# Robustness, Stability, and Fidelity of Explanations for a Deep Skin Cancer Classification Model
## 1 Introduction
On the one hand, physicians, who do not trust the technology, will not use it. On the other hand, blind trust in the technology can lead to medical error. Explainable AI promises a solution to these problems: provide explanations to increase trust and informed decision-making; and give reasons/a glass-box for the AI's decisions, instead of condoning black-box decisions. More specifically, explainable AI (XAI), sometimes also called interpretable machine learning (IML), is an emerging research direction concerned with helping the user or developer of complex machine learning models to understand the model's underlying decision process, and why these models behave the way they do [15, 16, 17, 18, 19]. XAI/IML techniques can be divided into global and local ones. Global interpretation methods provide explanations for the whole dataset, while the latter provide explanations for specific instances. Because we rely on the automatic feature extraction by CNNs, we cannot use intrinsically interpretable classification models that give us model-specific global explanations (such as random forest, decision tree, or logistic regression). Moreover, the local ones are more useful for our case, where we want to provide the patient with classification results and explanations for his/her specific lesion images. Thus, to address the dermatology AI interpretability issue, we compare two currently popular local XAI/IML techniques for images; one gradient- and one perturbation-based method:  
* Integrated gradients [20], which calculate feature attributions to the prediction by accumulating gradients along a path from a baseline instance to the specific instance of interest. * Local interpretable model-agnostic explanations [21], which build an interpretable surrogate model around the decision space of the CNN model's prediction in the local neighbourhood of the specific instance of interest. To compare the explanations quantitatively, we compute their performance with regard to three metrics: robustness, stability, and fidelity. Moreover, we provide the visual explanations for the \"most interesting\" [22; 23] explanation cases: those, which the CNN classifier classifies correctly and incorrectly with the highest probability. As pointed out in a recent review of explanation techniques for the medical domain [19], new XAI/IML are introduced constantly, but metrics and comparison studies are needed to assess and validate these techniques. To address this research gap, our main contribution is the qualitative and quantitative comparison of two popular explanation techniques for a deep CNN model. Although some skin cancer classification studies used visualizations to explain a few local classifications of their CNN models (e.g., [2; 5; 24]), to our knowledge, no study exists that quantitatively compares such explanations through metrics. Thus, our focus lies on the quality of the XAI/IML techniques that create such visualizations. In comparison to related work, we quantitatively and qualitatively compare the outcomes of different explanation techniques for the same model and the same classifications, while related work only showed a few local explanations/visualizations of randomly (i.e., with no reported rule) picked instances. The remainder of this paper is structured as follows. Section 2 describes the HAM1000 data and used methods. More specifically, we explicate the deep learning models' performance-interpretability trade-off, and how XAI/IML techniques work to address this trade-off. We also depict the quantitative metrics that we used to compare the explanation techniques. Section 3 presents the experimental results. Section 4 concludes our analysis, and discusses limitations and future work.