Reference-guided Pseudo-Label Generation for Medical Semantic Segmentation

• URL: http://arxiv.org/abs/2112.00735v1
• Date: Wed, 1 Dec 2021 12:21:24 GMT
• Title: Reference-guided Pseudo-Label Generation for Medical Semantic Segmentation
• Authors: Constantin Seibold, Simon Rei{\ss}, Jens Kleesiek, Rainer Stiefelhagen
• Abstract summary: We propose a novel approach to generate supervision for semi-supervised semantic segmentation. We use a small number of labeled images as reference material and match pixels in an unlabeled image to the semantics of the best fitting pixel in a reference set. We achieve the same performance as a standard fully supervised model on X-ray anatomy segmentation, albeit 95% fewer labeled images.
• Score: 25.76014072179711
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Producing densely annotated data is a difficult and tedious task for medical imaging applications. To address this problem, we propose a novel approach to generate supervision for semi-supervised semantic segmentation. We argue that visually similar regions between labeled and unlabeled images likely contain the same semantics and therefore should share their label. Following this thought, we use a small number of labeled images as reference material and match pixels in an unlabeled image to the semantics of the best fitting pixel in a reference set. This way, we avoid pitfalls such as confirmation bias, common in purely prediction-based pseudo-labeling. Since our method does not require any architectural changes or accompanying networks, one can easily insert it into existing frameworks. We achieve the same performance as a standard fully supervised model on X-ray anatomy segmentation, albeit 95% fewer labeled images. Aside from an in-depth analysis of different aspects of our proposed method, we further demonstrate the effectiveness of our reference-guided learning paradigm by comparing our approach against existing methods for retinal fluid segmentation with competitive performance as we improve upon recent work by up to 15% mean IoU.

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