IEEE Journal of Biomedical and Health Informatics最新文献_第3页

MMPI Net: A Novel Multimodal Model Considering the Similarities Between Perception and Imagination for Image evoked EEG Decoding.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-24 DOI: 10.1109/JBHI.2025.3554664

Jinze Tong, Wanzhong Chen

{"title":"MMPI Net: A Novel Multimodal Model Considering the Similarities Between Perception and Imagination for Image evoked EEG Decoding.","authors":"Jinze Tong, Wanzhong Chen","doi":"10.1109/JBHI.2025.3554664","DOIUrl":"10.1109/JBHI.2025.3554664","url":null,"abstract":"In recent years, non-invasive electroencephalography (EEG) has been widely used to decode high-level cognitive functions such as visual perception and imagination. The processes of visual perception and imagination in the human brain have been shown to share similar neural circuits and activation patterns in cognitive science. However, current research predominantly focuses on single cognitive processes, overlooking the natural commonalities between these processes and the insights that multimodal approaches can provide. To address this, this study proposes a novel multimodal model, MMPI Net, for jointly decoding EEG signals of visual image perception and imagination. MMPI Net comprises four components: Primitive Feature Extraction for Perception and Imagination (PFE), Cross-Semantic Feature Fusion (CSFF), Joint Semantic Feature Decoder (JSFD), and Semantic Classification (SC). To ensure the effectiveness of PFEM, an Improved Channel Attention Mechanism is introduced, which employs multiple parallel convolutional branches to enhance the extraction of important information and utilizes a Diverse Branch Block approach to reduce the parameter count. In the CSFF module, a cross-attention-based fusion method is designed to effectively capture and utilize intermodal information. In the JSFD phase, a Kolmogorov-Arnold Network is incorporated and coupled with linear layers to improve classification performance. Finally, a linear layer with Softmax is used as the SC module. Experimental results on two publicly available datasets show that, compared to models that use a single cognitive process, MMPI Net achieves average accuracy improvements of 14.22% and 106.1%, demonstrating its effectiveness.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143700354","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

Robust Steganography Technique for Enhancing the Protection of Medical Records in Healthcare Informatics.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-24 DOI: 10.1109/JBHI.2025.3554032

Hammad Riaz, Rizwan Ali Naqvi, Manzoor Ellahi, M Arslan Usman, M Rehan Usman, Daesik Jeong, Seung Won Lee

引用次数: 0

A Cuffless Blood Pressure Estimation Method Using Dimensionality Increasing and Two-Dimensional Convolution.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-21 DOI: 10.1109/JBHI.2025.3551613

Shouyi Cui, Guowei Yang, Jingxuan Guan, Yuheng He, Xuefang Zhou, Meihua Bi, Hanghai Shen, Yuansheng Xu

{"title":"A Cuffless Blood Pressure Estimation Method Using Dimensionality Increasing and Two-Dimensional Convolution.","authors":"Shouyi Cui, Guowei Yang, Jingxuan Guan, Yuheng He, Xuefang Zhou, Meihua Bi, Hanghai Shen, Yuansheng Xu","doi":"10.1109/JBHI.2025.3551613","DOIUrl":"10.1109/JBHI.2025.3551613","url":null,"abstract":"Blood pressure (BP) monitoring is a basic way to evaluate hypertension and its related diseases. Since non-invasive measurement with cuff is not real-time and invasive measurement with vessel puncture is not practical in daily life, this paper proposes a cuffless BP estimation method using two-dimensional (2D) convolution. Dimensionality increasing algorithms including recurrence plot and Gramian angular field are firstly used to convert electrocardiography (ECG) and photoplethysmography (PPG) signals into 2D images. New fused Gramian angular field (FGAF) and combined Gramian angular field (CGAF) are proposed to reduce the input 2D images data and enhance the signals' relevance. The converted images are used to train 2D convolutional models and estimate BP values. The 2D models effectively improved BP estimation accuracy, and the accuracy of the VGGNet 2D model using Gramian angular difference field (GADF) is improved by 38% compared with the corresponding 1D convolutional model. The proposed FGAF and CGAF can reduce input data by 50% while maintaining estimation accuracy, and the minimum mean absolute errors of the estimated BP values could reach 2.71 and 1.74 mmHg for systolic and diastolic blood pressures, respectively. To reduce model size, the VGGNet BP estimation model is pruned by reducing 60% of channel numbers while maintain the model performance. The pruned VGGNet model using the FGADF is then fine-tuned and validated by MIMIC-III dataset to show its generalization ability. Furthermore, a simple monitor system is built to show the feasibility of signal collection and BP estimation.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143673640","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

