With radiology shortages affecting over half of the global population, the potential of artificial
intelligence to revolutionize medical diagnosis and treatment is ever more important …

Recently, interpretable machine learning has re-explored concept bottleneck models (CBM).
An advantage of this model class is the user's ability to intervene on predicted concept …

Recent advancements in post-hoc and inherently interpretable methods have markedly
enhanced the explanations of black box classifier models. These methods operate either …

With the widespread adoption of deep learning-based models in practical applications,
concerns about their fairness have become increasingly prominent. Existing research …

In this paper, we introduce an explainable algorithm designed from a multi-modal foundation
model, that performs fast and explainable image classification. Drawing inspiration from …

A text encoder within Vision-Language Models (VLMs) like CLIP plays a crucial role in
translating textual input into an embedding space shared with images, thereby facilitating …

As artificial intelligence systems become increasingly integrated into daily life, the field of
explainability has gained significant attention. This trend is particularly driven by the …

Deep learning models have revolutionized medical imaging and diagnostics, yet their
opaque nature poses challenges for clinical adoption and trust. Amongst approaches to …

Vision transformers (ViTs) have emerged as a significant area of focus, particularly for their
capacity to be jointly trained with large language models and to serve as robust vision …

A classifier may depend on incidental features stemming from a strong correlation between
the feature and the classification target in the training dataset. Recently, Last Layer …