13 citations · 16 across the 5 of their papers we have counts for
5 papers
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…
Attention-based Interpretable Regression of Gene Expression in Histology
Mara Graziani, Niccolò Marini, Nicolas Deutschmann +3
Interpretability of deep learning is widely used to evaluate the reliability of medical imaging models and reduce the risks of inaccurate patient recommendations. For models exceed…