Ensuring medical AI safety: interpretability-driven detection and mitigation of spurious model behavior and associated data

Deep neural networks are increasingly employed in high-stakes medical applications, despite their tendency for shortcut learning in the presence of spurious correlations, which can have potentially fatal consequences in practice. Whereas a multitude of works address either the detection or mitigatio...

Ausführliche Beschreibung