568 citations · 768 across the 6 of their papers we have counts for
6 papers
Reading Race: AI Recognises Patient's Racial Identity In Medical Images
Imon Banerjee, Ananth Reddy Bhimireddy, John L. Burns +20
Background: In medical imaging, prior studies have demonstrated disparate AI performance by race, yet there is no known correlation for race on medical imaging that would be obviou…
Docs are ROCs: A simple off-the-shelf approach for estimating average human performance in diagnostic studies
Luke Oakden-Rayner, Lyle Palmer
Estimating average human performance has been performed inconsistently in research in diagnostic medicine. This has been particularly apparent in the field of medical artificial in…
Producing radiologist-quality reports for interpretable artificial intelligence
William Gale, Luke Oakden-Rayner, Gustavo Carneiro +2
Current approaches to explaining the decisions of deep learning systems for medical tasks have focused on visualising the elements that have contributed to each decision. We argue…
Detecting hip fractures with radiologist-level performance using deep neural networks
William Gale, Luke Oakden-Rayner, Gustavo Carneiro +2
We developed an automated deep learning system to detect hip fractures from frontal pelvic x-rays, an important and common radiological task. Our system was trained on a decade of…
A Bayesian Data Augmentation Approach for Learning Deep Models
Toan Tran, Trung Pham, Gustavo Carneiro +2
Data augmentation is an essential part of the training process applied to deep learning models. The motivation is that a robust training process for deep learning models depends on…
Automated 5-year Mortality Prediction using Deep Learning and Radiomics Features from Chest Computed Tomography
Gustavo Carneiro, Luke Oakden-Rayner, Andrew P. Bradley +2
We propose new methods for the prediction of 5-year mortality in elderly individuals using chest computed tomography (CT). The methods consist of a classifier that performs this pr…