  Annotated images and ground truth for the diagnosis of rare and novel
diseases are scarce. This is expected to prevail, considering the small number
of affected patient population and limited clinical expertise to annotate
images. Further, the frequently occurring long-tailed class distributions in
skin lesion and other disease classification datasets cause conventional
training approaches to lead to poor generalization due to biased class priors.
Few-shot learning, and meta-learning in general, aim to overcome these issues
by aiming to perform well in low data regimes. This paper focuses on improving
meta-learning for the classification of dermoscopic images. Specifically, we
propose a baseline supervised method on the meta-training set that allows a
network to learn highly representative and generalizable feature embeddings for
images, that are readily transferable to new few-shot learning tasks. We follow
some of the previous work in literature that posit that a representative
feature embedding can be more effective than complex meta-learning algorithms.
We empirically prove the efficacy of the proposed meta-training method on
dermoscopic images for learning embeddings, and show that even simple linear
classifiers trained atop these representations suffice to outperform some of
the usual meta-learning methods.
