2021
DOI: 10.48550/arxiv.2111.13424
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ContIG: Self-supervised Multimodal Contrastive Learning for Medical Imaging with Genetics

Abstract: High annotation costs are a substantial bottleneck in applying modern deep learning architectures to clinically relevant medical use cases, substantiating the need for novel algorithms to learn from unlabeled data. In this work, we propose ContIG, a self-supervised method that can learn from large datasets of unlabeled medical images and genetic data. Our approach aligns images and several genetic modalities in the feature space using a contrastive loss. We design our method to integrate multiple modalities of… Show more

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References 79 publications
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