DentAssignNet: Assignment Network for Dental Cast Labeling in the Presence of Dental Abnormalities.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-20 DOI: 10.1109/JBHI.2025.3549685

Tudor Dascalu, Shaqayeq Ramezanzade, Azam Bakhshandeh, Lars Bjorndal, Raluca Iurcov, Tomaz Vrtovec, Bulat Ibragimov

{"title":"DentAssignNet: Assignment Network for Dental Cast Labeling in the Presence of Dental Abnormalities.","authors":"Tudor Dascalu, Shaqayeq Ramezanzade, Azam Bakhshandeh, Lars Bjorndal, Raluca Iurcov, Tomaz Vrtovec, Bulat Ibragimov","doi":"10.1109/JBHI.2025.3549685","DOIUrl":"10.1109/JBHI.2025.3549685","url":null,"abstract":"This study focuses on the challenging problem of labeling a collection of objects with inherent morphological and positional dependencies, where instances may be missing or duplicated. We integrate principles of assignment theory in the design of a convolutional neural network to find the optimal label set given pairwise geometrical features extracted from the candidate objects. The objective function aims to minimize the distance between the one-hot encoded labels of the objects and the scores produced by the model, with added emphasis on the scores corresponding to the optimal assignment plan. We tested our solution in the dental domain on the task of finding the teeth labels given a set of candidate instances. The study database included 1200 dental casts of upper and lower jaws from 600 patients. The model reached identification accuracies of 0.952 and 0.968 for the lower and upper jaws, respectively. Moreover, we presented a solution for generating teeth candidates using a multi-step pipeline consisting of coarse and fine segmentations. The algorithm was tested on a database consisting of 600 dental casts, reaching an F1 score of 0.968.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143669761","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

Predicting Single-Cell Drug Sensitivity Utilizing Adaptive Weighted Features for Multi-Source Domain Adaptation. 利用多源域自适应加权特征预测单细胞药物敏感性

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-20 DOI: 10.1109/JBHI.2025.3553126

Wei Duan, Hui Liu, Judong Luo

{"title":"Predicting Single-Cell Drug Sensitivity Utilizing Adaptive Weighted Features for Multi-Source Domain Adaptation.","authors":"Wei Duan, Hui Liu, Judong Luo","doi":"10.1109/JBHI.2025.3553126","DOIUrl":"10.1109/JBHI.2025.3553126","url":null,"abstract":"The advancement of single-cell sequencing technology has promoted the generation of a large amount of single-cell transcriptional profiles, providing unprecedented opportunities to identify drug-resistant cell subpopulations within a tumor. However, few studies have focused on drug response prediction at single-cell level, and their performance remains suboptimal. This paper proposed scAdaDrug, a novel multi-source domain adaptation model powered by adaptive importance-aware representation learning to predict drug response of individual cells. We used a shared encoder to extract domain-invariant features related to drug response from multiple source domains by utilizing adversarial domain adaptation. Particularly, we introduced a plug-and-play module to generate importance-aware and mutually independent weights, which could adaptively modulate the latent representation of each sample in element-wise manner between source and target domains. Extensive experimental results showed that our model achieved state-of-the-art performance in predicting drug response on multiple independent datasets, including single-cell datasets derived from both cell lines and patient-derived xenografts (PDX) models, as well as clinical tumor patient cohorts. Moreover, the ablation experiments demonstrated our model effectively captured the underlying patterns determining drug response from multiple source domains. The source codes and datasets are available at: https://github.com/hliulab/scAdaDrug.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143669774","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

Multi-modal deep representation learning accurately identifies and interprets drug-target interactions.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-20 DOI: 10.1109/JBHI.2025.3553217

