Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
40039110
EEG classification is a challenging task due to the nonstationary nature of EEG data and the covariance shift induced by cross-subject variance. Recently, various machine learning and deep learning models have been developed to learn robust features ...
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
40038928
Lung segmentation is the first important step for lung nodule detection and lung cancer analysis. Deep neural networks have achieved state-of-the-art for most tasks in medical image analysis, including lung segmentation. However, training a deep lear...
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
40031517
Diffeomorphic image registration is a fundamental step in medical image analysis, owing to its capability to ensure the invertibility of transformations and preservation of topology. Currently, unsupervised learning-based registration techniques prim...
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
40031560
Myoelectric control interfaces, which map electromyographic (EMG) signals into control commands for external devices, have applications in active prosthesis control. However, the statistical characteristics of EMG signals change over time (e.g., beca...
Unsupervised domain adaptation (UDA) in medical image segmentation aims to improve the generalization of deep models by alleviating domain gaps caused by inconsistency across equipment, imaging protocols, and patient conditions. However, existing UDA...
IEEE journal of biomedical and health informatics
40030830
Cerebrovascular segmentation from time-of-flight magnetic resonance angiography (TOF-MRA) and computed tomography angiography (CTA) is essential in providing supportive information for diagnosing and treatment planning of multiple intracranial vascul...
IEEE journal of biomedical and health informatics
40030793
It is difficult for general registration methods to establish the fine correspondence between images with complex anatomical structures. To overcome the above problem, this work presents SFM-Net, an unsupervised multi-stage semantic feature-based net...
IEEE journal of biomedical and health informatics
40031262
Robust decoding performance is essential for the practical deployment of brain-computer interface (BCI) systems. Existing EEG decoding models often rely on large amounts of annotated data collected through specific experimental setups, which fail to ...
Cardiac motion estimation is important for assessing the contractile health of the heart, and performing this in 3D can provide advantages due to the complex 3D geometry and motions of the heart. Deep learning image registration (DLIR) is a robust wa...
Supervised Deep-Learning (DL)-based reconstruction algorithms have shown state-of-the-art results for highly-undersampled dynamic Magnetic Resonance Imaging (MRI) reconstruction. However, the requirement of excessive high-quality ground-truth data hi...