18 citations · 67 across the 9 of their papers we have counts for
9 papers
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…
Structural-Based Uncertainty in Deep Learning Across Anatomical Scales: Analysis in White Matter Lesion Segmentation
Nataliia Molchanova, Vatsal Raina, Andrey Malinin +7
This paper explores uncertainty quantification (UQ) as an indicator of the trustworthiness of automated deep-learning (DL) tools in the context of white matter lesion (WML) segment…
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…
Estimating axon radius using diffusion-relaxation MRI: calibrating a surface-based relaxation model with histology
Muhamed Barakovic, Marco Pizzolato, Chantal M. W. Tax +7
Axon radius is a potential biomarker for brain diseases and a crucial tissue microstructure parameter that determines the speed of action potentials. Diffusion MRI (dMRI) allows no…
Novel structural-scale uncertainty measures and error retention curves: application to multiple sclerosis
Nataliia Molchanova, Vatsal Raina, Andrey Malinin +6
This paper focuses on the uncertainty estimation for white matter lesions (WML) segmentation in magnetic resonance imaging (MRI). On one side, voxel-scale segmentation errors cause…
Shifts 2.0: Extending The Dataset of Real Distributional Shifts
Andrey Malinin, Andreas Athanasopoulos, Muhamed Barakovic +15
Distributional shift, or the mismatch between training and deployment data, is a significant obstacle to the usage of machine learning in high-stakes industrial applications, such…
Cortical lesions, central vein sign, and paramagnetic rim lesions in multiple sclerosis: emerging machine learning techniques and future avenues
Francesco La Rosa, Maxence Wynen, Omar Al-Louzi +11
The current multiple sclerosis (MS) diagnostic criteria lack specificity, and this may lead to misdiagnosis, which remains an issue in present-day clinical practice. In addition, c…
Model-Informed Machine Learning for Multi-component T2 Relaxometry
Thomas Yu, Erick Jorge Canales Rodriguez, Marco Pizzolato +9
Recovering the T2 distribution from multi-echo T2 magnetic resonance (MR) signals is challenging but has high potential as it provides biomarkers characterizing the tissue micro-st…
Shallow vs deep learning architectures for white matter lesion segmentation in the early stages of multiple sclerosis
Francesco La Rosa, Mário João Fartaria, Tobias Kober +4
In this work, we present a comparison of a shallow and a deep learning architecture for the automated segmentation of white matter lesions in MR images of multiple sclerosis patien…