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.

本文研究了在医学影像学领域中，Vision Transformer (ViT) 解释方法的表现，证明了 Transformer 的逐层相关传播法胜过本地可解释的模型不可知性解释和注意力可视化方法，在准确和可靠地表示 ViT 已经学到了什么方面提供了更好的表现。

面向医学成像的视觉 Transformer 解释评价

Towards Evaluating Explanations of Vision Transformers for Medical  Imaging

In socio-technical settings, operators are increasingly assisted by decision
support systems. By employing these, important properties of socio-technical
systems such as self-adaptation and self-optimization are expected to improve
further. To be accepted by and engage efficiently with operators, decision
support systems need to be able to provide explanations regarding the reasoning
behind specific decisions. In this paper, we propose the usage of Learning
Classifier Systems, a family of rule-based machine learning methods, to
facilitate transparent decision making and highlight some techniques to improve
that. We then present a template of seven questions to assess
application-specific explainability needs and demonstrate their usage in an
interview-based case study for a manufacturing scenario. We find that the
answers received did yield useful insights for a well-designed LCS model and
requirements to have stakeholders actively engage with an intelligent agent.

本文提出应用基于规则的机器学习方法 —— 学习分类器系统来促进透明决策，并介绍了一些提高它的技巧。我们还提出了七个问题的模板来评估特定应用领域的可解释性需求，并在制造情景的面试案例中演示了其用法。结果表明这些答案为良好设计的 LCS 模型提供了有用的见解，并要求利益相关者积极与智能代理进行互动。