Unsupervised pre-training has been proven as an effective approach to boost various downstream tasks given limited labeled data. Among various methods, contrastive learning learns a discriminative representation by constructing positive and negative pairs. However, it is not trivial to build reasonable pairs for a segmentation task in an unsupervised way. In this work, we propose a novel unsupervised pre-training framework that avoids the drawback of contrastive learning. Our framework consists of two principles: unsupervised over-segmentation as a pre-train task using mutual information maximization and boundary-aware preserving learning. Experimental results on two benchmark medical segmentation datasets reveal our method's effectiveness in improving segmentation performance when few annotated images are available.
@article{arxiv.2202.02371,
title = {Boundary-aware Information Maximization for Self-supervised Medical Image Segmentation},
author = {Jizong Peng and Ping Wang and Marco Pedersoli and Christian Desrosiers},
journal= {arXiv preprint arXiv:2202.02371},
year = {2022}
}