Article
Engineering, Biomedical
Linlin Zhu, Yu Han, Xiaoqi Xi, Lei Li, Mengnan Liu, Huijuan Fu, Siyu Tan, Bin Yan
Summary: This paper proposes an unsupervised CycleGAN model based on the efficient Transformer for metal artifact reduction in CT images. The model correlates global features through attention mechanisms and generates vital features to enhance the consistency of recovery information. Experimental results demonstrate that the proposed method outperforms existing methods in suppressing metal artifacts.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Debashis Das Chakladar, Partha Pratim Roy, Victor Chang
Summary: This paper proposes an Integrated Spatio-Temporal Deep Clustering (ISTDC) model that utilizes deep representation learning (DRL) to transform high-dimensional electroencephalography (EEG) data into low-dimensional feature space and improve clustering performance. Experimental results demonstrate that the proposed model achieves significant improvement in workload estimation compared to the state-of-the-art methods.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Yupeng Qiang, Xunde Dong, Yang Yang
Summary: This paper presents a multi-channel dense attention neural network (MCDANN) for myocardial infarction (MI) detection and localization using 12-lead ECG signals without denoising preprocessing. The MCDANN model exhibits remarkable performance in MI detection and localization, both for intra-patient and inter-patient cases. Notably, it substantially enhances the performance of inter-patient MI localization compared to other existing methods.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Qizhong Zhang, Junda Bai, Yang Liu, Yizhi Zhou
Summary: This study aims to develop a brain-computer interface (BCI) for dynamic motor imagery (dMI) electroencephalograph (EEG). The proposed method combines synchronization likelihood (SL) based functional brain network (FBN) and modified extreme learning machine (ELM) to interpret EEG signals. The method improves computational complexity and recognition rate.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Jens Moeller, Eveline Popanda, Nuri H. Aydin, Hubert Welp, Iris Tischoff, Carsten Brenner, Kirsten Schmieder, Martin R. Hofmann, Dorothea Miller
Summary: In this study, a method based on texture features is proposed, which can classify healthy gray and white matter against glioma degrees 4 samples with reasonable classification performance using a relatively low number of samples for training. The method achieves high classification performance without the need for large datasets and complex machine learning approaches.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Tianjiao Guo, Jie Yang, Qi Yu
Summary: This paper proposes a CNN-based approach for segmenting four typical DR lesions simultaneously, achieving competitive performance. This approach is significant for DR lesion segmentation and has potential in other segmentation tasks.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Shimin Liu, Zhiwen Huang, Jianmin Zhu, Baolin Liu, Panyu Zhou
Summary: In this study, a continuous blood pressure (BP) monitoring method based on random forest feature selection (RFFS) and a gray wolf optimization-gradient boosting regression tree (GWO-GBRT) prediction model was developed. The method extracted features from electrocardiogram (ECG) and photoplethysmography (PPG) signals, and employed RFFS to select sensitive features highly correlated with BP. A hybrid prediction model of gray wolf optimization (GWO) technique and gradient boosting regression tree (GBRT) algorithm was established to learn the relationship between BP and sensitive features. Experimental results demonstrated the effectiveness and advancement of the proposed method.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
D. Vineet Kumar, K. Vandana Dixit
Summary: This study utilized deep learning networks for skin lesion identification and segmentation, incorporating various features mining and model weight tuning to improve performance metrics such as accuracy.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Archana S. Nadhan, Jeena Jacob
Summary: The Internet of Things enables the connectivity of medical imaging devices to healthcare data, speeding up diagnosis and treatment. However, it also brings risks of cyber-attacks and unauthorized access. Research on cryptographic network for image encryption and decryption in the IoT healthcare context is explored, with the potential application of deep learning for secure medical image transmission.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Sally AbdulHussain Kadhum, Nassier A. Nassir
Summary: In this research, porous composites were successfully prepared and reinforced for bone scaffold applications. The functional groups, pore structure, and composition distribution of the materials were characterized using techniques such as FTIR, Atomic Force Microscopy (AFM), and Scanning Electron Microscopy (SEM).
