# Robustness, Stability, and Fidelity of Explanations for a Deep Skin Cancer Classification Model
## 2 Material and Methods  
### Data  
We used the HAM10000 dataset, a large public collection of dermatoscopic images, for our experiments. This dataset can be downloaded from the International Skin Imaging Collaboration (ISIC). (See [[https://www.isic-archive.com/](https://www.isic-archive.com/)]([https://www.isic-archive.com/](https://www.isic-archive.com/)), accessed on 10 August 2022). It consists of 10,050 dermoscopic images belonging to seven different classes. More specifically, 6705 images belong to the melanocytic nevi (nv) class, 1113 belong to the melanoma (mel) class, 1099 belong to the benign keratosis-like lesions (bkl) class, 514 belong to the basal cell carcinoma (bcc) class, 327 belong to the actinic keratoses (akiec) class, 142 belong to the vascular lesions (vasc) class, and 115 images belong to the dermatofibroma (df) class. Figure 1 shows five randomly picked examples of each of these classes.  
These 10,015 dermoscopic images were collected over a time period of 20 years from the department of dermatology at the Medical University of Viena, Austria, and the skin cancer practice of [PERSON] in Queensland, Australia (see [13] for a detailed description of this dataset). Since then, they have become a widely used dataset for dermatology benchmarking and training.  
As mentioned in the introduction, human domain expert classification performance for this dataset is about 64% [8], while current ML models mostly outperform the human experts in classification accuracy. In a recent article, [PERSON] et al. [1] compared several popular deep learning architectures for this dataset and reported the best performance for EfficientNetB0 with an accuracy of 62.1% (see Table 13 in [1]). [PERSON] et al. [2] reported a higher accuracy (72.1%), but they used external data to augment the dataset and the 72.1% is only for the classification into three types (i.e., benign, malignant, and neoplastic). [PERSON] et al. [8] used a 34-layer residual network and achieved an accuracy of 80.3% on the classification into the seven classes in the data (i.e., askiec, bcc, bkl, df, mel, nv, and vasc). This is similar to the accuracy we achieve with a significantly simpler model (see Section 3), and a result that outperforms human expert classifications and that ranks in the top quartile of all ML models developed for the HAM10000 dataset [8].  
A full overview of related work using the ISIC data and skin cancer classification models is out of scope for this article. Moreover, it would shift its focus which lies on the explanations of the classifications. A plethora of articles reviewing skin cancer classification models exist already. Thus, we refer the interested reader to one these overviews. For example, Table 1 by [PERSON] et al. [1] provides a very recent overview of research papers usingISIC data for skin cancer classification, [PERSON] et al. [25] survey approaches of integrating patient data into skin cancer CNN classification models, [PERSON] and [PERSON] [26] compare studies that use U-Net and attention-based methods for skin lesion image segmentation, and the study by [PERSON] and [PERSON] [6] includes a recent summary of papers using specifically the HAM10000 dataset for deep learning skin cancer classification models. In addition, articles have been published that highlight the increasing performance with transfer learning approaches [27], the significance of specific techniques for the multi-class classification [28], and the usefulness of CNN ensemble techniques [29].