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arXiv · 1907.07816

Unsupervised Task Design to Meta-Train Medical Image Classifiers

Gabriel Maicas·Cuong Nguyen·Farbod Motlagh·Jacinto C. Nascimento·Gustavo Carneiro
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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.

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Gabriel Maicas, Cuong Nguyen, Farbod Motlagh, Jacinto C. Nascimento, Gustavo Carneiro. 2019-07-17. Unsupervised Task Design to Meta-Train Medical Image Classifiers. https://arxiv.org/abs/1907.07816

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