1 code implementation • 15 Nov 2023 • Nataliia Molchanova, Vatsal Raina, Andrey Malinin, Francesco La Rosa, Adrien Depeursinge, Mark Gales, Cristina Granziera, Henning Muller, Mara Graziani, Meritxell Bach Cuadra
The results from a multi-centric MRI dataset of 172 patients demonstrate that our proposed measures more effectively capture model errors at the lesion and patient scales compared to measures that average voxel-scale uncertainty values.
no code implementations • 13 Jul 2023 • Mara Graziani, Laura O' Mahony, An-phi Nguyen, Henning Müller, Vincent Andrearczyk
By decomposing the latent space of a layer in singular vectors and refining them by unsupervised clustering, we uncover concept vectors aligned with directions of high variance that are relevant to the model prediction, and that point to semantically distinct concepts.
1 code implementation • 19 Apr 2023 • Laura O'Mahony, Vincent Andrearczyk, Henning Muller, Mara Graziani
Mechanistic interpretability aims to understand how models store representations by breaking down neural networks into interpretable units.
1 code implementation • 11 Apr 2023 • Omar S. M. El Nahhas, Chiara M. L. Loeffler, Zunamys I. Carrero, Marko van Treeck, Fiona R. Kolbinger, Katherine J. Hewitt, Hannah S. Muti, Mara Graziani, Qinghe Zeng, Julien Calderaro, Nadina Ortiz-Brüchle, Tanwei Yuan, Michael Hoffmeister, Hermann Brenner, Alexander Brobeil, Jorge S. Reis-Filho, Jakob Nikolas Kather
We tested our method for multiple clinically and biologically relevant biomarkers: homologous repair deficiency (HRD) score, a clinically used pan-cancer biomarker, as well as markers of key biological processes in the tumor microenvironment.
1 code implementation • 10 Feb 2023 • Vatsal Raina, Nataliia Molchanova, Mara Graziani, Andrey Malinin, Henning Muller, Meritxell Bach Cuadra, Mark Gales
This work describes a detailed analysis of the recently proposed normalised Dice Similarity Coefficient (nDSC) for binary segmentation tasks as an adaptation of DSC which scales the precision at a fixed recall rate to tackle this bias.
1 code implementation • 9 Nov 2022 • Nataliia Molchanova, Vatsal Raina, Andrey Malinin, Francesco La Rosa, Henning Muller, Mark Gales, Cristina Granziera, Mara Graziani, Meritxell Bach Cuadra
This paper focuses on the uncertainty estimation for white matter lesions (WML) segmentation in magnetic resonance imaging (MRI).
1 code implementation • 29 Aug 2022 • Mara Graziani, Niccolò Marini, Nicolas Deutschmann, Nikita Janakarajan, Henning Müller, María Rodríguez Martínez
Interpretability of deep learning is widely used to evaluate the reliability of medical imaging models and reduce the risks of inaccurate patient recommendations.
2 code implementations • 30 Jun 2022 • Andrey Malinin, Andreas Athanasopoulos, Muhamed Barakovic, Meritxell Bach Cuadra, Mark J. F. Gales, Cristina Granziera, Mara Graziani, Nikolay Kartashev, Konstantinos Kyriakopoulos, Po-Jui Lu, Nataliia Molchanova, Antonis Nikitakis, Vatsal Raina, Francesco La Rosa, Eli Sivena, Vasileios Tsarsitalidis, Efi Tsompopoulou, Elena Volf
This creates a need to be able to assess how robustly ML models generalize as well as the quality of their uncertainty estimates.
1 code implementation • 4 Aug 2020 • Mara Graziani, Sebastian Otalora, Stephane Marchand-Maillet, Henning Muller, Vincent Andrearczyk
Here we show that our architecture, by learning end-to-end an uncertainty-based weighting combination of multi-task and adversarial losses, is encouraged to focus on pathology features such as density and pleomorphism of nuclei, e. g. variations in size and appearance, while discarding misleading features such as staining differences.
1 code implementation • 9 Apr 2019 • Mara Graziani, Vincent Andrearczyk, Henning Müller
Explanations for deep neural network predictions in terms of domain-related concepts can be valuable in medical applications, where justifications are important for confidence in the decision-making.
no code implementations • 18 Nov 2015 • Mara Graziani
The electromyography (EMG) signal is the electrical manifestation of a neuromuscular activation that provides access to physiological processes which cause the muscle to generate force and produce movement.