1 citations · 3 across the 12 of their papers we have counts for
4 papers · 1 filter
Confidence is Not Reliability: Rethinking MC Dropout in Brain Tumour Segmentation
Xin Ci Wong, Duygu Sarikaya, Kieran Zucker +2
Glioma segmentation in multiparametric MRI is a critical component of treatment planning. A segmentation model that fails silently on treatment-critical sub-regions represents a pa…
Can Diffusion Models Bridge the Domain Gap in Cardiac MR Imaging?
Xin Ci Wong, Duygu Sarikaya, Kieran Zucker +2
Magnetic resonance (MR) imaging, including cardiac MR, is prone to domain shift due to variations in imaging devices and acquisition protocols. This challenge limits the deployment…
Learning disentangled representations for explainable chest X-ray classification using Dirichlet VAEs
Rachael Harkness, Alejandro F Frangi, Kieran Zucker +1
This study explores the use of the Dirichlet Variational Autoencoder (DirVAE) for learning disentangled latent representations of chest X-ray (CXR) images. Our working hypothesis i…
The pitfalls of using open data to develop deep learning solutions for COVID-19 detection in chest X-rays
Rachael Harkness, Geoff Hall, Alejandro F Frangi +2
Since the emergence of COVID-19, deep learning models have been developed to identify COVID-19 from chest X-rays. With little to no direct access to hospital data, the AI community…