  Rare diseases have extremely low-data regimes, unlike common diseases with
large amount of available labeled data. Hence, to train a neural network to
classify rare diseases with a few per-class data samples is very challenging,
and so far, catches very little attention. In this paper, we present a
difficulty-aware meta-learning method to address rare disease classifications
and demonstrate its capability to classify dermoscopy images. Our key approach
is to first train and construct a meta-learning model from data of common
diseases, then adapt the model to perform rare disease classification.To
achieve this, we develop the difficulty-aware meta-learning method that
dynamically monitors the importance of learning tasks during the
meta-optimization stage. To evaluate our method, we use the recent ISIC 2018
skin lesion classification dataset, and show that with only five samples per
class, our model can quickly adapt to classify unseen classes by a high AUC of
83.3%. Also, we evaluated several rare disease classification results in the
public Dermofit Image Library to demonstrate the potential of our method for
real clinical practice.
