39 citations · 95 across the 35 of their papers we have counts for
6 papers · 1 filter
Investigating Label Bias and Representational Sources of Age-Related Disparities in Medical Segmentation
Aditya Parikh, Sneha Das, Aasa Feragen
Algorithmic bias in medical imaging can perpetuate health disparities, yet its causes remain poorly understood in segmentation tasks. While fairness has been extensively studied in…
Are Sex-based Physiological Differences the Cause of Gender Bias for Chest X-ray Diagnosis?
Nina Weng, Siavash Bigdeli, Eike Petersen +1
While many studies have assessed the fairness of AI algorithms in the medical field, the causes of differences in prediction performance are often unknown. This lack of knowledge a…
An Automatic Guidance and Quality Assessment System for Doppler Imaging of Umbilical Artery
Chun Kit Wong, Manxi Lin, Alberto Raheli +5
Examination of the umbilical artery with Doppler ultrasonography is performed to investigate blood supply to the fetus through the umbilical cord, which is vital for the monitoring…
DTU-Net: Learning Topological Similarity for Curvilinear Structure Segmentation
Manxi Lin, Zahra Bashir, Martin Grønnebæk Tolsgaard +2
Curvilinear structure segmentation is important in medical imaging, quantifying structures such as vessels, airways, neurons, or organ boundaries in 2D slices. Segmentation via pix…
Feature robustness and sex differences in medical imaging: a case study in MRI-based Alzheimer's disease detection
Eike Petersen, Aasa Feragen, Maria Luise da Costa Zemsch +3
Convolutional neural networks have enabled significant improvements in medical image-based diagnosis. It is, however, increasingly clear that these models are susceptible to perfor…
Medical Imaging with Deep Learning: MIDL 2019 -- Extended Abstract Track
M. Jorge Cardoso, Aasa Feragen, Ben Glocker +4
This compendium gathers all the accepted extended abstracts from the Second International Conference on Medical Imaging with Deep Learning (MIDL 2019), held in London, UK, 8-10 Jul…