Abstract
The objective of this study was to examine changes in applied force and rate of chest compression (CC) during 5 min of CC with a target CC rate of 90/min (CC90) or 120/min (CC120) with and without metronome guidance during...
Read more
This work adds a cross-modal and cross-attention (C2MA) mechanism into a deep learning network aiming to improve accuracy and efficacy of acute ischemic stroke (AIS) lesion segmentation from CT perfusion maps. The proposed network uses a C2MA module directly to establish a spatial-wise relationship by using the multigroup non-local attention operation between two modal features and performs dynamic group-wise recalibration through group attention block. This study demonstrates the advantages of applying C2MA-network to segment AIS lesions, which yields promising segmentation accuracy and proves the potential of applying cross-modal interactions in attention to assist in identifying new imaging biomarkers for more accurately predicting AIS prognosis in future studies...
Read more
