6 papers
Detecting labeling bias using influence functions
Frida Jørgensen, Nina Weng, Siavash Bigdeli
Labeling bias arises during data collection due to resource limitations or unconscious bias, leading to unequal label error rates across subgroups or misrepresentation of subgroup…
Weight Space Correlation Analysis: Quantifying Feature Utilization in Deep Learning Models
Chun Kit Wong, Paraskevas Pegios, Nina Weng +4
Deep learning models in medical imaging are susceptible to shortcut learning, relying on confounding metadata (e.g., scanner model) that is often encoded in image embeddings. The c…
Patronus: Interpretable Diffusion Models with Prototypes
Nina Weng, Aasa Feragen, Siavash Bigdeli
Uncovering the opacity of diffusion-based generative models is urgently needed, as their applications continue to expand while their underlying procedures largely remain a black bo…
Diffusion-based Iterative Counterfactual Explanations for Fetal Ultrasound Image Quality Assessment
Paraskevas Pegios, Manxi Lin, Nina Weng +6
Obstetric ultrasound image quality is crucial for accurate diagnosis and monitoring of fetal health. However, acquiring high-quality standard planes is difficult, influenced by the…
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…
Slicing Through Bias: Explaining Performance Gaps in Medical Image Analysis using Slice Discovery Methods
Vincent Olesen, Nina Weng, Aasa Feragen +1
Machine learning models have achieved high overall accuracy in medical image analysis. However, performance disparities on specific patient groups pose challenges to their clinical…