Lena Rettinger, Peter Putz, Lea Aichinger, Susanne Maria Javorszky, Klaus Widhalm, Veronika Ertelt-Bach, Andreas Huber, Sevan Sargis, Lukas Maul, Oliver Radinger, Franz Werner, Sebastian Kuhn
Telehealth Education in Allied Health Care and Nursing: Web-Based Cross-Sectional Survey of Students' Perceived Knowledge, Skills, Attitudes, and Experience
JMIR medical education
detection characterization hidden defects impurities damages homogeneous materials like aluminum die casting materials well composite materials like fibermetal laminates fml still challenge work discusses methods challenges datadriven modeling automated damage defect detectors using measured xray single multiprojection images three main issues identified data feature variance data feature labeling supervised machine learning missing ground truth shown simulation synthetic measuring data deliver ground truth dataset accurate labeling datadriven modeling cannot used directly predict defects manufacturing processes noise significant impact feature detection discussed datadriven feature detectors implemented semantic pixel convolutional neural networks experimental data measured different devices lowquality lowcost lowq xray radiography typical industrial midquality xray radiography computed tomography ct system stateoftheart highquality ct device goals work training robust generalized datadriven ml feature detectors synthetic data transition ct singleprojection radiography imaging analysis although title implies primary task pore characterization aluminum highpressure diecast materials methods results limited use case