Related papers: Evaluating the Robustness of Self-Supervised Learning in Medical Imaging

Evaluating the Robustness of Self-Supervised Learning in Medical Imaging

URL: http://arxiv.org/abs/2105.06986v1
Date: Fri, 14 May 2021 17:49:52 GMT
Title: Evaluating the Robustness of Self-Supervised Learning in Medical Imaging
Authors: Fernando Navarro, Christopher Watanabe, Suprosanna Shit, Anjany Sekuboyina, Jan C. Peeken, Stephanie E. Combs and Bjoern H. Menze
Abstract summary: Self-supervision has demonstrated to be an effective learning strategy when training target tasks on small annotated data-sets. We show that networks trained via self-supervised learning have superior robustness and generalizability compared to fully-supervised learning in the context of medical imaging.
Score: 57.20012795524752
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Self-supervision has demonstrated to be an effective learning strategy when training target tasks on small annotated data-sets. While current research focuses on creating novel pretext tasks to learn meaningful and reusable representations for the target task, these efforts obtain marginal performance gains compared to fully-supervised learning. Meanwhile, little attention has been given to study the robustness of networks trained in a self-supervised manner. In this work, we demonstrate that networks trained via self-supervised learning have superior robustness and generalizability compared to fully-supervised learning in the context of medical imaging. Our experiments on pneumonia detection in X-rays and multi-organ segmentation in CT yield consistent results exposing the hidden benefits of self-supervision for learning robust feature representations.

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