Published on Wed Apr 10 2019

Curriculum semi-supervised segmentation

Hoel Kervadec, Jose Dolz, Eric Granger, Ismail Ben Ayed

This study investigates a curriculum-style strategy for semi-supervised CNN segmentation. It devises a regression network to learn image-level information such as the size of a target region. The strategy leverages unlabeled data in more efficiently and achieves very competitive results.

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Abstract

This study investigates a curriculum-style strategy for semi-supervised CNN segmentation, which devises a regression network to learn image-level information such as the size of a target region. These regressions are used to effectively regularize the segmentation network, constraining softmax predictions of the unlabeled images to match the inferred label distributions. Our framework is based on inequality constraints that tolerate uncertainties with inferred knowledge, e.g., regressed region size, and can be employed for a large variety of region attributes. We evaluated our proposed strategy for left ventricle segmentation in magnetic resonance images (MRI), and compared it to standard proposal-based semi-supervision strategies. Our strategy leverages unlabeled data in more efficiently, and achieves very competitive results, approaching the performance of full-supervision.