  As deep learning models increasingly find applications in critical domains
such as medical imaging, the need for transparent and trustworthy
decision-making becomes paramount. Many explainability methods provide insights
into how these models make predictions by attributing importance to input
features. As Vision Transformer (ViT) becomes a promising alternative to
convolutional neural networks for image classification, its interpretability
remains an open research question. This paper investigates the performance of
various interpretation methods on a ViT applied to classify chest X-ray images.
We introduce the notion of evaluating faithfulness, sensitivity, and complexity
of ViT explanations. The obtained results indicate that Layerwise relevance
propagation for transformers outperforms Local interpretable model-agnostic
explanations and Attention visualization, providing a more accurate and
reliable representation of what a ViT has actually learned. Our findings
provide insights into the applicability of ViT explanations in medical imaging
and highlight the importance of using appropriate evaluation criteria for
comparing them.
