IEEE Journal of Biomedical and Health Informatics最新文献

The boundaries and regions between individual classes in biomedical image classification are hazy and overlapping. These overlapping features make predicting the correct classification result for biomedical imaging data a difficult diagnostic task. Thus, in precise classification, it is frequently necessary to obtain all necessary information before making a decision. This article presents a novel deep-layered design architecture based on Neuro-Fuzzy-Rough intuition to predict hemorrhages using fractured bone images and head CT scans. To deal with data uncertainty, the proposed architecture design employs a parallel pipeline with rough-fuzzy layers. In this case, the rough-fuzzy function functions as a membership function, incorporating the ability to process rough-fuzzy uncertainty information. It not only improves the deep model's overall learning process, but it also reduces feature dimensions. The proposed architecture design improves the model's learning and self-adaptation capabilities. In experiments, the proposed model performed well, with training and testing accuracies of 96.77% and 94.52%, respectively, in detecting hemorrhages using fractured head images. The comparative analysis shows that the model outperforms existing models by an average of 2.6 $pm$ 0.90% on various performance metrics.

Most traditional instrument pose planning algorithms focus on optimizing the pose of vertical instruments in open spaces. However, there is a lack of research on pose planning for cantilevered instruments in confined environments. In this paper, we propose an innovative method to optimizing instrument pose under multi-objective constraints. The method introduces the concept of a personalized outer outer bounding sphere and defines the safe feasible region for intraoperative instruments based on the Euclidean distance. For optimizing handle orientation during surgery, we propose an algorithm based on a center search strategy, which ensures that the handle orientation solution set avoids interference with adjacent teeth. Additionally, we introduce an improved scheme based on the outer bounding sphere weighted average (OBS-WA) algorithm to optimize robotic arm joint angles, considering multi-objective constraints. One contribution of this study is the development of an improved skeleton-based instrument collision detection method that addresses the limitations of traditional triangular mesh detection in real-time performance. Another innovation lies in solving the multi-objective optimization problem within the oral cavity. By establishing a test system on an experimental platform, this study demonstrates compliance control and safety planning during tooth preparation.

Medical image segmentation plays a crucial role in computer-aided diagnosis and treatment planning. Unsupervised segmentation methods that can effectively leverage unlabeled data bring significant promise in clinical application. However, they remain a challenging task in maintaining anatomical structure topological consistency that often produces anatomical structure breaks, connectivity errors, or boundary discontinuities. To address these issues, we propose a novel Unsupervised Topological-Aware Diffusion Condensation Network (UTADC-Net) for medical image segmentation. Specifically, we design a diffusion condensation-based framework that achieves structural consistency in segmentation results by effectively modeling long-range dependencies between pixels and incorporating topological constraints. First, to effectively fuse local details and global semantic information, we employ a pixel-centric patch embedding module by simultaneously modeling local structural features and inter-region interactions. Second, to enhance the topological consistency of segmentation results, we introduce an adaptive topological constraint mechanism that guides the network to learn anatomically aligned structural representations through pixel-level topological relationships and corresponding loss functions. Extensive experiments conducted on three public medical image datasets demonstrate that our proposed UTADC-Net significantly outperforms existing unsupervised methods in terms of segmentation accuracy and topological structure preservation. Notably, our method demonstrates segmentation results with excellent anatomical structural consistency. These results indicate that our framework provides a novel and practical solution for unsupervised medical image segmentation.

The interaction between mothers and young children is a highly dynamic process neurally characterized by inter-brain synchrony (IBS) at θ and/or α rhythms. However, their establishment, dynamic changes, and roles in mother-child interactions remain unknown. In this study, through a simultaneous dynamic analysis of inter-brain EEG synchrony, intra-brain EEG power, and interactive behaviors from 40 mother-preschooler dyads during turn-taking cooperation, we constructed a dynamic inter-brain model that θ-IBS and α-IBS alternated with interactive behaviors, with EEG frequency-shift as a prerequisite for IBS transitions. When mothers attempt to track their children's attention and/or predict their intentions, they will adjust their EEG frequencies to align with their children's θ oscillations, leading to a higher occurrence of the θ-IBS state. Conversely, the α-IBS state, accompanied by the EEG frequency-shift to the α range, is more prominent during mother-led interactions. Further exploratory analysis reveals greater presence and stability of the θ-IBS state during cooperative than non-cooperative conditions, particularly in dyads with stronger emotional attachments and more frequent interactions in their daily lives. Our findings shed light on the neural oscillatory substrates underlying the IBS dynamics during mother-preschooler interactions.

