  Melanoma is the deadliest form of skin cancer. Automated skin lesion analysis
plays an important role for early detection. Nowadays, the ISIC Archive and the
Atlas of Dermoscopy dataset are the most employed skin lesion sources to
benchmark deep-learning based tools. However, all datasets contain biases,
often unintentional, due to how they were acquired and annotated. Those biases
distort the performance of machine-learning models, creating spurious
correlations that the models can unfairly exploit, or, contrarily destroying
cogent correlations that the models could learn. In this paper, we propose a
set of experiments that reveal both types of biases, positive and negative, in
existing skin lesion datasets. Our results show that models can correctly
classify skin lesion images without clinically-meaningful information:
disturbingly, the machine-learning model learned over images where no
information about the lesion remains, presents an accuracy above the AI
benchmark curated with dermatologists' performances. That strongly suggests
spurious correlations guiding the models. We fed models with additional
clinically meaningful information, which failed to improve the results even
slightly, suggesting the destruction of cogent correlations. Our main findings
raise awareness of the limitations of models trained and evaluated in small
datasets such as the ones we evaluated, and may suggest future guidelines for
models intended for real-world deployment.
