4 papers
Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data
Frederik Pahde, Thomas Wiegand, Sebastian Lapuschkin +1
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…
Navigating Neural Space: Revisiting Concept Activation Vectors to Overcome Directional Divergence
Frederik Pahde, Maximilian Dreyer, Leander Weber +5
With a growing interest in understanding neural network prediction strategies, Concept Activation Vectors (CAVs) have emerged as a popular tool for modeling human-understandable co…
Post-Hoc Concept Disentanglement: From Correlated to Isolated Concept Representations
Eren Erogullari, Sebastian Lapuschkin, Wojciech Samek +1
Concept Activation Vectors (CAVs) are widely used to model human-understandable concepts as directions within the latent space of neural networks. They are trained by identifying d…
Synthetic Generation of Dermatoscopic Images with GAN and Closed-Form Factorization
Rohan Reddy Mekala, Frederik Pahde, Simon Baur +11
In the realm of dermatological diagnoses, where the analysis of dermatoscopic and microscopic skin lesion images is pivotal for the accurate and early detection of various medical…