3 citations · 6 across the 2 of their papers we have counts for
2 papers
eess.IV2024★ 3 cited
Interpretability of Uncertainty: Exploring Cortical Lesion Segmentation in Multiple Sclerosis
Nataliia Molchanova, Alessandro Cagol, Pedro M. Gordaliza +8
Uncertainty quantification (UQ) has become critical for evaluating the reliability of artificial intelligence systems, especially in medical image segmentation. This study addresse…
eess.IV2023★ 3 cited
Fast refacing of MR images with a generative neural network lowers re-identification risk and preserves volumetric consistency
Nataliia Molchanova, Bénédicte Maréchal, Jean-Philippe Thiran +3
With the rise of open data, identifiability of individuals based on 3D renderings obtained from routine structural magnetic resonance imaging (MRI) scans of the head has become a g…