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arXiv · 2106.09229

An Evaluation of Self-Supervised Pre-Training for Skin-Lesion Analysis

Levy Chaves·Alceu Bissoto·Eduardo Valle·Sandra Avila
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Abstract

Self-supervised pre-training appears as an advantageous alternative to supervised pre-trained for transfer learning. By synthesizing annotations on pretext tasks, self-supervision allows to pre-train models on large amounts of pseudo-labels before fine-tuning them on the target task. In this work, we assess self-supervision for the diagnosis of skin lesions, comparing three self-supervised pipelines to a challenging supervised baseline, on five test datasets comprising in- and out-of-distribution samples. Our results show that self-supervision is competitive both in improving accuracies and in reducing the variability of outcomes. Self-supervision proves particularly useful for low training data scenarios ($<1\,500$ and $<150$ samples), where its ability to stabilize the outcomes is essential to provide sound results.

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Levy Chaves, Alceu Bissoto, Eduardo Valle, Sandra Avila. 2021-06-17. An Evaluation of Self-Supervised Pre-Training for Skin-Lesion Analysis. https://arxiv.org/abs/2106.09229

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