10 citations · 20 across the 6 of their papers we have counts for
10 papers
Causal Machine Learning for Healthcare and Precision Medicine
Pedro Sanchez, Jeremy P. Voisey, Tian Xia +3
Causal machine learning (CML) has experienced increasing popularity in healthcare. Beyond the inherent capabilities of adding domain knowledge into learning systems, CML provides a…
Indication as Prior Knowledge for Multimodal Disease Classification in Chest Radiographs with Transformers
Grzegorz Jacenków, Alison Q. O'Neil, Sotirios A. Tsaftaris
When a clinician refers a patient for an imaging exam, they include the reason (e.g. relevant patient history, suspected disease) in the scan request; this appears as the indicatio…
Controllable cardiac synthesis via disentangled anatomy arithmetic
Spyridon Thermos, Xiao Liu, Alison O'Neil +1
Acquiring annotated data at scale with rare diseases or conditions remains a challenge. It would be extremely useful to have a method that controllably synthesizes images that can…
Semi-supervised Meta-learning with Disentanglement for Domain-generalised Medical Image Segmentation
Xiao Liu, Spyridon Thermos, Alison O'Neil +1
Generalising deep models to new data from new centres (termed here domains) remains a challenge. This is largely attributed to shifts in data statistics (domain shifts) between sou…
Disentangled Representations for Domain-generalized Cardiac Segmentation
Xiao Liu, Spyridon Thermos, Agisilaos Chartsias +2
Robust cardiac image segmentation is still an open challenge due to the inability of the existing methods to achieve satisfactory performance on unseen data of different domains. S…
INSIDE: Steering Spatial Attention with Non-Imaging Information in CNNs
Grzegorz Jacenków, Alison Q. O'Neil, Brian Mohr +1
We consider the problem of integrating non-imaging information into segmentation networks to improve performance. Conditioning layers such as FiLM provide the means to selectively…