Boundary-aware Information Maximization for Self-supervised Medical Image Segmentation

• URL: http://arxiv.org/abs/2202.02371v1
• Date: Fri, 4 Feb 2022 20:18:00 GMT
• Title: Boundary-aware Information Maximization for Self-supervised Medical Image Segmentation
• Authors: Jizong Peng, Ping Wang, Marco Pedersoli, Christian Desrosiers
• Abstract summary: We propose a novel unsupervised pre-training framework that avoids the drawback of contrastive learning. Experimental results on two benchmark medical segmentation datasets reveal our method's effectiveness when few annotated images are available.
• Score: 13.828282295918628
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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