Khadija Guejdad, Ali Ikrou, Camilla Strandell-Laine, Redouane Abouqal, Jihane Belayachi
Healthcare Students' Perceptions of the Quality of the Clinical Learning Environment in Morocco: A Cross-Sectional Study
Nursing research and practice
treating patients coronary artery disease concurrent renal concerns often encounter conundrum achieve clearer view vascular details minimizing contrast radiation doses percutaneous coronary intervention pci goal use deep learning dl create realtime roadmap guiding pci end segmentation critical first step paves way detailed vascular analysis unlike traditional supervised learning demands extensive labeling time manpower strategy leans toward semisupervised learning method economizes labeling efforts also aims reducing contrast radiation exposure cag data sourced eight tertiary centers taiwan comprising 500 labeled 8952 unlabeled images employing 400 labels training reserving 100 validation built unet based network within teacherstudent architecture initial teacher model updated 8952 unlabeled images inputted employing quality control strategy involving consistency regularization randaugment optimized teacher model produced pseudolabels label expansion utilized train final student model attained average dice similarity coefficient 09003 segmentation outperforming supervised learning methods label count even 5 labels semisupervised training results surpassed supervised method 100 labels inputted semisupervised approachs advantage extends beyond singleframe prediction yielding consistently superior results continuous angiography films high labeling cost hinders dl training semisupervised learning quality control pseudolabel expansion overcome dlassisted segmentation potentially provides realtime pci roadmap diminishes radiation contrast doses