Accurate identification and classification of white blood cells are essential for diagnosing hematological malignancies and analyzing blood disorders. Existing approaches predominantly leverage masked autoencoders (MAEs) to extract intrinsic blood cell features through image reconstruction as a pretext task. However, these methods encounter two critical challenges: (1) their generalization performance deteriorates under domain shifts caused by variations in staining techniques, illumination conditions, and microscope settings, and (2) the learned data distribution often deviates from the true distribution of blood cell features. To overcome these limitations, we propose CD-CBC, a novel framework for cross-domain blood cell image classification that integrates contrastive representation learning with a denoising mechanism. CD-CBC consists of two key components: a LoRA-based segmentation anything model (LoRA-SAM) and a contrastive masked autoencoder (CMAE). LoRA-SAM mitigates shortcut learning in contrastive learning by eliminating background noise and platelet interference, while CMAE captures fine-grained semantic features and models spatial relationships, enhancing cross-domain robustness. Additionally, we introduce a denoising mechanism in the latent space, which guides the model to focus on unmasked patches during reconstruction, allowing it to better capture the true distribution of blood cell features. Extensive experiments on two benchmark blood cell datasets demonstrate that CD-CBC achieves superior cross-domain performance, reaching an average accuracy of 62.47%, which is 3.17% higher than the current state-of-the-art, thereby confirming its strong generalization capability.

As time-series data from the Internet of Medical Things (IoMT) increasingly permeates various aspects of medical research, public governance, and clinical treatment, its sensitivity raises significant privacy concerns, hindering the potential of deep learning applications for cross-institutional data integration. Previous practices focused on deep learning methods based on centralized data storage and processing, which are often unsuitable for decentralized and privacy-sensitive IoMT data scenarios. Most existing methods rely on mechanisms such as trusted coordinators, which face challenges in addressing potential passive data leakage and side-channel attacks, failing to effectively protect the privacy of sensitive data during collaborative training. To address these issues, we propose a privacy-preserving collaborative training model, Secure Long Sequence Time-Series Forecasting (SecLSTF), for IoMT time-series data and design a mapping strategy between model components and Multi-Party Computation (MPC) protocols. Building on this foundation, we propose a novel secret sharing protocol, Pleione, which focuses on optimizing the computational efficiency of the low-level secret-sharing protocol. The protocol centers on a hyper-invertible matrix and adopts a paired double random expansion mechanism, significantly reducing the communication rounds required for random number generation. This optimization enhances the overall training speed of SecLSTF. Subsequently, we replace the original computational support protocol with Pleione. Experimental results show that SecLSTF-Pleione significantly reduces computational time while maintaining computational accuracy, outperforming other protocols in component efficiency. This study offers a potential pathway for cross-institutional IoMT data sharing.

Medical records contain highly sensitive patient information. These medical records are significant for better research, diagnosis, and treatment. However, ensuring secure medical records storage is paramount to protect patient confidentiality, integrity, and privacy. Conventional methods involve encrypting and storing medical records in third-party clouds. Such storage enables convenient access and remote consultation. This cloud storage poses single-point attack risks and may lead to erroneous diagnoses and treatment. To address this, a novel (n,n)VSS scheme is proposed with data embedding, permutation ordered binary number system, tamper detection, and self-recovery mechanism. This approach enables the reconstruction of medical records even in the case of tampering. The tamper detection algorithm ensures data integrity. Simulation results demonstrate the superiority of proposed method in terms of security and reconstruction quality. Here, security analysis is done by considering attacks such as brute force, differential, and tampering attacks. Similarly, the reconstruction quality is evaluated using various human visual system parameters. The results show that the proposed technique provides a lower bit error rate ($approx$0), high average peak signal-to-noise ratio ($approx$35 dB), high structured similarity ($approx$1), high text embedding rate ($approx$0.7 BPP), and lossless reconstruction in the case of attacks.

