  Meta-training has been empirically demonstrated to be the most effective
pre-training method for few-shot learning of medical image classifiers (i.e.,
classifiers modeled with small training sets). However, the effectiveness of
meta-training relies on the availability of a reasonable number of
hand-designed classification tasks, which are costly to obtain, and
consequently rarely available. In this paper, we propose a new method to
unsupervisedly design a large number of classification tasks to meta-train
medical image classifiers. We evaluate our method on a breast dynamically
contrast enhanced magnetic resonance imaging (DCE-MRI) data set that has been
used to benchmark few-shot training methods of medical image classifiers. Our
results show that the proposed unsupervised task design to meta-train medical
image classifiers builds a pre-trained model that, after fine-tuning, produces
better classification results than other unsupervised and supervised
pre-training methods, and competitive results with respect to meta-training
that relies on hand-designed classification tasks.
