Due to the significant amount of time and expertise needed for manual segmentation of the brain cortex from magnetic resonance imaging (MRI) data, there is a substantial need for efficient and ...
As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective ...
Abstract: Dynamic convolution demonstrates outstanding representation capabilities, which are crucial for natural image segmentation. However, it fails when applied to medical image segmentation (MIS) ...
FLAMeS, a new convolutional neural network, enhances MS lesion segmentation accuracy using only T2-weighted FLAIR images, making it more applicable in clinical settings. The algorithm outperformed ...
Network segmentation has been a security best practice for decades, yet for many reasons, not all network deployments have fully embraced the approach of microsegmentation. With ransomware attacks ...
Abstract: A self-attention powered graph convolution network (GCN) is proposed for electrical resistance tomography (ERT) and ultrasonic transmission tomography (UTT) dual-modality tomography. It’s ...
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