  We consider the problem of image classification for the purpose of aiding
doctors in dermatological diagnosis. Dermatological diagnosis poses two major
challenges for standard off-the-shelf techniques: First, the data distribution
is typically extremely long tailed. Second, intra-class variability is often
large. To address the first issue, we formulate the problem as low-shot
learning, where once deployed, a base classifier must rapidly generalize to
diagnose novel conditions given very few labeled examples. To model diverse
classes effectively, we propose Prototypical Clustering Networks (PCN), an
extension to Prototypical Networks that learns a mixture of prototypes for each
class. Prototypes are initialized for each class via clustering and refined via
an online update scheme. Classification is performed by measuring similarity to
a weighted combination of prototypes within a class, where the weights are the
inferred cluster responsibilities. We demonstrate the strengths of our approach
in effective diagnosis on a realistic dataset of dermatological conditions.
