Towards Evaluating Explanations of Vision Transformers for Medical Imaging
This work addresses the need for transparent AI in critical domains like medical imaging, but it is incremental as it focuses on evaluating existing methods rather than introducing new ones.
The paper tackled the problem of evaluating explanation methods for Vision Transformers in medical imaging by assessing faithfulness, sensitivity, and complexity on chest X-ray classification, finding that Layerwise relevance propagation outperformed other methods in accuracy and reliability.
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.