11 citations · 14 across the 11 of their papers we have counts for
15 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…
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…
SuperCM: Improving Semi-Supervised Learning and Domain Adaptation through differentiable clustering
Durgesh Singh, Ahcène Boubekki, Robert Jenssen +1
Semi-Supervised Learning (SSL) and Unsupervised Domain Adaptation (UDA) enhance the model performance by exploiting information from labeled and unlabeled data. The clustering assu…
Supercm: Revisiting Clustering for Semi-Supervised Learning
Durgesh Singh, Ahcene Boubekki, Robert Jenssen +1
The development of semi-supervised learning (SSL) has in recent years largely focused on the development of new consistency regularization or entropy minimization approaches, often…
EnLVAM: Enhanced Left Ventricle Linear Measurements Utilizing Anatomical Motion Mode
Durgesh K. Singh, Ahcene Boubekki, Qing Cao +3
Linear measurements of the left ventricle (LV) in the Parasternal Long Axis (PLAX) view using B-mode echocardiography are crucial for cardiac assessment. These involve placing 4-6…
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…
Leveraging Activations for Superpixel Explanations
Ahcène Boubekki, Samuel G. Fadel, Sebastian Mair
Saliency methods have become standard in the explanation toolkit of deep neural networks. Recent developments specific to image classifiers have investigated region-based explanati…
EXACT: Towards a platform for empirically benchmarking Machine Learning model explanation methods
Benedict Clark, Rick Wilming, Artur Dox +11
The evolving landscape of explainable artificial intelligence (XAI) aims to improve the interpretability of intricate machine learning (ML) models, yet faces challenges in formalis…
Pantypes: Diverse Representatives for Self-Explainable Models
Rune Kjærsgaard, Ahcène Boubekki, Line Clemmensen
Prototypical self-explainable classifiers have emerged to meet the growing demand for interpretable AI systems. These classifiers are designed to incorporate high transparency in t…
Prototypical Self-Explainable Models Without Re-training
Srishti Gautam, Ahcene Boubekki, Marina M. C. Höhne +1
Explainable AI (XAI) has unfolded in two distinct research directions with, on the one hand, post-hoc methods that explain the predictions of a pre-trained black-box model and, on…
ProtoVAE: A Trustworthy Self-Explainable Prototypical Variational Model
Srishti Gautam, Ahcene Boubekki, Stine Hansen +4
The need for interpretable models has fostered the development of self-explainable classifiers. Prior approaches are either based on multi-stage optimization schemes, impacting the…
Joint Optimization of an Autoencoder for Clustering and Embedding
Ahcène Boubekki, Michael Kampffmeyer, Robert Jenssen +1
Deep embedded clustering has become a dominating approach to unsupervised categorization of objects with deep neural networks. The optimization of the most popular methods alternat…