12 papers
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…
Who Does Your Algorithm Fail? Investigating Age and Ethnic Bias in the MAMA-MIA Dataset
Aditya Parikh, Sneha Das, Aasa Feragen
Deep learning models aim to improve diagnostic workflows, but fairness evaluation remains underexplored beyond classification, e.g., in image segmentation. Unaddressed segmentation…
Onto-Epistemological Analysis of AI Explanations
Martina Mattioli, Eike Petersen, Aasa Feragen +2
Artificial intelligence (AI) is being applied in almost every field. At the same time, the currently dominant deep learning methods are fundamentally black-box systems that lack ex…
In the Picture: Medical Imaging Datasets, Artifacts, and their Living Review
Amelia Jiménez-Sánchez, Natalia-Rozalia Avlona, Sarah de Boer +26
Datasets play a critical role in medical imaging research, yet issues such as label quality, shortcuts, and metadata are often overlooked. This lack of attention may harm the gener…
Bayesian generative models can flag performance loss, bias, and out-of-distribution image content
Miguel López-Pérez, Marco Miani, Valery Naranjo +2
Generative models are popular for medical imaging tasks such as anomaly detection, feature extraction, data visualization, or image generation. Since they are parameterized by deep…
Explainable fetal ultrasound quality assessment with progressive concept bottleneck models
Manxi Lin, Aasa Feragen, Kamil Mikolaj +3
The quality of fetal ultrasound screening scans directly influences the precision of biometric measurements. However, acquiring high-quality scans is labor-intensive and highly rel…