IEEE journal of biomedical and health informatics
Dec 5, 2024
4D Flow Magnetic Resonance Imaging (4D Flow MRI) is a non-invasive measurement technique capable of quantifying blood flow across the cardiovascular system. While practical use is limited by spatial resolution and image noise, incorporation of traine...
Robot-assisted gait training (RAGT) is a promising technique for improving the gait ability of elderly adults and patients with gait disorders by enabling high-intensive and task-specific training. Gait functions involve multiple brain regions and ne...
RATIONALE AND OBJECTIVES: To develop interpretable machine learning models that utilize deep learning (DL) and radiomics based on multiparametric Magnetic resonance imaging (MRI) to predict preoperative lymph node (LN) metastasis in rectal cancer.
Gliomas are the most common and malignant form of primary brain tumors. Accurate segmentation and measurement from MRI are crucial for diagnosis and treatment. Due to the infiltrative growth pattern of gliomas, their labeling is very difficult. In tu...
The prediction of brain cancer occurrence and risk assessment of brain hemorrhage using a hybrid deep learning (DL) technique is a critical area of research in medical imaging analysis. One prominent challenge in this field is the accurate identifica...
IEEE transactions on pattern analysis and machine intelligence
Dec 4, 2024
Multi-modality imaging is widely used in clinical practice and biomedical research to gain a comprehensive understanding of an imaging subject. Currently, multi-modality imaging is accomplished by post hoc fusion of independently reconstructed images...
Brain connectivity represents the functional organization of the brain, which is an important indicator for evaluating neuropsychiatric disorders and treatment effects. Schizophrenia is associated with impaired functional connectivity but characteriz...
Recently, Deep Learning (DL) models have shown promising accuracy in analysis of medical images. Alzeheimer Disease (AD), a prevalent form of dementia, uses Magnetic Resonance Imaging (MRI) scans, which is then analysed via DL models. To address the ...
The early detection of Alzheimer's Disease (AD) is thought to be important for effective intervention and management. Here, we explore deep learning methods for the early detection of AD. We consider both genetic risk factors and functional magnetic ...
Neural networks : the official journal of the International Neural Network Society
Dec 3, 2024
In practice, collecting auxiliary labeled data with same feature space from multiple domains is difficult. Thus, we focus on the heterogeneous transfer learning to address the problem of insufficient sample sizes in neuroimaging. Viewing subjects, ti...