations of ophthalmological datasets to capture salient features and facilitate robust learning. By employing shared weights for encoding and diagnosing each organ, the model optimizes efficiency and doubles the effective dataset size. Experimental results on an ophthalmological dataset demonstrate superior performance compared to baseline models, with the hierarchical inference structure providing valuable insights into the model's decision-making process. The bilateral model not only enhances predictive modeling for conditions affecting bilaterally symmetrical organs but also empowers clinicians with interpretable outputs crucial for informed clinical decision-making, thereby advancing clinical practice and improving patient care.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143763895","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

FIND: A Framework for Iterative to Non-Iterative Distillation for Lightweight Deformable Registration. FIND：用于轻量可变形配准的迭代到非迭代蒸馏的框架。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-04-01 DOI: 10.1109/JBHI.2025.3556676

Yongtai Zhuo, Mingkang Liu, Jie Liu, Zhikai Yang, Rui Liu, Peng Xue, Lixu Gu

{"title":"FIND: A Framework for Iterative to Non-Iterative Distillation for Lightweight Deformable Registration.","authors":"Yongtai Zhuo, Mingkang Liu, Jie Liu, Zhikai Yang, Rui Liu, Peng Xue, Lixu Gu","doi":"10.1109/JBHI.2025.3556676","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3556676","url":null,"abstract":"Deformable image registration is crucial for medical image analysis, yet the complexity of deep learning networks often limits their deployment on resource-limited devices. Current distillation methods in registration tasks fail to effectively transfer complex deformation handling capabilities to non-iterative lightweight networks, leading to insignificant performance improvement. To address this, we propose the Framework for Iterative to Non-iterative Distillation (FIND), which efficiently transfers these capabilities to a Non-Iterative Lightweight (NIL) network. FIND employs a dual-step process: first, using recurrent distillation to derive a high-performance non-iterative teacher assistant from an iterative network; second, using advanced feature distillation from the assistant to the lightweight network. This enables NIL to perform rapid, effective registration on resource-limited devices. Experiments across four datasets show that NIL can achieve up to 60 times faster performance on CPU and 89 times on GPU than compared deep learning methods, with superior registration accuracy improvements of up to 3.5 points in Dice scores. Code is available at https://anonymous.4open.science/r/FIND-7A16.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143763733","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

Non-Invasive Neural Interfacing for Tetraplegic Individuals Using Residual Motor Neuron Activity Decoded At the Forearm or Wrist. 利用前臂或手腕的残余运动神经元活动解码四肢瘫痪患者的非侵入性神经接口。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-04-01 DOI: 10.1109/JBHI.2025.3556496

Xingchen Yang, Daniela Souza de Oliveira, Dominik I Braun, Matthias Ponfick, Dario Farina, Alessandro Del Vecchio

{"title":"Non-Invasive Neural Interfacing for Tetraplegic Individuals Using Residual Motor Neuron Activity Decoded At the Forearm or Wrist.","authors":"Xingchen Yang, Daniela Souza de Oliveira, Dominik I Braun, Matthias Ponfick, Dario Farina, Alessandro Del Vecchio","doi":"10.1109/JBHI.2025.3556496","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3556496","url":null,"abstract":"Hand paralysis due to spinal cord injury (SCI) greatly limits the quality of life of injured individuals. Despite complete loss of hand digit control, however, residual electrical muscle activity is often detected from these injured individuals. From this activity, individual motor unit action potentials can be identified and potentially used to infer their motion intent for interfacing purposes. We recently demonstrated that residual motor units can be decoded from tetraplegic individuals with SCI, by mapping both proximal and distal forearm activity using hundreds of electromyography (EMG) electrodes. Yet, few explored the feasibility of neural interfacing using only forearm motor units or even far-field wrist motor units in SCI, which will facilitate the use of fully wearable systems such as EMG bracelets. Here, we recognize finger gestures in eight tetraplegic individuals (Seven with motor complete SCI and one with motor incomplete SCI), using either forearm or wrist motor units. We demonstrate that motion- wise surface EMG decomposition can effectively increase the number of decomposed motor units from both forearm and wrist (on average 41.25 24.14 from the forearm and 30 9.72 from the wrist) and to reach high accuracy in gesture recognition at both locations (82% to 100% with the forearm data, and 62% to 99 with the wrist data). The decomposition met the requirement of real-time implementation. Moreover, the correlation between far-field motor units activity recorded from the wrist with the activity recorded at the forearm is revealed, further suggesting both locations are suitable for interfacing.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143763902","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