  This paper addresses the problem of few-shot skin disease classification by
introducing a novel approach called the Sub-Cluster-Aware Network (SCAN) that
enhances accuracy in diagnosing rare skin diseases. The key insight motivating
the design of SCAN is the observation that skin disease images within a class
often exhibit multiple sub-clusters, characterized by distinct variations in
appearance. To improve the performance of few-shot learning, we focus on
learning a high-quality feature encoder that captures the unique sub-clustered
representations within each disease class, enabling better characterization of
feature distributions. Specifically, SCAN follows a dual-branch framework,
where the first branch learns class-wise features to distinguish different skin
diseases, and the second branch aims to learn features which can effectively
partition each class into several groups so as to preserve the sub-clustered
structure within each class. To achieve the objective of the second branch, we
present a cluster loss to learn image similarities via unsupervised clustering.
To ensure that the samples in each sub-cluster are from the same class, we
further design a purity loss to refine the unsupervised clustering results. We
evaluate the proposed approach on two public datasets for few-shot skin disease
classification. The experimental results validate that our framework
outperforms the state-of-the-art methods by around 2% to 5% in terms of
sensitivity, specificity, accuracy, and F1-score on the SD-198 and Derm7pt
datasets.
