Contrastive learning of global and local features for medical image segmentation with limited annotations

• URL: http://arxiv.org/abs/2006.10511v2
• Date: Fri, 30 Oct 2020 14:45:25 GMT
• Title: Contrastive learning of global and local features for medical image segmentation with limited annotations
• Authors: Krishna Chaitanya, Ertunc Erdil, Neerav Karani, Ender Konukoglu
• Abstract summary: A key requirement for the success of supervised deep learning is a large labeled dataset. We propose strategies for extending the contrastive learning framework for segmentation of medical images in the semi-supervised setting. In the limited annotation setting, the proposed method yields substantial improvements compared to other self-supervision and semi-supervised learning techniques.
• Score: 10.238403787504756
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A key requirement for the success of supervised deep learning is a large labeled dataset - a condition that is difficult to meet in medical image analysis. Self-supervised learning (SSL) can help in this regard by providing a strategy to pre-train a neural network with unlabeled data, followed by fine-tuning for a downstream task with limited annotations. Contrastive learning, a particular variant of SSL, is a powerful technique for learning image-level representations. In this work, we propose strategies for extending the contrastive learning framework for segmentation of volumetric medical images in the semi-supervised setting with limited annotations, by leveraging domain-specific and problem-specific cues. Specifically, we propose (1) novel contrasting strategies that leverage structural similarity across volumetric medical images (domain-specific cue) and (2) a local version of the contrastive loss to learn distinctive representations of local regions that are useful for per-pixel segmentation (problem-specific cue). We carry out an extensive evaluation on three Magnetic Resonance Imaging (MRI) datasets. In the limited annotation setting, the proposed method yields substantial improvements compared to other self-supervision and semi-supervised learning techniques. When combined with a simple data augmentation technique, the proposed method reaches within 8% of benchmark performance using only two labeled MRI volumes for training, corresponding to only 4% (for ACDC) of the training data used to train the benchmark. The code is made public at https://github.com/krishnabits001/domain_specific_cl.

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