Jiayue Hu, Yuhang Liu, Xiangxiang Zeng, Quan Zou, Ran Su, Leyi Wei

{"title":"Multi-modal deep representation learning accurately identifies and interprets drug-target interactions.","authors":"Jiayue Hu, Yuhang Liu, Xiangxiang Zeng, Quan Zou, Ran Su, Leyi Wei","doi":"10.1109/JBHI.2025.3553217","DOIUrl":"10.1109/JBHI.2025.3553217","url":null,"abstract":"Deep learning offers efficient solutions for drug-target interaction prediction, but current methods often fail to capture the full complexity of multi-modal data (i.e. sequence, graphs, and three-dimensional structures), limiting both performance and generalization. Here, we present UnitedDTA, a novel explainable deep learning framework capable of integrating multi-modal biomolecule data to improve the binding affinity prediction, especially for novel (unseen) drugs and targets. UnitedDTA enables automatic learning unified discriminative representations from multi-modality data via contrastive learning and cross-attention mechanisms for cross-modality alignment and integration. Comparative results on multiple benchmark datasets show that UnitedDTA significantly outperforms the state-of-the-art drug-target affinity prediction methods and exhibits better generalization ability in predicting unseen drug-target pairs. More importantly, unlike most \"black-box\" deep learning methods, our well-established model offers better interpretability which enables us to directly infer the important substructures of the drug-target complexes that influence the binding activity, thus providing the insights in unveiling the binding preferences. Moreover, by extending UnitedDTA to other downstream tasks (e.g. molecular property prediction), we showcase the proposed multi-modal representation learning is capable of capturing the latent molecular representations that are closely associated with the molecular property, demonstrating the broad application potential for advancing the drug discovery process.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143669767","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

Infusing Multi-Hop Medical Knowledge Into Smaller Language Models for Biomedical Question Answering.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-19 DOI: 10.1109/JBHI.2025.3547444

Jing Chen, Zhihua Wei, Wen Shen, Rui Shang

{"title":"Infusing Multi-Hop Medical Knowledge Into Smaller Language Models for Biomedical Question Answering.","authors":"Jing Chen, Zhihua Wei, Wen Shen, Rui Shang","doi":"10.1109/JBHI.2025.3547444","DOIUrl":"10.1109/JBHI.2025.3547444","url":null,"abstract":"MedQA-USMLE is a challenging biomedical question answering (BQA) task, as its questions typically involve multi-hop reasoning. To solve this task, BQA systems should possess substantial medical professional knowledge and strong medical reasoning capabilities. While state-of-the-art larger language models, such as Med-PaLM 2, have overcome this challenge, smaller language models (SLMs) still struggle with it. To bridge this gap, we introduces a multi-hop medical knowledge infusion (MHMKI) procedure to endow SLMs with medical reasoning capabilities. Specifically, we categorize MedQA-USMLE questions into distinct reasoning types, then create pre-training instances tailored to each type of questions with the semi-structured information and hyperlinks of Wikipedia articles. To enable SLMs to efficiently capture the multi-hop knowledge embedded in these instances, we design a reasoning chain masked language model for further pre-training of BERT models. Moreover, we transform these pre-training instances into a combined question answering dataset for intermediate fine-tuning of GPT models. We evaluate MHMKI with six SLMs (three BERT models and three GPT models) across five datasets spanning three BQA tasks. Results show that MHMKI benefits SLMs in nearly all tasks, especially those requiring multi-hop reasoning. For instance, the accuracy of MedQA-USMLE shows a significant increase of 5.3% on average.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143663571","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

Blockchain-Enhanced Anonymous Data Sharing Scheme for 6G-Enabled Smart Healthcare With Distributed Key Generation and Policy Hiding.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-19 DOI: 10.1109/JBHI.2025.3550261

Xujie Ding, Yali Liu, Jianting Ning, Dongdong Chen

{"title":"Blockchain-Enhanced Anonymous Data Sharing Scheme for 6G-Enabled Smart Healthcare With Distributed Key Generation and Policy Hiding.","authors":"Xujie Ding, Yali Liu, Jianting Ning, Dongdong Chen","doi":"10.1109/JBHI.2025.3550261","DOIUrl":"10.1109/JBHI.2025.3550261","url":null,"abstract":"In recent years, cloud computing has seen widespread application in 6G-enabled smart healthcare, which facilitates the sharing of medical data. Before uploading medical data to cloud server, numerous data sharing schemes employ attribute-based encryption (ABE) to encrypt the sensitive medical data of data owner (DO), and only provide access to date user (DU) who meet certain conditions, which leads to privacy leakage and single points of failure, etc. This paper proposes a blockchain-enhanced anonymous data sharing scheme for 6G-enabled smart healthcare with distributed key generation and policy hiding, termed BADS-ABE, which achieves secure and efficient sharing of sensitive medical data. BADS-ABE designs an anonymous authentication scheme based on Groth signature, which ensures integrity of medical data and protects the identity privacy of DO. Meanwhile, BADS-ABE employs smart contract and Newton interpolation to achieve distributed key generation, which eliminates single point of failure due to the reliance on trusted authority (TA). Moreover, BADS-ABE achieves policy hiding and matching, which avoids the waste of decryption resources and protects the attribute privacy of DO. Finally, security analysis demonstrates that BADS-ABE meets the security requirements of a data sharing scheme for smart healthcare. Performance analysis indicates that BADS-ABE is more efficient compared with similar data sharing schemes.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143663570","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

