# Robustness, Stability, and Fidelity of Explanations for a Deep Skin Cancer Classification Model
## 1 Introduction  
Skin cancer is one of the most prevalent cancer types [1, 2]. The Center for Disease Control and Prevention estimates that there are 44 million visits to dermatologists every year, with skin lesions being one of the primary reasons for these visits [3]. Automating some of the tasks dermatologists work with, would not only bring a relief to the rising workload dermatologists struggle with but also make regular assessments easier and more affordable to a large number of patients. In recent years, advances in computer vision techniques and deep neural networks have yielded models that can automatically classify skin cancer. More specifically, the ability of convolutional neural networks (CNNs) to learn features have been noted also in the medical image analysis domain [4], and the dermatology subfield [1, 2, 5, 6]. As a whole, CNNs have become a widely used and \"state-of-art\" technique when developing algorithms for medical image classification (including dermatology) tasks. To our knowledge, in 2019, [PERSON] et al. [7] reported, for the first time, an on par skin cancer classification performance of CNN with dermatologists. Since then, scholars have increasingly published studies of automatic skin lesion classification models outperforming human domain experts/ dermatologists [8]. In fact, a recent survey by [PERSON] et al. [9] reports that in all their reviewed works, AI showed superior or at least equivalent performance compared with clinicians. However, one of the main disadvantages of CNNs with their many layers and weights is that they are opaque. This means thatit is unclear why a CNN arrived at a certain decision, making it difficult to trust the models. Thus, before these models can be integrated into clinical practice, the interpretability gap needs to be filled [10]. As explained by [PERSON] et al. [11], there are three main reasons why interpretability matters, and these reasons mostly related to how well the AI system is performing in comparison to human decision-makers: First, if the human decision-maker is performing better than the AI system, interpretability is needed mostly as a debugging function (i.e., for establishing the reasons why and where the AI is not performing as expected). As summarized by [PERSON] et al. [12], CNNs can suffer from a variety of flaws, and it is important to detect these flaws. Secondly, if the human and the AI are more or less on par, the interpretability need mainly arises to convince users to have confidence and trust in the AI (e.g., by showing that the human domain expert would decide exactly as the AI system). Thirdly, if the AI outperforms the human domain expert, the interpretability can show or teach humans to become better (e.g., by highlighting the most important features one should pay attention to or providing general rules). In this study, we are interested in all three reasons. For our experiments, we use the well-known HAM10000 data [13], an established public dataset for benchmarking and training of dermatology tasks. This dataset contains more than 10,000 dermascopic images spread between seven different types of skin lesions. According to a 2020 paper by [PERSON] et al. [8], human domain expert classification performance for this dataset is about 64%, while current CNNs clearly outperform the human experts. This means that the interpretability of such an AI system/CNN model might actually teach humans tricks or rules helping them to make better decisions/classifications of skin lesions. However, we also want to make sure that the reasons why these models make particular decisions, make sense (e.g., that no unreasonable parts of the skin lesion images, such as hair, are utilized for the classification), and that humans (both domain experts as well as patients) have more arguments and justification to trust and confide in such AI systems. As pointed out by [PERSON] et al. [14], AI systems will only be able to provide real clinical benefit if the physicians using them can balance trust and skepticism.