8 citations · 13 across the 11 of their papers we have counts for
8 papers · 1 filter
Deep Structural Causal Shape Models
Rajat Rasal, Daniel C. Castro, Nick Pawlowski +1
Causal reasoning provides a language to ask important interventional and counterfactual questions beyond purely statistical association. In medical imaging, for example, we may wan…
Evaluation of 3D GANs for Lung Tissue Modelling in Pulmonary CT
Sam Ellis, Octavio E. Martinez Manzanera, Vasileios Baltatzis +6
GANs are able to model accurately the distribution of complex, high-dimensional datasets, e.g. images. This makes high-quality GANs useful for unsupervised anomaly detection in med…
Automatic lesion analysis for increased efficiency in outcome prediction of traumatic brain injury
Margherita Rosnati, Eyal Soreq, Miguel Monteiro +8
The accurate prognosis for traumatic brain injury (TBI) patients is difficult yet essential to inform therapy, patient management, and long-term after-care. Patient characteristics…
Estimating Model Performance under Domain Shifts with Class-Specific Confidence Scores
Zeju Li, Konstantinos Kamnitsas, Mobarakol Islam +2
Machine learning models are typically deployed in a test setting that differs from the training setting, potentially leading to decreased model performance because of domain shift.…
Vector Quantisation for Robust Segmentation
Ainkaran Santhirasekaram, Avinash Kori, Mathias Winkler +2
The reliability of segmentation models in the medical domain depends on the model's robustness to perturbations in the input space. Robustness is a particular challenge in medical…
GLANCE: Global to Local Architecture-Neutral Concept-based Explanations
Avinash Kori, Ben Glocker, Francesca Toni
Most of the current explainability techniques focus on capturing the importance of features in input space. However, given the complexity of models and data-generating processes, t…