activity
20162024
most citedUnsupervised domain adaptation in brain lesion segmentation with adversarial networks

8 citations · 27 across the 21 of their papers we have counts for

collaborators
Showing eess.IVShow all

5 papers · 1 filter

eess.IV2024

Quantifying the Impact of Population Shift Across Age and Sex for Abdominal Organ Segmentation

Kate Čevora, Ben Glocker, Wenjia Bai

Deep learning-based medical image segmentation has seen tremendous progress over the last decade, but there is still relatively little transfer into clinical practice. One of the m…

eess.IV20235 cited

Analysing race and sex bias in brain age prediction

Carolina Piçarra, Ben Glocker

Brain age prediction from MRI has become a popular imaging biomarker associated with a wide range of neuropathologies. The datasets used for training, however, are often skewed and…

eess.IV2023

Robustness Stress Testing in Medical Image Classification

Mobarakol Islam, Zeju Li, Ben Glocker

Deep neural networks have shown impressive performance for image-based disease detection. Performance is commonly evaluated through clinical validation on independent test sets to…

eess.IV20221 cited

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…

eess.IV2022

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…