A Few-shot learning approach for Monkeypox recognition from a cross-domain perspective

Abstract

Monkeypox is a zoonotic infectious skin disease initially endemic in Africa only. However, some countries are now beginning to report cases of apparent community transmission. In Computer Aided Diagnosis, deep learning has gained substantial improvement over traditional methods. Commonly, training a supervised deep model requires a large number of labeled samples. However, the collection and annotation of new disease images such as human monkeypox are time-consuming and expensive. Thus, we introduce a few-shot learning based approach for the recognition of human monkeypox in images. It requires merely a small number of training samples. In particular, it is a novel framework built with a normal backbone and auxiliary backbones. They are co-trained with Self-supervised Learning and Cross-domain Adaption techniques. The self-supervision penalty is used to help the auxiliary backbones effectively learn priors from source domain. The combined features across different domains are unified through a power transform layer. Extensive experiments are conducted on a task of recognizing chickenpox, measles, and human monkeypox diseases in a three-way few-shot manner. The results demonstrate that our method outperforms mainstream few-shot learning algorithms such as meta-learning based and fine-tuning based methods.