TPNET: A time-sensitive small sample multimodal network for cardiotoxicity risk prediction.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-19 DOI: 10.1109/JBHI.2025.3552819

Yuan He, Fengyun Zhang, Kaimiao Hu, Changming Sun, Jie Geng, Ning Ren, Ran Su

{"title":"TPNET: A time-sensitive small sample multimodal network for cardiotoxicity risk prediction.","authors":"Yuan He, Fengyun Zhang, Kaimiao Hu, Changming Sun, Jie Geng, Ning Ren, Ran Su","doi":"10.1109/JBHI.2025.3552819","DOIUrl":"10.1109/JBHI.2025.3552819","url":null,"abstract":"Cancer therapy-related cardiac dysfunction (CTRCD) is a potential complication associated with cancer treatment, particularly in patients with breast cancer, requiring monitoring of cardiac health during the treatment process. Tissue Doppler imaging (TDI) is a remarkable technique that can provide a comprehensive reflection of the left ventricle's physiological status. We hypothesized that the combination of TDI features with deep learning techniques could be utilized to predict CTRCD. To evaluate the hypothesis, we developed a temporal-multimodal pattern network for efficient training (TPNET) model to predict the incidence of CTRCD over a 24-month period based on TDI, function, and clinical data from 270 patients. Our model achieved an area under curve (AUC) of 0.83 and sensitivity of 0.88, demonstrating greater robustness compared to other existing visual models. To further translate our model's findings into practical applications, we utilized the integrated gradients (IG) attribution to perform a detailed evaluation of all the features. This analysis has identified key pathogenic signs that may have remained unnoticed, providing a viable option for implementing our model in preoperative breast cancer patients. Additionally, our findings demonstrate the potential of TPNET in discovering new causative agents for CTRCD.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143663572","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

3D ShiftBTS: Shift Operation for 3D Multimodal Brain Tumor Segmentation.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-18 DOI: 10.1109/JBHI.2025.3552166

Guangqi Yang, Xiaoxin Guo, Haoran Zhang, Zhenyuan Zheng, Hongliang Dong, Songbai Xu

{"title":"3D ShiftBTS: Shift Operation for 3D Multimodal Brain Tumor Segmentation.","authors":"Guangqi Yang, Xiaoxin Guo, Haoran Zhang, Zhenyuan Zheng, Hongliang Dong, Songbai Xu","doi":"10.1109/JBHI.2025.3552166","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3552166","url":null,"abstract":"Recently, ShiftViT and its variants have attracted much attention for their simple and efficient shift operation, showing excellent efficacy in several tasks on natural images, surpassing Swin Transformer. However, considering the complexity of 3D multimodal images, which have higher dimensions than natural images, and the relative stability of the human tissue structure in medical images, the applicability of shift operation on 3D multimodal medical data has yet to be determined. This paper demonstrates that ShiftViT has enormous potential in 3D multimodal medical image analysis. Using 3D medical image segmentation as a representative downstream task, we investigate how shift operation can improve model performance. First, applying ShiftViT to 3D multimodal medical images not only effectively extracts global information but also significantly enhances the model's performance. Second, as a plug-and-play strategy, the shift operation can be integrated with other modules without adding additional computational burden, proving its flexibility in the overall system. Finally, we further investigate the generalizability of the shift operation by introducing a cascaded attention module, which provides useful insights to improve the generalizability of 3D medical image segmentation models. Through this study, we extend the application scope of ShiftViT and bring new exploration directions to the field of 3D multimodal medical image analysis. Our research results prove the feasibility of applying ShiftViT in 3D multimodal medical images and provide an effective and scalable model, which is expected to further promote the development of medical image processing technology. The code will be published in https://github.com/ydlam/3D-ShiftBTS.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143657001","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