608 citations · 612 across the 6 of their papers we have counts for
8 papers
Invariant Risk Minimisation for Cross-Organism Inference: Substituting Mouse Data for Human Data in Human Risk Factor Discovery
Odhran O'Donoghue, Paul Duckworth, Giuseppe Ughi +10
Human medical data can be challenging to obtain due to data privacy concerns, difficulties conducting certain types of experiments, or prohibitive associated costs. In many setting…
Can non-specialists provide high quality gold standard labels in challenging modalities?
Samuel Budd, Thomas Day, John Simpson +5
Probably yes. -- Supervised Deep Learning dominates performance scores for many computer vision tasks and defines the state-of-the-art. However, medical image analysis lags behind…
Detecting Hypo-plastic Left Heart Syndrome in Fetal Ultrasound via Disease-specific Atlas Maps
Samuel Budd, Matthew Sinclair, Thomas Day +11
Fetal ultrasound screening during pregnancy plays a vital role in the early detection of fetal malformations which have potential long-term health impacts. The level of skill requi…
Surface Agnostic Metrics for Cortical Volume Segmentation and Regression
Samuel Budd, Prachi Patkee, Ana Baburamani +3
The cerebral cortex performs higher-order brain functions and is thus implicated in a range of cognitive disorders. Current analysis of cortical variation is typically performed by…
3D Probabilistic Segmentation and Volumetry from 2D projection images
Athanasios Vlontzos, Samuel Budd, Benjamin Hou +2
X-Ray imaging is quick, cheap and useful for front-line care assessment and intra-operative real-time imaging (e.g., C-Arm Fluoroscopy). However, it suffers from projective informa…
A Survey on Active Learning and Human-in-the-Loop Deep Learning for Medical Image Analysis
Samuel Budd, Emma C Robinson, Bernhard Kainz
Fully automatic deep learning has become the state-of-the-art technique for many tasks including image acquisition, analysis and interpretation, and for the extraction of clinicall…