  State-of-the-art deep learning approaches for skin lesion recognition often
require pretraining on larger and more varied datasets, to overcome the
generalization limitations derived from the reduced size of the skin lesion
imaging datasets. ImageNet is often used as the pretraining dataset, but its
transferring potential is hindered by the domain gap between the source dataset
and the target dermatoscopic scenario. In this work, we introduce a novel
pretraining approach that sequentially trains a series of Self-Supervised
Learning pretext tasks and only requires the unlabeled skin lesion imaging
data. We present a simple methodology to establish an ordering that defines a
pretext task curriculum. For the multi-class skin lesion classification
problem, and ISIC-2019 dataset, we provide experimental evidence showing that:
i) a model pretrained by a curriculum of pretext tasks outperforms models
pretrained by individual pretext tasks, and ii) a model pretrained by the
optimal pretext task curriculum outperforms a model pretrained on ImageNet. We
demonstrate that this performance gain is related to the fact that the
curriculum of pretext tasks better focuses the attention of the final model on
the skin lesion. Beyond performance improvement, this strategy allows for a
large reduction in the training time with respect to ImageNet pretraining,
which is especially advantageous for network architectures tailored for a
specific problem.