JOURNAL OF THE MECHANICAL BEHAVIOR OF BIOMEDICAL MATERIALS
(2024)
Article
Computer Science, Interdisciplinary Applications
Nitzan Avidan, Moti Freiman
Summary: This study aims to enhance the generalization capabilities of DNN-based MRI reconstruction methods for undersampled k-space data. By introducing a mask-aware DNN architecture and training method, the under-sampled data and mask are encoded within the model structure, leading to improved performance. Rigorous testing on the widely accessible fastMRI dataset reveals that this approach demonstrates better generalization capabilities and robustness compared to traditional DNN methods.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Yunhao Bai, Wenqi Li, Jianpeng An, Lili Xia, Huazhen Chen, Gang Zhao, Zhongke Gao
Summary: This study proposes an effective MIL method for classifying WSI of esophageal cancer. The use of self-supervised learning for feature extractor pretraining enhances feature extraction from esophageal WSI, leading to more robust and accurate performance. The proposed framework outperforms existing methods, achieving an accuracy of 93.07% and AUC of 95.31% on a comprehensive dataset of esophageal slide images.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Ziyang Chen, Laura Cruciani, Elena Lievore, Matteo Fontana, Ottavio De Cobelli, Gennaro Musi, Giancarlo Ferrigno, Elena De Momi
Summary: In this paper, a deep learning-based approach is proposed to recover 3D information of intra-operative scenes, which can enhance the safety of robot-assisted surgery by implementing depth estimation using stereo images.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Engineering, Biomedical
Chenming Yao, Sofiane Boudaoud, Freddy Odille, Odette Fokapu
Summary: This article presents a preliminary study on the extraction and modeling of induced potentials (IPs) from contaminated electrocardiographic (ECG) signals using wavelet decomposition. The results show that four selected wavelets are efficient in extracting IPs. The evaluation of the modeling algorithm demonstrates promising results in terms of matching the shapes of the extracted IPs.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Ana Maria Amaro de Sousa, Michel J. A. M. van Putten, Stephanie van den Berg, Maryam Amir Haeri
Summary: Interictal discharges are important signatures of epilepsy and their detection can assist in epilepsy diagnostics. This study explored unsupervised and semi-supervised deep learning approaches for the automatic detection of these discharges in EEG recordings. The best performance was achieved using a semi-supervised approach, with a sensitivity of 81.9% and specificity of 91.7%.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Shuo Yang, Aoyang Shan, Lei Wang, Yangzheng Li, Shuo Liu
Summary: The STTransformer architecture, based on a two-stream attention network, achieved promising results in cross-task and cross-subject mental fatigue transfer learning. This architecture uses multiple attention mechanisms to capture common features between different individuals and experimental paradigms, showing good performance in multiple individual and two mental fatigue experiments.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Yang Zhao, Junhua Zhang, Hongjian Li, Qiyang Wang
Summary: This paper proposes an automatic AVR measurement method to address the low precision issue of manual measurement in Adolescent Idiopathic Scoliosis (AIS). The proposed method achieves accurate AVR estimation and improves the efficiency of orthopedists.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Jun Li, Zhijie Shi, Jialiang Zhu, Jin Liu, Lihua Qiu, Yeye Song, Liqun Wang, Yuling Li, Yongliang Liu, Dawei Zhang, Haima Yang, Le Fu
Summary: Automatic segmentation of the placenta in MRI is challenging due to the variability of its position and shape, as well as the blurring caused by PAS. In this study, a refinement fusion based on the U-Net (RFU-Net) is proposed to address these issues and improve the segmentation accuracy. The RFU-Net utilizes ResNet34 as a feature extractor and incorporates a fusion multiscale feature (FMF) to handle the variable placental shape. A refinement segmentation module (RSM) is designed to provide information about the placental position, leading to improved segmentation results. Experimental results demonstrate the effectiveness of RFU-Net in achieving accurate placenta segmentation and highlighting potential areas of interest for diagnosis.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Peixin Lu, Lianting Hu, Alexis Mitelpunkt, Surbhi Bhatnagar, Long Lu, Huiying Liang
Summary: This study proposes a hierarchical attention-based multimodal fusion framework (HAMF) for early detection of Alzheimer's Disease (AD) using imaging, genetic, and clinical data. HAMF outperforms unimodal models, achieving an accuracy of 87.2% and an AUC of 0.913. Comparison between unimodal and multimodal models shows that multimodal fusion improves model performance, with clinical data being the most important modality.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Yuhao Tang, Dacheng Wang, Liyan Zhang, Ye Yuan
Summary: This study focuses on the automatic generation of radiology reports to alleviate the burden on doctors. The proposed abnormal semantic diffusion module and length-controllable self attention decoder improve the efficiency and quality of report generation. Additionally, a novel XRG-COVID-19 clinical dataset is tailored for experimental evaluation.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
