AI RESEARCH
Confidence Matters: Uncertainty Quantification and Precision Assessment of Deep Learning-based CMR Biomarker Estimates Using Scan-rescan Data
arXiv CS.CV
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ArXi:2603.26789v1 Announce Type: new The performance of deep learning (DL) methods for the analysis of cine cardiovascular magnetic resonance (CMR) is typically assessed in terms of accuracy, overlooking precision. In this work, uncertainty estimation techniques, namely deep ensemble, test-time augmentation, and Monte Carlo dropout, are applied to a state-of-the-art DL pipeline for cardiac functional biomarker estimation, and new distribution-based metrics are proposed for the assessment of biomarker precision.