3 papers
cs.CV2026
Detecting and refurbishing ground truth errors during training of deep learning-based echocardiography segmentation models
Iman Islam, Bram Ruijsink, Andrew J. Reader +1
Deep learning-based medical image segmentation typically relies on ground truth (GT) labels obtained through manual annotation, but these can be prone to random errors or systemati…
cs.CV2026
Confidence Matters: Uncertainty Quantification and Precision Assessment of Deep Learning-based CMR Biomarker Estimates Using Scan-rescan Data
Dewmini Hasara Wickremasinghe, Michelle Gibogwe, Andrew Bell +6
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…
cs.CV2025
Deep Learning-Based Fetal Lung Segmentation from Diffusion-weighted MRI Images and Lung Maturity Evaluation for Fetal Growth Restriction
Zhennan Xiao, Katharine Brudkiewicz, Zhen Yuan +7
Fetal lung maturity is a critical indicator for predicting neonatal outcomes and the need for post-natal intervention, especially for pregnancies affected by fetal growth restricti…