Advances in deep learning have transformed medical imaging, yet progress is hindered by data privacy regulations and fragmented datasets across institutions. To address these challenges, we propose FedVGM, a privacy-preserving federated learning framework for multi-modal medical image analysis. FedVGM integrates four imaging modalities, including brain MRI, breast ultrasound, chest X-ray, and lung CT, across 14 diagnostic classes without centralizing patient data. Using transfer learning and an ensemble of VGG16 and MobileNetV2, FedVGM achieves 97.7% $pm$ 0.01 accuracy on the combined dataset and 91.9-99.1% across individual modalities. We evaluated three aggregation strategies and demonstrated median aggregation to be the most effective. To ensure clinical interpretability, we apply explainable AI techniques and validate results through performance metrics, statistical analysis, and k-fold cross-validation. FedVGM offers a robust, scalable solution for collaborative medical diagnostics, supporting clinical deployment while preserving data privacy.

Hypertension is a critical cardiovascular risk factor, underscoring the necessity of accessible blood pressure (BP) monitoring for its prevention, detection, and management. While cuffless BP estimation using wearable cardiovascular signals via deep learning models (DLMs) offers a promising solution, their implementation often entails high computational costs. This study addresses these challenges by proposing an end-to-end broad learning model (BLM) for efficient cuffless BP estimation. Unlike DLMs that prioritize network depth, the BLM increases network width, thereby reducing computational complexity and enhancing training efficiency for continuous BP estimation. An incremental learning mode is also explored to provide high memory efficiency and flexibility. Validation on the University of California Irvine (UCI) database (403.67 hours) demonstrated that the standard BLM (SBLM) achieved a mean absolute error (MAE) of 11.72 mmHg for arterial BP (ABP) waveform estimation, performance comparable to DLMs such as long short-term memory (LSTM) and the one-dimensional convolutional neural network (1D-CNN), while improving training efficiency by 25.20 times. The incremental BLM (IBLM) offered horizontal scalability by expanding through node addition in a single layer, maintaining predictive performance while reducing storage demands through support for incremental learning with streaming or partial datasets. For systolic and diastolic BP prediction, the SBLM achieved MAEs (mean error $pm$ standard deviation) of 3.04 mmHg (2.85 $pm$ 4.15 mmHg) and 2.57 mmHg (-2.47 $pm$ 3.03 mmHg), respectively. This study highlights the potential of BLM for personalized, real-time, continuous cuffless BP monitoring, presenting a practical solution for healthcare applications.

Foundation Models (FMs) have shown great promise for multimodal medical image analysis such as Magnetic Resonance Imaging (MRI). However, certain MRI sequences may be unavailable due to various constraints, such as limited scanning time, patient discomfort, or scanner limitations. The absence of certain modalities can hinder the performance of FMs in clinical applications, making effective missing modality imputation crucial for ensuring their applicability. Previous approaches, including generative adversarial networks (GANs), have been employed to synthesize missing modalities in either a one-to-one or many-to-one manner. However, these methods have limitations, as they require training a new model for different missing scenarios and are prone to mode collapse, generating limited diversity in the synthesized images. To address these challenges, we propose DiffM⁴RI, a diffusion model for many-to-many missing modality imputation in MRI. DiffM⁴RI innovatively formulates the missing modality imputation as a modality-level inpainting task, enabling it to handle arbitrary missing modality situations without the need for training multiple networks. Experiments on the BraTs datasets demonstrate DiffM⁴RI can achieve an average SSIM improvement of 0.15 over MustGAN, 0.1 over SynDiff, and 0.02 over VQ-VAE-2. These results highlight the potential of DiffM⁴RI in enhancing the reliability of FMs in clinical applications.