Enhancing reconstruction-based out-of-distribution detection in brain MRI with model and metric ensembles

Our study shows that a reconstruction-based autoencoder, when combined with appropriate metrics, enhances OOD detection in brain MRI. These findings emphasize the importance of carefully selecting metrics and model configurations and highlight the need for tailored approaches, as standard deep learning approaches do not always align with the unique challenges of OOD detection. Improving OOD detection can increase the reliability of automated medical image analysis.