18 citations · 47 across the 11 of their papers we have counts for
5 papers · 1 filter
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…
Uncovering Unique Concept Vectors through Latent Space Decomposition
Mara Graziani, Laura O' Mahony, An-Phi Nguyen +2
Interpreting the inner workings of deep learning models is crucial for establishing trust and ensuring model safety. Concept-based explanations have emerged as a superior approach…
Disentangling Neuron Representations with Concept Vectors
Laura O'Mahony, Vincent Andrearczyk, Henning Muller +1
Mechanistic interpretability aims to understand how models store representations by breaking down neural networks into interpretable units. However, the occurrence of polysemantic…
Regression-based Deep-Learning predicts molecular biomarkers from pathology slides
Omar S. M. El Nahhas, Chiara M. L. Loeffler, Zunamys I. Carrero +14
Deep Learning (DL) can predict biomarkers from cancer histopathology. Several clinically approved applications use this technology. Most approaches, however, predict categorical la…
Tackling Bias in the Dice Similarity Coefficient: Introducing nDSC for White Matter Lesion Segmentation
Vatsal Raina, Nataliia Molchanova, Mara Graziani +4
The development of automatic segmentation techniques for medical imaging tasks requires assessment metrics to fairly judge and rank such approaches on benchmarks. The Dice Similari…