# Unsupervised Task Design to Meta-Train Medical Image Classifiers

## TL;DR

This paper introduces an unsupervised method for designing classification tasks to improve meta-training of medical image classifiers, reducing reliance on costly hand-designed tasks and achieving competitive results.

## Contribution

It presents a novel unsupervised task design approach for meta-training medical image classifiers, eliminating the need for hand-crafted tasks.

## Key findings

- Outperforms other unsupervised pre-training methods.
- Achieves results comparable to meta-training with hand-designed tasks.
- Improves classification accuracy after fine-tuning.

## Abstract

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.