108 citations · 199 across the 5 of their papers we have counts for
5 papers
Failure Detection in Medical Image Classification: A Reality Check and Benchmarking Testbed
Melanie Bernhardt, Fabio De Sousa Ribeiro, Ben Glocker
Failure detection in automated image classification is a critical safeguard for clinical deployment. Detected failure cases can be referred to human assessment, ensuring patient sa…
Potential sources of dataset bias complicate investigation of underdiagnosis by machine learning algorithms
Mélanie Bernhardt, Charles Jones, Ben Glocker
An increasing number of reports raise concerns about the risk that machine learning algorithms could amplify health disparities due to biases embedded in the training data. Seyyed-…
Algorithmic encoding of protected characteristics in image-based models for disease detection
Ben Glocker, Charles Jones, Melanie Bernhardt +1
It has been rightfully emphasized that the use of AI for clinical decision making could amplify health disparities. An algorithm may encode protected characteristics, and then use…
Active label cleaning for improved dataset quality under resource constraints
Melanie Bernhardt, Daniel C. Castro, Ryutaro Tanno +9
Imperfections in data annotation, known as label noise, are detrimental to the training of machine learning models and have an often-overlooked confounding effect on the assessment…
Training Variational Networks with Multi-Domain Simulations: Speed-of-Sound Image Reconstruction
Melanie Bernhardt, Valery Vishnevskiy, Richard Rau +1
Speed-of-sound has been shown as a potential biomarker for breast cancer imaging, successfully differentiating malignant tumors from benign ones. Speed-of-sound images can be recon…