9 papers
Local Label-Informed Feature Transfer for Generating Ground-Truth Medical Images: A Comparison of GAN- and Diffusion-Based Approaches
Rick Wilming, Irem Ozseker, Luca Matteo Cornils +4
Validating Explainable Artificial Intelligence (XAI) methods in medical imaging requires ground-truth data with known locations of informative features. However, current approaches…
Multi-Depth Concept Extraction for Post-Hoc Vision Encoder Explanation
Ahcène Boubekki, Samuel G. Fadel, Sebastian Mair
Explainable AI methods for vision models aim to identify the parts of the input that are important for the final prediction and subsequently relate these regions to human-understan…
Explainable AI needs formalization
Stefan Haufe, Rick Wilming, Benedict Clark +4
The field of "explainable artificial intelligence" (XAI) seemingly addresses the desire that decisions of machine learning systems should be human-understandable. However, in its c…
Post-hoc Self-explanation of CNNs
Ahcène Boubekki, Line H. Clemmensen
Although standard Convolutional Neural Networks (CNNs) can be mathematically reinterpreted as Self-Explainable Models (SEMs), their built-in prototypes do not on their own accurate…
Explaining deep learning for ECG using time-localized clusters
Ahcène Boubekki, Konstantinos Patlatzoglou, Joseph Barker +2
Deep learning has significantly advanced electrocardiogram (ECG) analysis, enabling automatic annotation, disease screening, and prognosis beyond traditional clinical capabilities.…
WiseLVAM: A Novel Framework For Left Ventricle Automatic Measurements
Durgesh Kumar Singh, Qing Cao, Sarina Thomas +3
Clinical guidelines recommend performing left ventricular (LV) linear measurements in B-mode echocardiographic images at the basal level -- typically at the mitral valve leaflet ti…