ContIG: Self-supervised Multimodal Contrastive Learning for Medical Imaging with Genetics

TL;DR

ContIG is a self-supervised contrastive learning method that effectively integrates medical images and genetic data, improving downstream task performance and revealing new biological insights.

Contribution

It introduces a novel multimodal contrastive learning framework that handles variable modalities and enhances interpretability in medical imaging with genetics.

Findings

Outperforms state-of-the-art self-supervised methods on benchmark tasks

Uncovers meaningful image-genetics associations through GWAS

Provides improved explainability of learned cross-modal features

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 each individual person in the same model end-to-end, even when the available modalities vary across individuals. Our procedure outperforms state-of-the-art self-supervised methods on all evaluated downstream benchmark tasks. We also adapt gradient-based explainability algorithms to better understand the learned cross-modal associations between the images and genetic modalities. Finally, we perform genome-wide association studies on the features learned by our models, uncovering interesting relationships between images and genetic data.