  All datasets contain some biases, often unintentional, due to how they were
acquired and annotated. These biases distort machine-learning models'
performance, creating spurious correlations that the models can unfairly
exploit, or, contrarily destroying clear correlations that the models could
learn. With the popularity of deep learning models, automated skin lesion
analysis is starting to play an essential role in the early detection of
Melanoma. The ISIC Archive is one of the most used skin lesion sources to
benchmark deep learning-based tools. Bissoto et al. experimented with different
bounding-box based masks and showed that deep learning models could classify
skin lesion images without clinically meaningful information in the input data.
Their findings seem confounding since the ablated regions (random rectangular
boxes) are not significant. The shape of the lesion is a crucial factor in the
clinical characterization of a skin lesion. In that context, we performed a set
of experiments that generate shape-preserving masks instead of rectangular
bounding-box based masks. A deep learning model trained on these
shape-preserving masked images does not outperform models trained on images
without clinically meaningful information. That strongly suggests spurious
correlations guiding the models. We propose use of general adversarial network
(GAN) to mitigate the underlying bias.
