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Found 8 papers, 1 papers with code
Reading Race: AI Recognises Patient's Racial Identity In Medical Images
Methods: Using private and public datasets we evaluate: A) performance quantification of deep learning models to detect race from medical images, including the ability of these models to generalize to external environments and across multiple imaging modalities, B) assessment of possible confounding anatomic and phenotype population features, such as disease distribution and body habitus as predictors of race, and C) investigation into the underlying mechanism by which AI models can recognize race.
Hidden Stratification Causes Clinically Meaningful Failures in Machine Learning for Medical Imaging
Machine learning models for medical image analysis often suffer from poor performance on important subsets of a population that are not identified during training or testing.
Exploring large scale public medical image datasets
The MURA labels were more accurate, but the original normal/abnormal labels were inaccurate for the subset of cases with degenerative joint disease, with a sensitivity of 60% and a specificity of 82%.
Towards generative adversarial networks as a new paradigm for radiology education
Medical students and radiology trainees typically view thousands of images in order to "train their eye" to detect the subtle visual patterns necessary for diagnosis.
Deep Learning Predicts Hip Fracture using Confounding Patient and Healthcare Variables
In this study, we trained deep learning models on 17, 587 radiographs to classify fracture, five patient traits, and 14 hospital process variables.
Producing radiologist-quality reports for interpretable artificial intelligence
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.
Detecting hip fractures with radiologist-level performance using deep neural networks
We developed an automated deep learning system to detect hip fractures from frontal pelvic x-rays, an important and common radiological task.
Automated 5-year Mortality Prediction using Deep Learning and Radiomics Features from Chest Computed Tomography
We propose new methods for the prediction of 5-year mortality in elderly individuals using chest computed tomography (CT).
